computational biology phd requirements

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Center for Computational Biology

Computational Biology PhD

The main objective of the Computational Biology PhD is to train the next generation of scientists who are both passionate about exploring the interface of computation and biology, and committed to functioning at a high level in both computational and biological fields.

The program emphasizes multidisciplinary competency, interdisciplinary collaboration, and transdisciplinary research, and offers an integrated and customizable curriculum that consists of two semesters of didactic course work tailored to each student’s background and interests, research rotations with faculty mentors spanning computational biology’s core disciplines, and dissertation research jointly supervised by computational and biological faculty mentors.

The Computational Biology Graduate Group facilitates student immersion into UC Berkeley’s vibrant computational biology research community. Currently, the Group includes over 46 faculty from across 14 departments of the College of Letters and Science, the College of Engineering, the College of Natural Resources, and the School of Public Health. Many of these faculty are available as potential dissertation research advisors for Computational Biology PhD students, with more available for participation on doctoral committees.

The First Year

The time to degree (normative time) of the Computational Biology PhD is five years. The first year of the program emphasizes gaining competency in computational biology, the biological sciences, and the computational sciences (broadly construed). Since student backgrounds will vary widely, each student will work with faculty and student advisory committees to develop a program of study tailored to their background and interests. Specifically, all first-year students must:

• Perform three rotations with Core faculty (one rotation with a non-Core faculty is acceptable with advance approval)
• Complete course work requirements (see below)
• Complete a course in the Responsible Conduct of Research
• Attend the computational biology seminar series
• Complete experimental training (see below)

Laboratory Rotations

Entering students are required to complete three laboratory rotations during their first year in the program to seek out a Dissertation Advisor under whose supervision dissertation research will be conducted. Students should rotate with at least one computational Core faculty member and one experimental Core faculty member. Click here to view rotation policy. 

Course Work & Additional Requirements

Students must complete the following coursework in the first three (up to four) semesters. Courses must be taken for a grade and a grade of B or higher is required for a course to count towards degree progress:

• Fall and Spring semester of CMPBIO 293, Doctoral Seminar in Computational Biology
• A Responsible Conduct of Research course, most likely through the Department of Molecular and Cell Biology.
• STAT 201A & STAT 201B : Intro to Probability and Statistics at an Advanced Level. Note: Students who are offered admission and are not prepared to complete STAT 201A and 201B will be required to complete STAT 134 or PH 142 first.
• CS61A : The Structure and Interpretation of Computer Programs. Note: students with the equivalent background can replace this requirement with a more advanced CS course of their choosing.
• 3 elective courses relevant to the field of Computational Biology , one of which must be at the graduate level (see below for details).
• Attend the computational biology invited speaker seminar series. A schedule is circulated to all students by email and is available on the Center website. Starting with the 2023 entering class, CCB PhD students must enroll in CMPBIO 275: Computational Biology Seminar , which provides credit for this seminar series.
• 1) completion of a laboratory course at Berkeley with a minimum grade of B,
• 2) completion of a rotation in an experimental lab (w/ an experimental project), with a positive evaluation from the PI,
• a biological sciences undergraduate major with at least two upper division laboratory-based courses,
• a semester or equivalent of supervised undergraduate experimental laboratory-based research at a university,
• or previous paid or volunteer/internship work in an industry-based experimental laboratory.

Students are expected to develop a course plan for their program requirements and to consult with the Head Graduate Advisor before the Spring semester of their first year for formal approval (signature required). The course plan will take into account the student’s undergraduate training areas and goals for PhD research areas.

Satisfactory completion of first year requirements will be evaluated at the end of the spring semester of the first year. If requirements are satisfied, students will formally choose a Dissertation advisor from among the core faculty with whom they rotated and begin dissertation research.

Waivers: Students may request waivers for the specific courses STAT 201A, STAT 201B, and CS61A. In all cases of waivers, the student must take alternative courses in related areas so as to have six additional courses, as described above. For waiving out of STAT 201A/B, students can demonstrate they have completed the equivalent by passing a proctored assessment exam on Campus. For waiving out CS61A, the Head Graduate Advisor will evaluate student’s previous coursework based on the previous course’s syllabus and other course materials to determine equivalency.

Electives: Of the three electives, students are required to choose one course in each of the two following cluster areas:

• Cluster A (Biological Science) : These courses are defined as those for which the learning goals are primarily related to biology. This includes courses covering topics in molecular biology, genetics, evolution, environmental science, experimental methods, and human health. This category may also cover courses whose focus is on learning how to use bioinformatic tools to understand experimental data.
• Cluster B (Computational Sciences): These courses are defined as those for which the learning goals involve computing, inference, or mathematical modeling, broadly defined. This includes courses on algorithms, computing languages or structures, mathematical or probabilistic concepts, and statistics. This category would include courses whose focus is on biological applications of such topics.

In the below link we give some relevant such courses, but students can take courses beyond this list; for courses not on this list, the Head Graduate Advisor will determine to which cluster a course can be credited. For classes that have significant overlap between these two clusters, the department which offers the course may influence the decision of the HGA as to whether the course should be assigned to cluster A or B.

See below for some suggested courses in these categories:

Suggested Coursework Options

Second Year & Beyond

At the beginning of the fall of the second year, students begin full-time dissertation research in earnest under the supervision of their Dissertation advisor. It is anticipated that it will take students three (up to four) semesters to complete the 6 course requirement. Students are required to continue to participate annually in the computational biology seminar series.

Qualifying Examination

Students are expected to take and pass an oral Qualifying Examination (QE) by the end of the spring semester (June 15th) of their second year of graduate study. Students must present a written dissertation proposal to the QE committee no fewer than four weeks prior to the oral QE. The write-up should follow the format of an NIH-style grant proposal (i.e., it should include an abstract, background and significance, specific aims to be addressed (~3), and a research plan for addressing the aims) and must thoroughly discuss plans for research to be conducted in the dissertation lab. Click here for more details on the guidelines and format for the QE. Click here to view the rules for the composition of the committee and the form for declaring your committee.

Advancement to Candidacy

After successfully completing the QE, students will Advance to Candidacy. At this time, students select the members of their dissertation committee and submit this committee for approval to the Graduate Division. Students should endeavor to include a member whose research represents a complementary yet distinct area from that of the dissertation advisor (ie, biological vs computational, experimental vs theoretical) and that will be integrated in the student’s dissertation research. Click here to view the rules for the composition of the committee and the form for declaring your committee.

Meetings with the Dissertation Committee

After Advancing to Candidacy, students are expected to meet with their Dissertation Committee at least once each year.

Teaching Requirements

Computational Biology PhD students are required to teach at least two semesters (starting with Fall 2019 class), but may teach more. The requirement can be modified if the student has funding that does not allow teaching. Starting with the Fall 2019 class: At least one of those courses should require that you teach a section. Berkeley Connect or CMPBIO 293 can count towards one of the required semesters.

The Dissertation

Dissertation projects will represent scholarly, independent and novel research that contributes new knowledge to Computational Biology by integrating knowledge and methodologies from both the biological and computational sciences. Students must submit their dissertation by the May Graduate Division filing deadline (see Graduate Division for date) of their fifth–and final–year.

Special Requirements

Students will be required to present their research either orally or via a poster at the annual retreat beginning in their second year.

• Financial Support

The Computational Biology Graduate Group provides a competitive stipend as well as full payment of fees and non-resident tuition (which includes health care). Students maintaining satisfactory academic progress are provided full funding for five to five and a half years. The program supports students in the first year, while the PI/mentor provides support from the second year on. A portion of this support is in the form of salary from teaching assistance as a Graduate Student Instructor (GSI) in allied departments, such as Molecular and Cell Biology, Integrative Biology, Plant and Microbial Biology, Mathematics, Statistics or Computer Science. Teaching is part of the training of the program and most students will not teach more than two semesters, unless by choice.

Due to cost constraints, the program admits few international students; the average is two per year. Those admitted are also given full financial support (as noted above): stipend, fees and tuition.

Students are also strongly encouraged to apply for extramural fellowships for the proposal writing experience. There are a number of extramural fellowships that Berkeley students apply for that current applicants may find appealing. Please note that the NSF now only allows two submissions – once as an undergrad and once in grad school. The NSF funds students with potential, as opposed to specific research projects, so do not be concerned that you don’t know your grad school plans yet – just put together a good proposal! Although we make admissions offers before the fellowships results are released, all eligible students should take advantage of both opportunities to apply, as it’s a great opportunity and a great addition to a CV.

• National Science Foundation Graduate Research Fellowship (app deadlines in Oct)
• Hertz Foundation Fellowship (app deadline Oct)
• National Defense Science and Engineering Graduate Fellowship (app deadline in mid-Fall)
• DOE Computational Science Graduate Fellowship (Krell Institute) (app deadline in Jan)

CCB no longer requires the GRE for admission (neither general, nor subject). The GRE will not be seen by the review committee, even if sent to Berkeley.

PLEASE NOTE: The application deadline is Monday, December 2 , 2024, 8:59 PST/11:59 EST

We invite applications from students with distinguished academic records, strong foundations in the basic biological, physical and computational sciences, as well as significant computer programming and research experience. Admission for the Computational Biology PhD is for the fall semester only, and Computational Biology does not offer a Master’s degree.

We are happy to answer any questions you may have, but please be sure to read this entire page first, as many of your questions will be answered below or on the Tips tab.

IMPORTANT : Please note that it is not possible to select a specific PhD advisor until the end of the first year in the program, so contacting individual faculty about openings in their laboratories will not increase your chances of being accepted into the program. You will have an opportunity to discuss your interests with relevant faculty if you are invited to interview in February.

Undergraduate Preparation

Minimum requirements for admission to graduate study:

• A bachelor’s degree or recognized equivalent from an accredited institution.
• Minimum GPA of 3.0.
• Undergraduate preparation reflecting a balance of training in computational biology’s core disciplines (biology, computer science, statistics/mathematics), for example, a single interdisciplinary major, such as computational biology or bioinformatics; a major in a core discipline and a combination of interdisciplinary course work and research experiences; or a double major in core disciplines.
• Basic research experience and aptitude are key considerations for admission, so evidence of research experience and letters of recommendation from faculty mentors attesting to the applicant’s research experience are of particular interest.
• GRE – NOT required or used for review .
• TOEFL scores for international students (see below for details).

Application Requirements

ALL materials, including letters, are due December 2, 2024 (8:59 PST). More information is provided and required as part of the online application, so please create an account and review the application before emailing with questions (and please set up an account well before the deadline):

• A completed graduate application: The online application opens in early or mid-September and is located on the Graduate Division website . Paper applications are not accepted. Please create your account and review the application well ahead of the submit date , as it will take time to complete and requests information not listed here.
• A nonrefundable application fee: The fee must be paid using a major credit card and is not refundable. For US citizens and permanent residents, the fee is $135; US citizens and permanent residents may request a fee waiver as part of the online application. For all other students (international) the fee is $155 (no waivers, no exceptions). Graduate Admissions manages the fee, not the program, so please contact them with questions.
• Three letters of recommendation, minimum (up to five are accepted): Letters of recommendation must be submitted online as part of the Graduate Division’s application process. Letters are also due Dec. 2, so please inform your recommenders of this deadline and give them sufficient advance notice. It is your responsibility to monitor the status of your letters of recommendation (sending prompts, as necessary) in the online system.
• Transcripts: Unofficial copies of all relevant transcripts, uploaded as part of the online application (see application for details). Scanned copies of official transcripts are strongly preferred, as transcripts must include applicant and institution name and degree goal and should be easy for the reviewers to read (print-outs from online personal schedules can be hard to read and transcripts without your name and the institution name cannot be used for review). Do not send via mail official transcripts to Grad Division or Computational Biology, they will be discarded.
• Essays: Follow links to view descriptions of what these essays should include ( Statement of Purpose [2-3 pages], Personal Statement [1-2 pages]). Also review Tips tab for formatting advice.
• (Highly recommended) Applicants should consider applying for extramural funding, such as NSF Fellowships. These are amazing opportunities and the application processes are great preparation for graduate studies. Please see Financial Support tab.
• Read and follow all of the “Application Tips” listed on the last tab. This ensures that everything goes smoothly and you make a good impression on the faculty reviewing your file.

The GRE general test is not required. GRE subject tests are not required. GRE scores will not be a determining factor for application review and admission, and will NOT be seen by the CCB admissions committee. While we do not encourage anyone to take the exam, in case you decide to apply to a different program at Berkeley that does require them: the UC Berkeley school code is 4833; department codes are unnecessary. As long as the scores are sent to UC Berkeley, they will be received by any program you apply to on campus.

TOEFL/IELTS

Adequate proficiency in English must be demonstrated by those applicants applying from countries where English is not the official language. There are two standardized tests you may take: the Test of English as a Foreign Language (TOEFL), and the International English Language Testing System (IELTS). TOEFL minimum passing scores are 90 for the  Internet-based test (IBT) , and 570 for the paper-based format (PBT) . The TOEFL may be waived if an international student has completed at least one year of full-time academic course work with grades of B or better while in residence at a U.S. university (transcript will be required). Please click here for more information .

Application Deadlines

The Application Deadline is 8:59 pm Pacific Standard Time, December 2, 2024 . The application will lock at 9pm PST, precisely. All materials must be received by the deadline. While rec letters can continue to be submitted and received after the deadline, the committee meets in early December and will review incomplete applications. TOEFL tests should be taken by or before the deadline, but self-reported scores are acceptable for review while the official scores are being processed. All submitted applications will be reviewed, even if materials are missing, but it may impact the evaluation of the application.

It is your responsibility to ensure and verify that your application materials are submitted in a timely manner. Please be sure to hit the submit button when you have completed the application and to monitor the status of your letters of recommendation (sending prompts, as necessary). Please include the statement of purpose and personal statement in the online application. While you can upload a CV, please DO NOT upload entire publications or papers. Please DO NOT send paper résumés, separate folders of information, or articles via mail. They will be discarded unread.

The Computational Biology Interview Visit dates are yet to be determined, but will be posted here once they are.

Top applicants who are being considered for admission will be invited to visit campus for interviews with faculty. Invitations will be made by early January. Students are expected to stay for the entire event, arriving in Berkeley by 5:30pm on the first day and leaving the evening of the final day. In the application, you must provide the names of between 7-10 faculty from the Computational Biology website with whom you are interested in conducting research or performing rotations. This helps route your application to our reviewers and facilitates the interview scheduling process. An invitation is not a guarantee of admission.

International students may be interviewed virtually, as flights are often prohibitively expensive.

Tips for the Application Process

Uploaded Documents: Be sure to put your name and type of essay on your essays ( Statement of Purpose [2-3 pages], Personal Statement [1-2 pages]) as a header or before the text, whether you use the text box or upload a PDF or Word doc. There is no minimum length on either essay, but 3 pages maximum is suggested. The Statement of Purpose should describe your research and educational background and aspirations. The Personal Statement can include personal achievements not necessarily related to research, barriers you’ve had to overcome, mentoring and volunteering activities, things that make you unique and demonstrate the qualities you will bring to the program.

Letters of Recommendation: should be from persons who have supervised your research or academic work and who can evaluate your intellectual ability, creativity, leadership potential and promise for productive scholarship. If lab supervision was provided by a postdoc or graduate student, the letter should carry the signature or support of the faculty member in charge of the research project. Note: the application can be submitted before all of the recommenders have completed their letters. It is your responsibility to keep track of your recommender’s progress through the online system. Be sure to send reminders if your recommenders do not submit their letters.

Extramural fellowships: it is to your benefit to apply for fellowships as they may facilitate entry into the lab of your choice, are a great addition to your CV and often provide higher stipends. Do not allow concerns about coming up with a research proposal before joining a lab prevent you from applying. The fellowships are looking for research potential and proposal writing skills and will not hold you to specific research projects once you have started graduate school.

Calculating GPA: Schools can differ in how they assign grades and calculate grade point averages, so it may be difficult for this office to offer advice. The best resource for calculating the GPA for your school is to check the back of the official transcripts where a guide is often provided or use an online tool. There are free online GPA conversion tools that can be found via an internet search.

Faculty Contact/Interests: Please be sure to list faculty that interest you as part of the online application. You are not required to contact any faculty in advance, nor will it assist with admission, but are welcome to if you wish to learn more about their research.

Submitting the application: To avoid the possibility of computer problems on either side, it is NOT advisable to wait until the last day to start and/or submit your application. It is not unusual for the application system to have difficulties during times of heavy traffic. However, there is no need to submit the application too early. No application will be reviewed before the deadline.

Visits: We only arrange one campus visit for recruitment purposes. If you are interested in visiting the campus and meeting with faculty before the application deadline, you are welcome to do so on your own time (we will be unable to assist).

Name: Please double check that you have entered your first and last names in the correct fields. This is our first impression of you as a candidate, so you do want to get your name correct! Be sure to put your name on any documents that you upload (Statement of Purpose, Personal Statement).

California Residency: You are not considered a resident if you hope to enter our program in the Fall, but have never lived in California before or are here on a visa. So, please do not mark “resident” on the application in anticipation of admission. You must have lived in California previously, and be a US citizen or Permanent Resident, to be a resident.

Faculty Leadership Head Graduate Advisor and Chair for the PhD & DE John Huelsenbeck ( [email protected] )

Associate Head Graduate Advisor for PhD & DE Liana Lareau ( [email protected] )

Equity Advisor Rasmus Nielsen ( [email protected] )

Director of CCB Elizabeth Purdom ( [email protected] )

Core PhD & DE Faculty ( link )

Staff support Student Services Advisor (GSAO): Kate Chase ( [email protected] )

Graduate Programs

Computational biology.

The Center for Computational Molecular Biology (CCMB) offers Ph.D. degrees in Computational Biology to train the next generation of scientists to perform cutting edge research in the multidisciplinary field of Computational Biology.

During the course of their Ph.D. studies students will develop and apply novel computational, mathematical , and statistical techniques to problems in the life sciences. Students in this program must achieve mastery in three areas - computational science, molecular biology, and probability and statistical inference - through a common core of studies that spans and integrates these areas.

The Ph.D. program in Computational Biology draws on course offerings from the disciplines of the Center’s Core faculty members. These areas are Applied Mathematics, Computer Science, the Division of Biology and Medicine, the Center for Biomedical Informatics, and the School of Public Health. Our faculty and Director of Graduate Studies work with each student to develop the best plan of coursework and research rotations to meet the student’s goals in their research focus and satisfy the University’s requirements for graduation.

Applicants should state a preference for at least one of these areas in their personal statement or elsewhere in their application. In addition, students interested in the intersection of Applied Mathematics and Computational Biology are encouraged to apply directly to the  Applied Mathematics Ph.D. program , and also to contact relevant  CCMB faculty members .

Our Ph.D. program assumes the following prerequisites: mathematics through intermediate calculus, linear algebra and discrete mathematics, demonstrated programming skill, and at least one undergraduate course in chemistry and in molecular biology. Exceptional strengths in one area may compensate for limited background in other areas, but some proficiency across the disciplines must be evident for admission.

Additional Resources

CCMB computing resources include a set of multiprocessor computer clusters and data storage servers with 392 processors. The CCMB Cluster is the largest dedicated computing system on campus for computational biology and bioinformatics applications. See also answers to  frequently asked questions .

Application Information

Application requirements, gre subject:.

Not required

GRE General:

Personal statement:.

Applicants will be asked a series of short form questions regarding their interest in computational biology, their research experiences, and their goals for the future. 1) Describe the life experiences that inspired you to pursue a career in science. 2) Describe at least one research experience you have had that prepared you intellectually/ scientifically for a career in computational biology. 3) Explain at least one challenge you have overcome in life or research to pursue a scientific career and what you have learned from this experience. 4) Why would you like to pursue your PhD in the Brown CCMB program? (Include at least two faculty members who you would like to work with at Brown and why.) 5) Discuss how you aspire to contribute to our mission to promote diversity and inclusion through your research, teaching, or service.

Dates/Deadlines

Application deadline, completion requirements.

Six graduate–level courses, two eight–week laboratory rotations, preliminary research presentation, dissertation, oral defense

Contact and Location

Center for computational molecular biology, location address, mailing address.

• Program Faculty
• Program Handbook
• Graduate School Handbook

Ph.D. in Computational Biology and Bioinformatics

General info.

• Faculty working with students: 60
• Students: 29
• Part time study available: No
• Application Terms: Fall
• Application Deadline: November 30

Monica Franklin Program Coordinator CBB Graduate Program Duke University Box 90090 Durham, NC 27708

Phone: 919-668-1049

Email: [email protected]

Website:  https://medschool.duke.edu/education/biomedical-phd-programs/computational-biology-and-bioinformatics-program

Program Description

The mission of the Graduate Program in Computational Biology and Bioinformatics (CBB) is to train predoctoral students to become leaders at the interdisciplinary intersection of quantitative and biomedical sciences. The program provides rigorous training in quantitative approaches from computer science, statistics, mathematics, physics, and engineering that enable its students to successfully address contemporary challenges across biology and medicine.  CBB trains students who have an interest and aptitude in both the computational and biological sciences. During their time in the program, students develop expertise in one or more quantitative areas, as well as in the specific biological area on which their research focuses.

Certificate in CBB

For students enrolled in other Ph.D. or masters programs of participating departments, the program also offers the opportunity to pursue a certificate in CBB. Students qualify for a CBB certificate by successfully completing two core courses plus an additional CBB course. Registration for the Computational Biology seminar every semester except the semester of graduation is also required.

• Computational Biology and Bioinformatics: PhD Admissions and Enrollment Statistics
• Computational Biology and Bioinformatics: PhD Completion Rate Statistics
• Computational Biology and Bioinformatics: PhD Time to Degree Statistics
• Computational Biology and Bioinformatics: PhD Career Outcomes Statistics

Application Information

Application Terms Available:  Fall

Application Deadline:  November 30

Graduate School Application Requirements See the Application Instructions page for important details about each Graduate School requirement.

• Transcripts: Unofficial transcripts required with application submission; official transcripts required upon admission
• Letters of Recommendation: 3 Required
• Statement of Purpose: Required
• Résumé: Required
• GRE Scores: GRE General (Optional)
• English Language Exam: TOEFL, IELTS, or Duolingo English Test required* for applicants whose first language is not English *test waiver may apply for some applicants
• GPA: Undergraduate GPA calculated on 4.0 scale required

Department-Specific Application Requirements (submitted through online application)

Writing Sample None required

Additional Components Optional Video Essay: How would a Duke PhD training experience help you achieve your academic and professional goals? Max video length 2 minutes; record externally and provide URL in application.

We strongly encourage you to review additional department-specific application guidance from the program to which you are applying: Departmental Application Guidance

List of Graduate School Programs and Degrees

Computational Biology

About the Program

Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.

Visit Group Website

Admission to the University

Applying for graduate admission.

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

Applicants for the Computational Biology PhD are expected to have a strong foundation in relevant stem fields, achieved by coursework in at least two computational biology subfields (including, but not limited to, advanced topics in biology, computer science, mathematics, statistics). Typical students admitted to the program have demonstrated outstanding potential as a research scientist and have clear academic aptitude in multiple disciplines, as well as excellent communication skills. This is assessed based on research experience, coursework & grades, essays ( statement of purpose & personal history ), and letters of recommendation. Three letters of recommendation are required, but up to five can be submitted.

The GRE is no longer accepted or used as part of the review (this includes both the general and subject exams). The program does *not* offer a Masters degree in Computational Biology.

Doctoral Degree Requirements

Normative time requirements, normative time to advancement: two years.

Please refer to the PhD page on the CCB website for the most up-to-date requirements and information.

Year 1 Students perform three laboratory rotations with the chief aim of identifying a research area and thesis laboratory. They also take courses to advance their knowledge in their area of expertise or fill in gaps in foundational knowledge. With guidance from the program, students are expected to complete six total graded courses by the end of the second year (not including the Doc Sem, Seminar Series or Ethics courses). Please see the program's website for more detailed course and curriculum requirements.

Year 2 Students attend seminars, complete course requirements, and prepare a dissertation prospectus in preparation for their PhD oral qualifying examination. With the successful passing of the orals, students select their thesis committee and advance to candidacy for the PhD degree.

Normative Time in Candidacy: Three years

Years 3 to 5 Students undertake research for the PhD dissertation under a three or four-person committee in charge of their research and dissertation. Students conduct original laboratory research and then write the dissertation based on the results of this research. On completion of the research and approval of the dissertation by the committee, the students are awarded the doctorate.

Total Normative Time: 5-5.5 years

Time to advancement.

Lab Rotations

Students conduct three 10-week laboratory rotations in the first year. The thesis lab, where dissertation research will take place, is chosen at the end of the third rotation in late April/early May.

Qualifying Examination

The qualifying examination will evaluate a student’s depth of knowledge in his or her research area, breadth of knowledge in fundamentals of computational biology, ability to formulate a research plan, and critical thinking. The QE prospectus will include a description of the specific research problem that will serve as a framework for the QE committee members to probe the student’s foundational knowledge in the field and area of research. Proposals will be written in the manner of an NIH-style grant proposal. The prospectus must be completed and submitted to the chair no fewer than four weeks prior to the oral qualifying examination. Students are expected to pass the qualifying examination by the end of the fourth semester in the program.

Time in Candidacy

Advancement.

After passing the qualifying exam by the end of the second year, students have until the beginning of the fifth semester to select a thesis committee and submit the Advancement to Candidacy paperwork to the Graduate Division.

Dissertation

Primary dissertation research is conducted in years 3-5/5.5. Requirements for the dissertation are decided in consultation with the thesis advisor and thesis committee members. To this end, students are required to have yearly thesis committee meetings with the committee after advancing to candidacy.

Dissertation Presentation/Finishing Talk

There is no formal defense of the completed dissertation; however, students are expected to publicly present an Exit Talk about their dissertation research in their final year.

Required Professional Development

Presentations.

All computational biology students are expected to attend the annual retreat, and will regularly present research talks there. They are also encouraged to attend national and international conferences to present research.

Computational biology students are required to teach for one or two semesters (either one semester at 50% (20hrs/wk) or two semesters at 25% (10hrs/wk)) and may teach more. The requirement can be modified if the student has funding that does not allow teaching.

Designated Emphasis Requirements

Curriculum/coursework.

Please refer to the DE page on the CCB website for the most up-to-date requirements and information.

The DE curriculum consists of one semester of the Doctoral Seminar in computational biology (CMPBIO 293, offered Fall & Spring) taken before the qualifying exam, plus three courses, one each from the three broad areas listed below, which may be independent from or an integral part of a student’s Associated Program. The three courses should be taken in different departments, only one of which may be the student’s home program. These requirements must be fulfilled with coursework taken with a grade of B or better while the student is enrolled as a graduate student at UC Berkeley. S/U graded courses do not count . See below for recommended coursework.

Students do not need to complete all of the course requirements prior to the application or the qualifying exam. The Doctoral Seminar does not need to be taken in order, ie either Fall or Spring are ok, but should be prior to or in the same semester as the Qualifying Exam. The DE will be rescinded if coursework has not been completed upon graduation (students should report their progress each year to the DE advisor, especially if they wish to change one of the courses they listed for the requirement).

• Computer Science and Engineering: A single course at the level of CS61A or higher will fulfill this requirement. Students can also take CS 88 (as an alternative to CS61A), though depending on their background, Data 8 may be necessary to complete this course. Students with a more advanced background are recommended to take a higher level CS course to fulfill the requirement.
• Biostatistics, Mathematics and Statistics: A single course at the level of Stat 131A, 133, 134, or 135 or higher will fulfill this requirement. Students with a more advanced background are recommended to take one of either Stat 201A & 201B or a higher level course to fulfill the requirement. Statistics or probability courses from other departments may be able to fulfill this requirement with prior approval of the program.
• Biology: please select an appropriate biology course from the list linked below (not up-to-date), or choose a course from current course listings.
• Computational Biology: CMPBIO C293, Doctoral Seminar, offered Fall & Spring.

More information, including a link to pre-approved courses, can be found on the CCB website .

Qualifying Examination and Dissertation

The qualifying examination and dissertation committees must include at least one (more is fine) Core faculty members from the Computational Biology Graduate Group. The faculty member(s) may serve any role on the committee from Chair to ASR. The Qualifying Examination must include examination of knowledge within the area of Computational and Genomic Biology. The Comp Bio Doctoral Seminar must be completed before the QE, as it will be important preparation for the exam.

Seminars & Retreat

Students must attend the annual Computational Biology Retreat (generally held in November) as well as regular CCB Seminar Series , or equivalent, as designated by the Curriculum Committee. Students are also strongly encouraged to attend or volunteer with program events during Orientation, Recruitment, Symposia, etc. Available travel funds will be dependent upon participation.

CMPBIO 201 Classics in Computational Biology 3 Units

Terms offered: Fall 2015, Fall 2014, Fall 2013 Research project and approaches in computational biology. An introducton to the diverse ways biological problems are investigated computationally through critical evaluation of the classics and recent peer-reviewed literature. This is the core course required of all Computational Biology graduate students. Classics in Computational Biology: Read More [+]

Rules & Requirements

Prerequisites: Acceptance in the Computational Biology Phd program; consent of instructor

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of lecture and 2 hours of discussion per week

Additional Format: One hour of Lecture and Two hours of Discussion per week for 15 weeks.

Additional Details

Subject/Course Level: Computational Biology/Graduate

Grading: Letter grade.

Classics in Computational Biology: Read Less [-]

CMPBIO C210 Introduction to Quantitative Methods In Biology 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course provides a fast-paced introduction to a variety of quantitative methods used in biology and their mathematical underpinnings. While no topic will be covered in depth, the course will provide an overview of several different topics commonly encountered in modern biological research including differential equations and systems of differential equations, a review of basic concepts in linear algebra, an introduction to probability theory, Markov chains, maximum likelihood and Bayesian estimation, measures of statistical confidence, hypothesis testing and model choice, permutation and simulation, and several topics in statistics and machine learning including regression analyses, clustering, and principal component analyses. Introduction to Quantitative Methods In Biology: Read More [+]

Objectives & Outcomes

Student Learning Outcomes: Ability to calculate means and variances for a sample and relate it to expectations and variances of a random variable. Ability to calculate probabilities of discrete events using simple counting techniques, addition of probabilities of mutually exclusive events, multiplication of probabilities of independent events, the definition of conditional probability, the law of total probability, and Bayes’ formula, and familiarity with the use of such calculations to understand biological relationships. Ability to carry out various procedures for data visualization in R. Ability to classify states in discrete time Markov chains, and to calculate transition probabilities and stationary distributions for simple discrete time, finite state-space Markov chains, and an understanding of the modeling of evolutionary processes as Markov chains. Ability to define likelihood functions for simple examples based on standard random variables. Ability to implement simple statistical models in R and to use simple permutation procedures to quantify uncertainty. Ability to implement standard and logistic regression models with multiple covariates in R. Ability to manipulate matrices using multiplication and addition. Ability to model simple relationships between biological variables using differential equations. Ability to work in a Unix environment and manipulating files in Unix. An understanding of basic probability theory including some of the standard univariate random variables, such as the binomial, geometric, exponential, and normal distribution, and how these variables can be used to model biological systems. An understanding of powers of matrices and the inverse of a matrix. An understanding of sampling and sampling variance. An understanding of the principles used for point estimation, hypothesis testing, and the formation of confidence intervals and credible intervals. Familiarity with ANOVA and ability to implementation it in R. Familiarity with PCA, other methods of clustering, and their implementation in R. Familiarity with basic differential equations and their solutions. Familiarity with covariance, correlation, ordinary least squares, and interpretations of slopes and intercepts of a regression line. Familiarity with functional programming in R and/or Python and ability to define new functions. Familiarity with one or more methods used in machine learning/statistics such as hidden Markov models, CART, neural networks, and/or graphical models. Familiarity with python allowing students to understand simple python scripts. Familiarity with random effects models and ability to implement them in R. Familiarity with the assumptions of regression and methods for investigating the assumptions using R. Familiarity with the use of matrices to model transitions in a biological system with discrete categories.

Prerequisites: Introductory calculus and introductory undergraduate statistics recommended

Credit Restrictions: Students will receive no credit for INTEGBI C201 after completing INTEGBI 201. A deficient grade in INTEGBI C201 may be removed by taking INTEGBI 201, or INTEGBI 201.

Fall and/or spring: 15 weeks - 3 hours of lecture and 3 hours of laboratory per week

Additional Format: Three hours of lecture and three hours of laboratory per week.

Formerly known as: Integrative Biology 201

Also listed as: INTEGBI C201

Introduction to Quantitative Methods In Biology: Read Less [-]

CMPBIO C231 Introduction to Computational Molecular and Cell Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022, Fall 2021 This class teaches basic bioinformatics and computational biology, with an emphasis on alignment, phylogeny, and ontologies. Supporting foundational topics are also reviewed with an emphasis on bioinformatics topics, including basic molecular biology, probability theory, and information theory. Introduction to Computational Molecular and Cell Biology: Read More [+]

Prerequisites: BIO ENG 11 or BIOLOGY 1A (may be taken concurrently); and a programming course ( ENGIN 7 or COMPSCI 61A )

Credit Restrictions: Students will receive no credit for BIO ENG C231 after completing BIO ENG 231 . A deficient grade in BIO ENG C231 may be removed by taking BIO ENG 231 , or BIO ENG 231 .

Instructor: Holmes

Also listed as: BIO ENG C231

Introduction to Computational Molecular and Cell Biology: Read Less [-]

CMPBIO C249 Computational Functional Genomics 4 Units

Terms offered: Fall 2024, Fall 2023 This course provides a survey of the computational analysis of genomic data, introducing the material through lectures on biological concepts and computational methods, presentations of primary literature, and practical bioinformatics exercises. The emphasis is on measuring the output of the genome and its regulation. Topics include modern computational and statistical methods for analyzing data from genomics experiments: high-throughput RNA sequencing data , single-cell data, and other genome-scale measurements of biological processes. Students will perform original analyses with Python and command-line tools. Computational Functional Genomics: Read More [+]

Course Objectives: This course aims to equip students with practical proficiency in bioinformatics analysis of genomic data, as well as understanding of the biological, statistical, and computational underpinnings of this field.

Student Learning Outcomes: Students completing this course should have stronger programming skills, practical proficiency with essential bioinformatics methods that are applicable to genomics research, understanding of the statistics underlying these methods, and awareness of key aspects of genome function and challenges in the field of genomics.

Prerequisites: Math 54 or EECS 16A /B; CS 61A or another course in python; BioE 11 or Bio 1a; and BioE 131. Introductory statistics or data science is recommended

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Lareau

Also listed as: BIO ENG C249

Computational Functional Genomics: Read Less [-]

CMPBIO C256 Human Genome, Environment and Public Health 4 Units

Terms offered: Spring 2024, Spring 2023, Fall 2020 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to molecular and genetic epidemiology. The latest methods for genome-wide association studies and other approaches to identify genetic variants and environmental risk factors important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. Recent developments in genomics , epigenomics and other ‘omics’ will be included. Computer and wet laboratory work will provide hands-on experience. Human Genome, Environment and Public Health: Read More [+]

Prerequisites: Introductory level biology/genetics course, or consent of instructor. Introductory biostatistics and epidemiology courses strongly recommended

Credit Restrictions: Students will receive no credit for PB HLTH C256 after completing CMPBIO 156 . A deficient grade in PB HLTH C256 may be removed by taking CMPBIO 156 .

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week

Additional Format: Two hours of lecture and two hours of laboratory per week.

Instructors: Barcellos, Holland

Also listed as: PB HLTH C256

Human Genome, Environment and Public Health: Read Less [-]

CMPBIO C256A Human Genome, Environment and Human Health 3 Units

Terms offered: Spring 2017 This introductory course will cover basic principles of human/population genetics and molecular biology relevant to understanding how data from the human genome are being used to study disease and other health outcomes. The latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to disease and health will be presented. The application of biomarkers to define exposures and outcomes will be explored. The course will cover recent developments in genomics, epigenomics and other ‘omics’, including applications of the latest sequencing technology and characterization of the human microbiome. Human Genome, Environment and Human Health: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Also listed as: PB HLTH C256A

Human Genome, Environment and Human Health: Read Less [-]

CMPBIO C256B Genetic Analysis Method 3 Units

Terms offered: Prior to 2007 This introductory course will provide hands-on experience with modern wet laboratory techniques and computer analysis tools for studies in molecular and genetic epidemiology and other areas of genomics in human health. Students will also participate in critical review of journal articles. Students are expected to understand basic principles of human/population genetics and molecular biology, latest designs and methods for genome-wide association studies and other approaches to identify genetic variants, environmental risk factors and the combined effects of gene and environment important to human health. Students will learn how to perform DNA extraction, polymerase chain reaction and methods for genotyping, sequencing, and cytogenetics. Genetic Analysis Method: Read More [+]

Prerequisites: Introductory level biology course. Completion of introductory biostatistics and epidemiology courses strongly recommended and may be taken concurrently with permission. PH256A is a requirement for PH256B; they can be taken concurrently

Fall and/or spring: 15 weeks - 2-2 hours of lecture and 1-3 hours of laboratory per week

Additional Format: Two hours of lecture and one to three hours of laboratory per week.

Also listed as: PB HLTH C256B

Genetic Analysis Method: Read Less [-]

CMPBIO 275 Computational Biology Seminar/Journal Club 1 Unit

Terms offered: Fall 2024, Spring 2024, Fall 2023 This seminar course will cover a wide range of topics in the field of computational biology. The main goals of the course are to expose students to cutting edge research in the field and to prepare students for engaging in academic discourse with seminar speakers - who are often leaders in their fields. A selected number of class meetings will be devoted to the review of scientific papers published by upcoming seminar speakers and the other class meetings will be devoted to discussing other related articles in the field. The seminar will expose students to both the breadth and highest standards of current computational biology research. Computational Biology Seminar/Journal Club: Read More [+]

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of seminar per week

Additional Format: One hour of seminar per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Computational Biology Seminar/Journal Club: Read Less [-]

CMPBIO 276 Algorithms for Computational Biology 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide familiarity with algorithms and probabilistic models that arise in various computational biology applications, such as suffix trees, suffix arrays, pattern matching, repeat finding, sequence alignment, phylogenetics, hidden Markov models, gene finding, motif finding, linear/logistic regression, random forests, convolutional neural networks, genome-wide association studies, pathogenicity prediction, and sequence-to-epigenome prediction. Algorithms for Computational Biology: Read More [+]

Prerequisites: CompSci 70 AND CompSci 170, MATH 54 OR EECS 16A OR an equivalent linear algebra course

Repeat rules: Course may be repeated for credit with instructor consent.

Instructors: Song, Ioannidis

Algorithms for Computational Biology: Read Less [-]

CMPBIO 290 Special Topics - Computational Biology 1 - 4 Units

Terms offered: Fall 2022, Fall 2021, Spring 2018 This graduate-level course will cover various special topics in computational biology and the theme will vary from semester to semester. The course will focus on computational methodology, but also cover relevant biological applications. This course will be offered according to student demand and faculty availability. Special Topics - Computational Biology: Read More [+]

Prerequisites: Graduate standing in EECS, MCB, Computational Biology or related fields; or consent of the instructor

Fall and/or spring: 15 weeks - 1-3 hours of lecture per week

Additional Format: One to three hours of lecture per week for standard offering. In some instances, condensed special topics classes running from 2-10 weeks may also be offered usually to accommodate guest instructors. Total works hours will remain the same but more work in a given week will be required.

Special Topics - Computational Biology: Read Less [-]

CMPBIO 293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Fall and/or spring: 15 weeks - 2 hours of seminar per week

Additional Format: Two hours of seminar per week.

Doctoral Seminar in Computational Biology: Read Less [-]

CMPBIO C293 Doctoral Seminar in Computational Biology 2 Units

Terms offered: Spring 2024, Fall 2022, Fall 2021 This interactive seminar builds skills, knowledge and community in computational biology for first year PhD and second year Designated Emphasis students. Topics covered include concepts in human genetics/genomics, microbiome data analysis, laboratory methodologies and data sources for computational biology, workshops/instruction on use of various bioinformatics tools, critical review of current research studies and computational methods, preparation for success in the PhD program and career development. Faculty members of the graduate program in computational biology and scientists from other institutions will participate. Topics will vary each semester. Doctoral Seminar in Computational Biology: Read More [+]

Instructors: Moorjani, Rokhsar

Also listed as: MCELLBI C296

CMPBIO 294A Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

Prerequisites: Standing as a Computational Biology graduate student

Fall and/or spring: 15 weeks - 2-20 hours of laboratory per week

Additional Format: Two to Twenty hours of Laboratory per week for 15 weeks.

Introduction to Research in Computational Biology: Read Less [-]

CMPBIO 294B Introduction to Research in Computational Biology 2 - 12 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Closely supervised experimental or computational work under the direction of an individual faculty member; an introduction to methods and research approaches in particular areas of computational biology. Introduction to Research in Computational Biology: Read More [+]

CMPBIO 295 Individual Research for Doctoral Students 1 - 12 Units

Terms offered: Summer 2024 10 Week Session, Summer 2023 10 Week Session, Summer 2022 10 Week Session Laboratory research, conferences. Individual research under the supervision of a faculty member. Individual Research for Doctoral Students: Read More [+]

Prerequisites: Acceptance in the Computational Biology PhD program; consent of instructor

Fall and/or spring: 15 weeks - 1-20 hours of laboratory per week

Summer: 10 weeks - 1.5-30 hours of laboratory per week

Additional Format: One to twenty hours of laboratory per week. One and one-half to thirty hours of laboratory per week for 10 weeks.

Individual Research for Doctoral Students: Read Less [-]

CMPBIO 477 Introduction to Programming for Bioinformatics Bootcamp 1.5 Unit

Terms offered: Prior to 2007 The goals of this course are to introduce students to Python, a simple and powerful programming language that is used for many applications, and to expose them to the practical bioinformatic utility of Python and programming in general. The course will allow students to apply programming to the problems that they face in the lab and to leave this course with a sufficiently generalized knowledge of programming (and the confidence to read the manuals) that they will be able to apply their skills to whatever projects they happen to be working on. Introduction to Programming for Bioinformatics Bootcamp: Read More [+]

Prerequisites: This is a graduate course and upper level undergraduate students can only enroll with the consent of the instructor

Summer: 3 weeks - 40-40 hours of workshop per week

Additional Format: Organized as a bootcamp, the ten-day course will include two sessions daily, each consisting of roughly two hours of lecture and up to three hours of hands on exercises.

Subject/Course Level: Computational Biology/Other professional

Introduction to Programming for Bioinformatics Bootcamp: Read Less [-]

Contact Information

Computational biology graduate group.

574 Stanley Hall

Phone: 510-642-0379

Fax: 510-666-3399

[email protected]

Director, CCB

Elizabeth Purdom

[email protected]

Graduate Program Manager

574 Stanley Hall, MC #3220

[email protected]

Head Graduate Advisor for the PhD & DE

John Huelsenbeck

[email protected]

CCB DE Advising

CCB DE email

[email protected]

Executive Director, CCB

Phone: 510-666-3342

[email protected]

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Computational Biology

Our interdisciplinary M.S. in Computational Biology program is designed to provide students with expertise in the leading quantitative methods underlying modern biomedical science. The program is an in-depth response to the ever-growing need for computational methods and mathematical models in processing, analyzing, and interpreting the vast amounts of biological data generated by high-throughput techniques. Computer simulations are required to understand and predict the dynamics of complex biological systems. Precision medicine, drug development, and cancer research are only a few among the many thriving fields increasingly relying on quantitative genomics, bioinformatics, and systems biology.

The M.S. in Computational Biology (MS-CB) presents a unique, rigorous training program, equipping students with theoretical understanding and practical mastery of state-of-the-art applications of computational approaches in biology and medicine. Our faculty from Weill Cornell Medicine, Sloan-Kettering Institute, and Cornell Tech are world-class leaders in computational biology research and applications.

Upon graduation, with extensive training and field-specific, curricular workshops in career development, students will be prepared to launch successful professional careers at the forefront of data analytics, bioinformatics, and computer modeling, for example in the pharmaceutical or biotech industries. Likewise, for those interested in pursuing further education in computational biology at the PhD level, this degree will attest to their preparation and enhance their competitiveness.

Our  curriculum  is highly interdisciplinary and includes training in cutting-edge bioinformatics, statistics, machine learning, computation and simulation, quantitative biology, and genomics. The training emphasizes hands-on computer labs and practical skills to prepare students for careers beyond the classroom.

Program features include:

• 18 months duration, full-time study
• cohesive interdisciplinary educational program
• individual mentored research project
• career development training

Please  see here  for a complete list of faculty

Tuition, Fees and Scholarships

Please refer to the  student services website  for program-specific details on tuition and fees. Please note that this tuition cost and fees are set for the current academic year and are subject to change.

A small number of partial scholarships are available. Applicants applying by the priority deadline are automatically considered for these merit-based scholarships.

Program Requirements

Applicants must hold a bachelor’s degree in science or engineering. Applicants must have completed undergraduate level coursework in calculus, linear algebra, probability theory or statistics, and computer programming.

We seek applications from students with diverse undergraduate degrees and welcome applications from talented individuals of all backgrounds. 

All application forms and supporting documents can be  submitted online . You will be asked to submit or upload:

• Personal Statement describing your background and specific interest in the MS-CB program.
• Résumé/C.V.
• Three letters of recommendation. Letters must be submitted electronically as instructed through the online application.
• Transcripts from all previously attended colleges and universities
• Domestic Transcripts - Unofficial transcripts from U.S. institutions may be submitted for application review. Official transcripts will be requested from accepted students prior to matriculation.
• If using WES, please select the WES Basic Course-by-Course evaluation and choose "Cornell University - Manhattan NY" as the recipient with "Weill Graduate School of Medical Sciences" as the School/Division 
• Evaluations are accepted only from  current members of the National Association of Credit Evaluation Services (NACES) . Official course-by-course evaluations are required for application review.
• $80 application fee
• Results of the General Graduate Record (GRE) examination are  optional . The Institution Code Number is 2119.
• Scores from the  Test of English as a Foreign Language (TOEFL) ,  International English Language Testing System (IELTS) , or  Duolingo English Test . Test scores are valid for two years after the test date. To see if you qualify for an exemption, see below.
• To submit your official TOEFL scores, please go to  http://www.ets.org/toefl  and request your scores to be sent to Weill Cornell Graduate School using code 2119. Please monitor your application to ensure that your scores are populated by ETS.  
• IELTS results must be submitted directly via e-delivery to “Weill Cornell Graduate School of Medical Sciences.”
• Results for the Duolingo English Test, applicants must submit their results directly through Duolingo to “Weill Cornell Graduate School of Medical Sciences".

Application Timeline & Deadlines

The application site for Fall 2024 admission is open. 

We are still accepting applications for the Fall 2024 class. We are operating with a rolling admissions process at this point and encourage you to submit your application as early as possible to avoid potential seat capacity or timing restrictions.  

Final deadline for applications: May 1, 2024. 

English Language Proficiency Exam Exemptions

The English language proficiency requirement may be waived if an applicant meets at least one of the following criteria:

• Citizenship/Permanent Residency
• If the applicant is a citizen or permanent resident of the United States or its territories (e.g., Puerto Rico), or a citizen of the United Kingdom, Ireland, Australia, New Zealand, or Canada, they are exempt.
• Applicants who are citizens of all other countries, including India, Pakistan, the Philippines, Hong Kong, Singapore, etc. are not exempt and must submit English language proficiency exam scores.
• English-Language Instruction
• Applicants who, at the time of enrollment, have studied in full-time status for at least two academic years within the last  five  years in the United States, the United Kingdom, Ireland, Australia, or New Zealand, or with English language instruction in Canada or South Africa, are exempt.
• Applicants must submit a transcript that shows they studied in one of the approved locations, and that the academic program was at least two years in length.
• Even if English was the language of instruction of the course or institution, it must have been in one of the eligible locations, otherwise the applicant is not exempt from the requirement.

Prospective Student Events

We're always working on putting events together. Be sure to check back soon for more event listings.

Faculty Stories

• Aguiar-Pulido, Vanessa
• Bao, Zhirong
• Bendall, Matthew
• Berger, Michael
• Betel, Doron
• Brady, Nicholas
• Christini, David
• Davis, Melissa
• Dundar, Friederike
• Elemento, Olivier
• Hajirasouliha, Iman
• Imielinski, Marcin
• Kentsis, Alex
• Khurana, Ekta
• Krogh-Madsen, Trine
• Krumsiek, Jan
• Landau, Dan
• Laughney, Ashley
• Lee, Guinevere
• Leslie, Christina
• Mason, Christopher
• Nixon, Douglas
• Papaemmanuil, Elli
• Sboner, Andrea
• Schultz, Nikolaus
• Skrabanek, Luce
• Tilgner, Hagen
• Ventura, Andrea

Courses and Required Curricular Components

• Analysis of Next-Generation Sequencing Data
• Career Development in Computational Biology
• Cellular and Molecular Biology
• Computational Biology Research
• Data Structures and Algorithms for Computational Biology
• Dynamic Models in Biology
• Functional Interpretation of High-Throughput Data
• MS-CB Thesis Research
• MS-CB Thesis Research Exploration 1&2
• Quantitative Genomics and Genetics

Student Stories

I have enjoyed exploring a bunch of different areas of research, specifically those to which I was completely new. Everyone here has been extremely supportive and there has been a lot of room for growth. Overall it's been really fun figuring out what I'd like to do moving forward!

Weill Cornell is unique in how focused it is on medical science research. There is no other institution where I can so easily find professors to collaborate with for clinical research. In the short time I have spent in the program, I have meaningfully contributed to several projects that will be published.

Student Handbook

To view the MSCB Student Handbook, click here .

  Contact Information

  Trine Krogh-Madsen, PhD, Director 413 E. 69th St, Box 190 New York, NY 10065 (646) 962 - 5392 [email protected]

Lucia Li , Program Coordinator 1300 York Ave, Box 65 New York, NY 10065 [email protected]

Weill Cornell Medicine Graduate School of Medical Sciences 1300 York Ave. Box 65 New York, NY 10065 Phone: (212) 746-6565 Fax: (212) 746-8906

Computational and Systems Biology PhD Program

Computational and systems biology.

The field of computational and systems biology represents a synthesis of ideas and approaches from the life sciences, physical sciences, computer science, and engineering. Recent advances in biology, including the human genome project and massively parallel approaches to probing biological samples, have created new opportunities to understand biological problems from a systems perspective. Systems modeling and design are well established in engineering disciplines but are newer in biology. Advances in computational and systems biology require multidisciplinary teams with skill in applying principles and tools from engineering and computer science to solve problems in biology and medicine. To provide education in this emerging field, the Computational and Systems Biology (CSB) program integrates MIT's world-renowned disciplines in biology, engineering, mathematics, and computer science. Graduates of the program are uniquely prepared to make novel discoveries, develop new methods, and establish new paradigms. They are also well-positioned to assume critical leadership roles in both academia and industry, where this field is becoming increasingly important.

Computational and systems biology, as practiced at MIT, is organized around "the 3 Ds" of description, distillation, and design. In many research programs, systematic data collection is used to create detailed molecular- or cellular-level descriptions of a system in one or more defined states. Given the complexity of biological systems and the number of interacting components and parameters, system modeling is often conducted with the aim of distilling the essential or most important subsystems, components, and parameters, and of obtaining simplified models that retain the ability to accurately predict system behavior under a wide range of conditions. Distillation of the system can increase the interpretability of the models in relation to evolutionary and engineering principles such as robustness, modularity, and evolvability. The resulting models may also serve to facilitate rational design of perturbations to test understanding of the system or to change system behavior (e.g., for therapeutic intervention), as well as efforts to design related systems or systems composed of similar biological components.

CSB Faculty and Research

More than 70 faculty members at the Institute participate in MIT's Computational and Systems Biology Initiative (CSBi). These investigators span nearly all departments in the School of Science and the School of Engineering, providing CSB students the opportunity to pursue thesis research in a wide variety of different laboratories. It is also possible for students to arrange collaborative thesis projects with joint supervision by faculty members with different areas of expertise. Areas of active research include computational biology and bioinformatics, gene and protein networks, regulatory genomics, molecular biophysics, instrumentation engineering, cell and tissue engineering, predictive toxicology and metabolic engineering, imaging and image informatics, nanobiology and microsystems, biological design and synthetic biology, neurosystems biology, and cancer biology.

The CSB PhD Program

The CSB PhD program is an Institute-wide program that has been jointly developed by the Departments of Biology, Biological Engineering, and Electrical Engineering and Computer Science. The program integrates biology, engineering, and computation to address complex problems in biological systems, and CSB PhD students have the opportunity to work with CSBi faculty from across the Institute. The curriculum has a strong emphasis on foundational material to encourage students to become creators of future tools and technologies, rather than merely practitioners of current approaches. Applicants must have an undergraduate degree in biology (or a related field), bioinformatics, chemistry, computer science, mathematics, statistics, physics, or an engineering discipline, with dual-emphasis degrees encouraged.

CSB Graduate Education

All students pursue a core curriculum that includes classes in biology and computational biology, along with a class in computational and systems biology based on the scientific literature. Advanced electives in science and engineering enhance both the breadth and depth of each student's education. During their first year, in addition to coursework, students carry out rotations in multiple research groups to gain a broader exposure to work at the frontier of this field, and to identify a suitable laboratory in which to conduct thesis research. CSB students also serve as teaching assistants during one semester in the second year to further develop their teaching and communication skills and facilitate their interactions across disciplines. Students also participate in training in the responsible conduct of research to prepare them for the complexities and demands of modern scientific research. The total length of the program, including classwork, qualifying examinations, thesis research, and preparation of the thesis is roughly five years.

The CSB curriculum has two components. The first is a core that provides foundational knowledge of both biology and computational biology. The second is a customized program of electives that is selected by each student in consultation with members of the CSB graduate committee. The goal is to allow students broad latitude in defining their individual area of interest, while at the same time providing oversight and guidance to ensure that training is rigorous and thorough.

Core Curriculum

The core curriculum consists of three classroom subjects plus a set of three research rotations in different research groups. The classroom subjects fall into three areas described below.

Modern Biology (One Subject): A term of modern biology at MIT strengthens the biology base of all students in the program. Subjects in biochemistry, genetics, cell biology, molecular biology, or neurobiology fulfill this requirement. The particular course taken by each student will depend on their background and will be determined in consultation with graduate committee members.

Computational Biology (One Subject): A term of computational biology provides students with a background in the application of computation to biology, including analysis and modeling of sequence, structural, and systems data. This requirement can be fulfilled by 7.91[J] / 20.490[J] Foundations of Computational and Systems Biology.

Topics in Computational and Systems Biology (One Subject): All first-year students in the program participate in / 7.89[J] Topics in Computational and Systems Biology, an exploration of problems and approaches in the field of computational and systems biology through in-depth discussion and critical analysis of selected primary research papers. This subject is restricted to first-year PhD students in CSB or related fields in order to build a strong community among the class. It is the only subject in the program with such a limitation.

Research Group Rotations (Three Rotations): To assist students with lab selection and provide a range of research activities in computational and systems biology, students participate in three research rotations of one to two months' duration during their first year. Students are encouraged to gain experience in experimental and computational approaches taken across different disciplines at MIT.

Advanced Electives

The requirement of four advanced electives is designed to develop both breadth and depth. The electives add to the base of the diversified core and contribute strength in areas related to student interest and research direction. To develop depth, two of the four advanced electives must be in the same research area or department. To develop breadth, at least one of the electives must be in engineering and at least one in science. Each student designs a program of advanced electives that satisfies the distribution and area requirements in close consultation with members of the graduate committee.

Additional Subjects: As is typical for students in other doctoral programs at MIT, CSB PhD students may take classes beyond the required diversified core and advanced electives described above. These additional subjects can be used to add breadth or depth to the proposed curriculum, and might be useful to explore advanced topics relevant to the student's thesis research in later years. The CSB Graduate Committee works with each graduate student to develop a path through the curriculum appropriate for his or her background and research interests.

Training in the Responsible Conduct of Research: Throughout the program, students will be expected to attend workshops and other activities that provide training in the ethical conduct of research. This is particularly important in interdisciplinary fields such as computational and systems biology, where different disciplines often have very different philosophies and conventions. By the end of the fourth year, students will have had about 16 hours of training in the responsible conduct of research.

Qualifying Exams: In addition to coursework and a research thesis, each student must pass a written and an oral qualifying examination at the end of the second year or the beginning of the third year. The written examination involves preparing a research proposal based on the student's thesis research, and presenting the proposal to the examination committee. This process provides a strong foundation for the thesis research, incorporating new research ideas and refinement of the scope of the research project. The oral examination is based on the coursework taken and on related published literature. The qualifying exams are designed to develop and demonstrate depth in a selected area (the area of the thesis research) as well as breadth of knowledge across the field of computational and systems biology.

Thesis Research: Research will be performed under the supervision of a CSBi faculty member, culminating in the submission of a written thesis and its oral defense before the community and thesis defense committee. By the second year, a student will have formed a thesis advisory committee that they will meet with on an annual basis.

Computational Biology PhD

Under the auspices of the Center for Computational Biology, the Computational Biology Graduate Group offers the PhD in Computational Biology as well as the Designated Emphasis in Computational and Genomic Biology, a specialization for doctoral students in associated programs. The PhD is concerned with advancing knowledge at the interface of the computational and biological sciences and is therefore intended for students who are passionate about being high functioning in both fields. The designated emphasis augments disciplinary training with a solid foundation in the different facets of genomic research and provides students with the skills needed to collaborate across disciplinary boundaries to solve a wide range of computational biology and genomic problems.

Contact Info

[email protected]

574 Stanley Hall, MC 3220

Berkeley, CA 94720

At a Glance

Department(s)

Computational Biology Graduate Group

Admit Term(s)

Application Deadline

December 2, 2024

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

/images/cornell/logo35pt_cornell_white.svg" alt="computational biology phd requirements"> Cornell University --> Graduate School

Computational biology ph.d. (ithaca), field of study.

Computational Biology

Program Description

Computation has become essential to biological research. Genomic databases, protein databanks, MRI images of the human brain, and remote sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of biology. The computational biologist must have skills in mathematics and computation as well as in biology. A key goal in training is to develop the ability to relate biological processes to computational models.   The field provides interdisciplinary training and research opportunities in a range of subareas of computational biology involving topics such as DNA and protein databases, protein structure and function, computational neuroscience, biomechanics, population genetics, and management of natural and agricultural systems. Students majoring in computational biology are expected to obtain a broad, interdisciplinary knowledge of fundamental principles in biology, computational science, and mathematics. But because the field covers a wide range of areas, it would be unrealistic to expect a student to master each facet in detail. Instead, students choose from specific subareas of study: they are expected to develop competence in at least one specific subdomain of biology (i.e., genetics, macromolecular biology, cellular biology, organismal biology, behavioral biology or ecology) and in relevant subareas of computational science and mathematics.  Students are supervised by field faculty drawn from sixteen departments.

Contact Information

102 Weill Hall Cornell University Ithaca, NY  14853

Concentrations by Subject

• computational behavioral biology
• computational biology
• computational cell biology
• computational ecology
• computational genetics
• computational macromolecular biology
• computational organismal biology

Visit the Graduate School's Tuition Rates page.

Application Requirements and Deadlines

Requirements Summary:

Please see the field's Ph.D. program page .

Learning Outcomes

Fundamentals: Demonstrated mastery of fundamental concepts, theory, and methodology in areas of biology, computer science, and mathematics relevant to the chosen specialty.

Breadth: Demonstrated broad knowledge of theory and research across several sub-disciplines in computational biology.

Originality: Demonstrated the ability to independently conduct, document, and defend original research having the potential to produce new biological insights and/or improved computational methods.

Communication: Demonstrated proficiency in oral and written presentation of results appropriate for a career in advanced research in government or industry, or advanced research and/or teaching at a college or university.

Literacy and Outreach: Demonstrated broad knowledge of the scientific literature relevant to the specialty area, including awareness of recent advances, active areas of research, and open questions. Students should also have demonstrated the ability to participate in the broader research community outside of Cornell, through meetings, conferences, individual collaborations, or other interactions.

Ethics: Demonstrated the ability to follow established ethical standards for the field, pertaining to topics such as (but not limited to) recognition of prior scholarship, acknowledgment of intellectual and material contributions to research, falsification of data, appropriate handling of human and animal subjects and of hazardous materials, and respectful and fair treatment of students and co-workers of diverse backgrounds.

Teaching: (For those entering a teaching profession) Demonstrated the ability to communicate complex idea and methods in terms students can understand, to grade and comment effectively on student work, to lead discussions effectively, and to plan an effective course in the field.

Career Progress: Demonstrated significant progress toward future career goals, or found employment, if desired.

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Yale Computational Biology and Biomedical Informatics

Requirements & curriculum overview, terminal m.s. requirements:.

A en route Master’s degree may also be obtained by a CBB Ph.D. student who is en route to obtaining a PhD degree or who leaves Yale prior to receiving a PhD degree. Such students must (1) complete two years (four terms) of study in the Ph.D. program (2) complete the required course work for the Ph.D. program with an average grade of High Pass or higher, with ten required course credits taken at Yale including three successful research rotations and (3) meet the Graduate School’s Honors requirement of at least two Honors grades to earn the Master’s degree.

Ph.D. Requirements:

This section outlines the current CBB curriculum, and other requirements for the Ph.D. degree. Because of the interdisciplinary nature of the field, we anticipate that the students will be extremely heterogeneous in their background and training. As a result, the co-directors are willing to meet with students to help them individually tailor the curriculum to their background and interests. The emphasis will be on gaining competency in three broad “core areas”:

• Computational biology and biomedical informatics
• Biological sciences (e.g., genetics, immunobiology, cell biology, etc.)
• Informatics ( e.g.,  computer science, data science, statistics, applied mathematics, etc.)

Completion of the curriculum will typically take 4 semesters, depending in part on the prior training of the student. Since students may have very different prior training in biology and computing, the courses taken may vary considerably. In addition, students will spend a significant amount of time during this period doing intensive research rotations in faculty laboratories and attending relevant lectures and seminars.

Specifically, we expect that all students will:

• Three required graduate courses in computational biology and biomedical informatics
• Two graduate courses in the biological sciences
• Two graduate courses in areas of informatics
• Two additional courses in any of the three core areas (which may be undergraduate courses with approval)
• One year-long graduate course that consists of three lab rotations taken over the fall and spring semesters of the first year (graded as Pass or Fail)
• Any additional courses required to satisfy areas of minimum expected competency
• Take a half-semester graduate seminar on research ethics in the 1st and 4th years (graded as credit or non-credit)
• Participate in the CBB/BIDS seminar series
• Serve as a teaching assistant in two semester courses

Students will typically take 2 courses each semester and 3 research rotations during the first year. Students are expected to find a dissertation adviser (or co-advisers) by the end of the first year. In the summer after the first year, students will start working in the laboratory of their chosen Ph.D. supervisor. Students must pass a qualifying examination normally given at the end of the second year or the beginning of the third year. There is no language requirement.

Students may be able to waive some course requirements based on graduate coursework completed at other universities where they have been enrolled as a graduate student. Courses must be equivalent to Yale graduate courses, and the Graduate School usually sets a maximum limit of three courses that can be waived.

Carnegie Mellon University School of Computer Science

Computational biology department.

Ph.D. in Computational Biology

The Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) provides interdisciplinary training in developing and using quantitative and computational approaches to tackle the key scientific questions of our times. By developing advanced computational methods and applying them to real-world data, our trainees advance scientific knowledge at the interface of the life, medical, engineering and computer sciences. CPCB trainees are taught and mentored by leading experts at two of the foremost computer science and biomedical research institutions in the world.

Visit the Website

• Back to Doctoral Programs

More Information

2024-25 Bulletin

Computational & systems biology, phd, computational & systems biology.

The graduate program in Computational and Systems Biology trains the next generation of scientists in technology-intensive, quantitative, systems-level approaches to molecular biology. As technological changes generate exponentially larger amounts of data, the scale of the biological questions under investigation grows ever larger. Students in the Computational and Systems Biology program learn to leverage advances in cutting-edge, high-throughput experimental and computational tools. Because of its interdisciplinary nature, the program’s curriculum accommodates students with a wide variety of backgrounds, including genetics, biochemistry, molecular biology, mathematics, engineering, physics, chemistry, computer science, and statistics. The faculty in the program are highly interdisciplinary and specialize in the application of computer science, information technology, biophysics, biochemistry, genetics, applied mathematics, and statistics to problems in molecular biology.

Doctoral Candidacy

To earn a PhD at Washington University, a student must complete all courses required by their department; maintain satisfactory academic progress; pass certain examinations; fulfill residence and Mentored Experience Requirements; write, defend, and submit a dissertation; and file an Intent to Graduate. For a general layout of doctoral degree general requirements in Arts & Sciences, including an explanation of Satisfactory Academic Progress, students should review the Doctoral Degree Academic Information page of the Arts & Sciences Bulletin.

Program Requirements

• Total Units Required: 36 Credits
• Students are expected to maintain satisfactory academic progress in accordance with academic milestones. Students entering their seventh year in the program will receive a warning letter in regards to reaching their stated degree length. Students entering their eighth year in the program will be required to obtain permission from the Associate Dean of Graduate Education.
• Note: Students must be enrolled in 9 graduate credits each semester to retain full-time status. As students complete their course work, if enrolled in fewer than 9 graduate credits, they must enroll in a specific Arts & Sciences graduate course that will show 0 units but does count as full-time status. Students should connect with their department to ensure proper enrollment prior to Add/Drop.
• Continued support is guaranteed for the duration of your graduate studies, provided that you maintain satisfactory progress towards completion of the degree. 

Required Courses

This generally requires two to five semesters and usually consists of four to nine courses in areas fundamental to the student's program. Students are expected to maintain a B average in graduate courses.

DBBS Required Courses

• Biol 5098 Graduate Research Fundamentals
• Biol 5011 Ethics & Research Science

Program Required Courses

• Biol 5495 Computational Molecular Biology
• Biol 5488 Genomics

Three Advanced Electives

Common options include the following:

• Biol 5014 Biotech Industry Innovators
• Biol 5285 Current Topics in Human and Mammalian Genetics
• Biol 5357 Chemistry and Physics of Biomolecules
• Biol 5483 Human Genetic Analysis
• Biol 5312 Macromolecular Interactions
• Biol 548 Nucleic Acids & Protein Biosynthesis
• Biol 5491 Advanced Genetics
• Math 493C Probability
• CSE 502N Data Structures and Algorithms
• CSE 514A Data Mining
• CSE 517A Machine Learning
• MSB 621 Computational Statistical Genetics
• INFO 558 Applications of Deep Neural Networks

Three Semesters of Journal Clubs 

Participation in  Biol 5496 Seminar in Computational Molecular Biology  is strongly encouraged but not required.

Laboratory Rotations

Selecting a thesis advisor is the most important decision a student makes in graduate school. To help each student make an informed, thoughtful choice, the Division builds in flexibility to explore options. Students usually participate in three lab rotations during their first year. Additional rotations can be arranged, and rotation lengths are flexible. Students usually begin their thesis research by the end of their first year.

Scientific Scholarship

Keeping abreast of scientific developments is critical for faculty and students alike. The Division offers many ways to stay current. More than 15 weekly biology seminars provide excellent opportunities to meet outstanding scientists from outside Washington University. Several annual symposia bring internationally recognized speakers to campus. Journal clubs meet weekly for students, postdoctoral fellows and faculty to present and discuss current scientific literature. A number of  Interdisciplinary Research Pathways  allow students to enhance their PhD program. Program retreats allow for informal interaction among students and faculty. The Division also provides funds for each student for professional development.

Qualifying Examinations

Progress toward the PhD is contingent upon the student passing examinations that are variously called  preliminary, qualifying, general, comprehensive,  or  major field exams.  The qualifying process varies according to the program. In some programs, it consists of a series of incremental, sequential, and cumulative exams over a considerable time. In others, the exams are held during a relatively short period of time. Exams may be replaced by one or more papers. The program, which determines the structure and schedule of the required examinations, is responsible for notifying the Office of Graduate Studies, Arts & Sciences, of the student’s outcome, whether successful or unsuccessful.

Program-specific information: In the spring/summer semesters of Year 2, students must pass a Qualifying Exam (QE). Following a successful QE defense, students will identify and finalize their committee and complete their thesis proposal by December 31 of Year 3.

Mentored Experience Requirements

Doctoral students at Washington University must complete a department-defined Mentored Experience. The Mentored Experience Requirement is a doctoral degree milestone that is notated on the student’s transcript when complete. Each department has an established  Mentored Experience Implementation Plan  in which the number of units that a student must earn through Mentored Teaching Experience(s) and/or Mentored Professional Experience(s) is defined. The Mentored Experience Implementation Plans outline how doctoral students within the discipline will be mentored to achieve competencies in teaching at basic and advanced levels. Some departments may elect to include Mentored Professional Experiences as an avenue for completing some units of the Mentored Experience Requirement. Doctoral students will enroll in LGS 6XXX Mentored Teaching Experience or LGS 7020 Mentored Professional Experience to signify their progression toward completing the overall Mentored Experience Requirement for the degree.

The Doctoral Dissertation

A Research Advisory Committee (RAC) must be created no later than the end of the student’s third year; departments may set shorter timelines (e.g., by the end of the student's second year) for this requirement. As evidence of the mastery of a specific field of knowledge and of the capacity for original scholarly work, each candidate must complete a dissertation that is approved by their RAC.

A  Title, Scope & Procedure Form for the dissertation must be signed by the committee members and by the program chair. It must be submitted to the Office of Graduate Studies, Arts & Sciences, at least 6 months before the degree is expected to be conferred or before beginning the fifth year of full-time enrollment, whichever is earlier.

A  Doctoral Dissertation Guide & Template  that give instructions regarding the format of the dissertation are available on the website of the Office of Graduate Studies, Arts & Sciences. Both should be read carefully at every stage of dissertation preparation.

The Office of Graduate Studies, Arts & Sciences, requires each student to make the full text of the dissertation available to the committee members for their review at least 1 week before the defense. Most degree programs require 2 or more weeks for the review period; students should check with their faculty.

The Dissertation Defense

Approval of the written dissertation by the RAC is necessary before the student can orally defend their dissertation. The Dissertation Defense Committee that observes and examines the student’s defense consists of at least five members, who normally meet these criteria:

• Three of the five must be full-time Washington University faculty members or, for programs offered by Washington University-affiliated partners, full-time members of a Washington University-affiliated partner institution who are authorized to supervise PhD students and who have appropriate expertise in the proposed field of study; one of these three must be the PhD student’s primary thesis advisor, and one may be a member of the emeritus faculty. A fourth member may come from inside or outside the student’s program. The fifth member must be from outside the student’s program; this fifth member may be a Washington University research professor or lecturer, a professor from another university, or a scholar from the private sector or government who holds a doctorate and maintains an active research program.
• Three of the five normally come from the student’s degree program; at least one of the five must not.

All committees must be approved by the Office of Graduate Studies, Arts & Sciences, regardless of whether they meet the normal criteria.

The committee is appointed by the Office of Graduate Studies, Arts & Sciences, upon the request of the degree program. The student is responsible for making the full text of the dissertation accessible to their committee members for their review in advance of the defense. Faculty and graduate students who are interested in the subject of the dissertation are normally welcome to attend all or part of the defense but may ask questions only at the discretion of the committee members. Although there is some variation among degree programs, the defense ordinarily focuses on the dissertation itself and its relation to the student’s field of expertise.

Submission of the Dissertation

After the defense, the student must submit an electronic copy of the dissertation online to the Office of Graduate Studies, Arts & Sciences. The submission website requires students to choose among publishing and copyrighting services offered by ProQuest’s ETD Administrator.  The degree program is responsible for delivering the final approval form, signed by the committee members at the defense and then by the program chair or director, to the Office of Graduate Studies, Arts & Sciences. Students who defend their dissertations successfully have not yet completed their PhD requirements; they finish earning their degree only when their dissertation submission has been accepted by the Office of Graduate Studies, Arts & Sciences.

Visit the Biology & Biomedical Sciences page for additional information about this program.

Contact Info

• Open positions
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• Alumni & Collaborations
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• Current Assembly Projects
• Older Assembly Projects
• Metagenomic Databases
• Ph.D. Programs
• JHU courses
• Summer Internships
• Consulting Core

Information for prospective Ph.D. students in Computational Biology or Bioinformatics

The Ph.D. programs in Computational Biology at Johns Hopkins University span four Departments and a wide range of research topics. Our programs provide interdisciplinary training in computational and quantitative approaches to scientific problems that include questions in genomics, medicine, genome engineering, sequencing technology, molecular biology, genetics, and others.

Our students are actively involved in high-profile research, and have developed very widely-used bioinformatics software systems such as Bowtie , Tophat , and Cufflinks . and the more-recent systems HISAT and Stringtie (for RNA-seq alignment and assembly) and Kraken (for metagenomic sequence analysis). The work they do with Hopkins faculty prepares them to go on to postdoctoral and tenure track faculty positions at top-ranked universities including (in recent years) Harvard, the University of Washington, Carnegie Mellon, the University of Maryland, and Brown.

Students in computational biology at Hopkins can enroll in one of four different Ph.D. programs. These include Biomedical Engineering, ranked #1 in the nation; Biostatistics, also ranked #1 in the nation; Biology, ranked #6 in the nation; and the rapidly growing Computer Science Department, ranked #23 in the nation. Hopkins is also ranked #4 in the nation in Bioinformatics, a ranking that just started appearing in 2022.

CCB faculty have appointments in each of these programs, and some of us maintain appointments in multiple programs. To determine which program fits your interests and background, browse the course lists below. Each program has a separate application process; please apply specifically to the departments you're interested in. Applications to multiple programs are permitted, but if you're not certain, we encourage you to contact potential faculty advisors before you apply. Wherever you apply, make it clear that your interest is Computational Biology.

Sample Course Offerings for Ph.D. students in Computational Biology

Department of biomedical engineering, whiting school of engineering.

The Johns Hopkins Department of Biomedical Engineering (BME), widely regarded as the top program of its kind in the world and ranked #1 in the nation by U.S. News , is dedicated to solving important scientific problems at the intersection of multiple disciplines and that have the potential to make a significant impact on medicine and health. At the intersection of inquiry and discovery, the department integrates biology, medicine, and engineering and draws upon the considerable strengths and talents of the Johns Hopkins Schools of Engineering and Medicine. See the BME Ph.D. program website for many details.

Department of Computer Science, Whiting School of Engineering

The faculty represent a broad spectrum of disciplines encompassing core computer science and many cross-disciplinary areas including Computational Biology and Medicine, Information Security, Machine Learning, Data Intensive Computing, Computer-Integrated Surgery, and Natural Language Processing.

Ph.D. program

A total of 8 courses are required, and a typical load is 3 courses per semester. See the CS Department website for details. For a look at courses that might be included in Ph.D. training, see this page , though note that it is not a comprehensive list. For the Computer Science Ph.D., 2 out of the required 8 classes can be taken outside the Department. These may include any of the courses in the BME, Biostatistics, and Biology programs listed on this page.

Department of Biostatistics, Bloomberg School of Public Health

Johns Hopkins Biostatistics is the oldest department of its kind in the world and has long been considered as one of the best. In 2022, it was ranked #1 in the nation by U.S. News .

All students in the Biostatistics Ph.D. program have to complete the core requirements:

• A two-year sequence on biostatistical methodology (140.751-756)
• A two-year sequence on probability and the foundations and theory of statistical science (550.620-621, 140.673-674, 140.771-772);
• Principles of Epidemiology (340.601)

In addition, students in computational biology might take:

• 140.776.01 Statistical Computing (3 credits)
• 140.638.01 Analysis of Biological Sequences (3 credits)
• 140.644.01 Statistica machine learning: methods, theory, and applications (4 credits)
• 140.688.01 Statistics for Genomics (3 credits)

Further courses might include 2-3 courses in Computer Science, BME, or Biology listed on this page.

Department of Biology, Krieger School of Arts and Sciences

The Hopkins Biology Graduate Program, founded in 1876, is the oldest Biology graduate school in the country. People like Thomas Morgan, E. B. Wilson, Edwin Conklin and Ross Harrison, were part of the initial graduate classes when the program was first founded. Hopkins is ranked #6 in the nation in Biological Sciences by U.S. News

Quantitative and computational biology are an integral part of the CMDB training program. During the first semester students attend Quantitative Biology Bootcamp, a one week intensive course in using computational tools and programming for biological data analysis. Two of our core courses - Graduate Biophysical Chemistry and Genomes and Development - each have an associated computational lab component.

Ph.D. in Cell, Molecular, Developmental Biology, and Biophysics (CMDB):

The CMDB core includes the following courses:

• 020.607 Quantitative Biology Bootcamp
• 020.674 Graduate Biophysical Chemistry
• 020.686 Advanced Cell Biology
• 020.637 Genomes and Development
• 020.668 Advanced Molecular Biology
• 020.606 Molecular Evolution
• 020.620 Stem Cells
• 020.630 Human Genetics
• 020.640 Epigenetics & Chromosome Dynamics
• 020.650 Eukaryotic Molecular Biology
• 020.644 RNA

Students in computational biology can use their electives to take more computationally intensive courses. You have considerable flexibility to design a program of study with your Ph.D. advisor.

The Center for Computational Biology at Johns Hopkins University

for Integrative Genomics Lewis-Sigler Institute

Ph.d. program requirements, qcb graduate program requirements.

See QCB Student Handbook  for program details.  

• QCB 515 Method and Logic in Quantitative Biology
• QCB 537 (fall term) and QCB 538 (spring term): Current Research Topics in the Quantitative Life Sciences
• COS/QCB 551  Introduction to Genomics and Computational Molecular Biology
• Three elective courses from the lists below, including at least one from the Quantitative  course list and one from the Biological  course list
• QCB 501  Topics in Ethics in Science, our Responsible Conduct of Research (RCR) course
• MOL 550 The Graduate Primer
• Participation in our Graduate Colloquium
• Research rotations in your first year (three required)
• General exam (taken in January of your second year)
• Teaching (usually completed in fourth year of study)
• Annual thesis committee meetings
• Dissertation defense

The course of study for each student must be approved by the Director of Graduate Studies in the beginning of their first year, and course substitutions are possible with the permission of the DGS.

QCB 515: Method and Logic in Quantitative Biology 

Close reading of published papers illustrating the principles, achievements, and difficulties that lie at the interface of theory and experiment in biology. Two important papers, read in advance by all students, will be considered each week; the emphasis will be on discussion with students as opposed to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, and maximum likelihood methods. A general tutorial on Matlab and specific tutorials for the four homework assignments will be available. 

COS/QCB 551: Introduction to Genomics and Computational Molecular Biology 

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques). 

QCB 537/538 Current Research Topics in the Quantitative Life Sciences

Mandatory first-year graduate course centered around the weekly QCB seminar series, intended to help develop competency in critical reading and assessment of academic literature across subfields early in graduate training. Class meetings comprise student-driven presentations and discussions surveying research topics relevant to upcoming talks, with an emphasis on latest methodologies and debates. Assessment includes seminar and class attendance, in-class and in-seminar participation, and peer evaluation.

LSI Graduate Colloquium 

QCB students are required to attend the LSI Graduate Colloquium during the fall and spring terms, usually held on Thursday afternoons. Second year students will give research talks in the fall term and fourth year students will present their work in the spring term. The series will end with first-year students giving short presentations on the work they have done in one of their rotations. 

QCB 501: Topics in Ethics in Science

Discussion and evaluation of the role professional researchers play in dealing with the reporting of research, responsible authorship, human and animal studies, misconduct and fraud in science, intellectual property, and professional conduct in scientific relationships. Participants are expected to read the materials and cases prior to each meeting. Successful completion is based on regular attendance and active participation in discussion. This half-term course is designed to satisfy federal funding agencies' requirements for training in the ethical practice of scientists. Required for graduate students and post-docs.

(must take at least one)

APC 524 /MAE 506/AST 506  Software Engineering for Scientific Computing 

CBE 517  Soft Matter Mechanics Fundamentals & Applications 

CHM 503/CBE 524/MSE 514  Introduction to Statistical Mechanics 

CHM 515  Biophysical Chemistry I 

CHM 516  Biophysical Chemistry II 

CHM 542  Principles of Macromolecular Structure: Protein Folding, Structure, and Design

COS 511  Theoretical Machine Learning

COS 513  Foundations of Probabilistic Modeling

COS 524/COS 424  Fundamentals of Machine Learning

COS 557 Artificial Intelligence for Precision Health

COS 597D  Advanced Topics in Computer Science: Advanced Computational Genomics

COS 597F  Advanced Topics in Computer Sci: Computational Biology of Single Cells

COS 597G  Advanced Topics in Computer Science: Understanding Large Language Models

COS 597O  Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations 

ELE 535  Machine Learning and Pattern Recognition 

MAE 550/MSE 560  - Lessons from Biology for Engineering Tiny Devices 

MAE 567/CBE 568  Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics 

MAT 586/APC 511/MOL 511/QCB 513  Computational Methods in Cryo-Electron Microscopy 

MOL 518  Quantitative Methods in Cell and Molecular Biology 

MSE 504/CHM 560/PHY 512/CBE 520  Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science 

NEU 437/537  Computational Neuroscience 

NEU 501  Cellular and Circuits Neuroscience 

NEU 560  Statistical Modeling and Analysis of Neural Data 

ORF 524  Statistical Theory and Methods

PHY 561/2  Biophysics

QCB 505/PHY 555  Topics in Biophysics and Quantitative Biology 

QCB 508  Foundations of Statistical Genomics

CHM 403  Advanced Organic Chemistry 

CHM/QCB 541  Chemical Biology II 

EEB 504  Fundamental Concepts in Ecology, Evolution, and Behavior II  

EEB 522 Colloquium on the Biology of Populations

MAE 566  Biomechanics and Biomaterials: From Cells to Organisms 

MAE 567/CBE 568  Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics

MOL 504  Cellular Biochemistry 

MOL 506  Cell Biology and Development 

MOL 521  - Systems Microbiology and Immunology (half-term course)

MOL 523  Molecular Basis of Cancer 

MOL 559  Viruses: Strategy & Tactics 

QCB 490  Molecular Mechanisms of Longevity

QCB 535  Biological networks across scales: Open problems and research methods of systems biology

QCB 570  Biochemistry of Physiology and Disease 

(note: these do not count towards course requirements)

APC 350  Introduction in Differential Equations  

COS 226  Algorithms and Data Structures

COS 343  Algorithms for Computational Biology 

EEB 324  Theoretical Ecology

MOL/QCB 485  Mathematical Models in Biology

ORF 309/MAT 380  Probability and Stochastic Systems

QCB 302  Research Topics in QCB

QCB 311 Genomics

Please visit Course Offerings  to see the most up-to-date course information.

Graduate School

Quantitative and Computational Biology

General information, program offerings:, department for program:, director of graduate studies:, graduate program administrator:.

The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty in the Institute and the Departments of Chemistry, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, evolutionary and population genomics, statistical genetics, and metabolomics and proteomics.

Program Highlights

An Outstanding Tradition:  Chartered in 1746, Princeton University has long been considered among the world’s most outstanding institutions of higher education, with particular strength in mathematics and the quantitative sciences. Building upon the legacies of greats such as Turing, von Neumann, Tukey, Compton, Feynman, and Einstein, Princeton established the Lewis-Sigler Institute of Integrative Genomics in 1999 to carry this tradition of quantitative science into the realm of biology.

World Class Research:  The Lewis-Sigler Institute and the QCB program focus on attacking problems of great fundamental significance using a mixture of theory, computation, and experimentation.

World Class Faculty:  The research efforts are led by the QCB program’s 50+ faculty, who include a Nobel Laureate, members of the National Academy of Sciences, Howard Hughes Investigators, and numerous faculty who have received major national research awards (e.g., NIH Pioneer, NIH Innovator, Packard, NSF PECASE, NSF CAREER, etc.).

Personalized Education:  A hallmark of any Princeton education is personal attention. The QCB program is no exception. Lab sizes are generally modest, typically 6 – 16 researchers, and all students have extensive direct contact with their faculty mentors. Many students choose to work at the interface of two different labs, enabling them to build close intellectual relationships with multiple principal investigators.

Stimulating Environment:  The physical heart of the QCB program is the Carl Icahn Laboratory, an architectural landmark located adjacent to biology, chemistry, physics, and mathematics on Princeton’s main campus. Students have access to a wealth of resources, both intellectual and tangible, such as world-leading capabilities in DNA sequencing, mass spectrometry, and microscopy. They also benefit from the friendly atmosphere of the program, which includes tea and cookies every afternoon. When not busy doing science, students can partake in an active campus social scene and world class arts and theater events on campus.

Program Offerings

Program offering: ph.d..

Five courses, QCB515, QCB535, QCB537, QCB538, and COS/QCB551, are required for all students, as is a Responsible Conduct in Research (RCR) course. Two elective courses must be taken from the list below, including at least one from the quantitative course list. Courses not on the approved lists may be taken as electives with approval from the DGS.

Note: The full course of study must be reviewed and approved by the Director of Graduate Studies (DGS).

Quantitative Courses (must take at least one)

• APC 524/MAE 506/AST 506 Software Engineering for Scientific Computing
• CBE 517 Soft Matter Mechanics: Fundamentals & Applications
• CHM 503/CBE 524/MSE 514 Introduction to Statistical Mechanics
• CHM 515 Biophysical Chemistry I 
• CHM 516 Biophysical Chemistry II
• CHM 542 Principles of Macromolecular Structure: Protein Folding, Structure, and Design
• COS 511 Theoretical Machine Learning
• COS 524/COS 424 Fundamentals of Machine Learning
• COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics
• COS 597F Advanced Topics in Computer Science: Computational Biology of Single Cells
• COS 597G Advanced Topics in Computer Science: Understanding Large Language Models
• COS 597O Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations 
• ELE 535 Machine Learning and Pattern Recognition
• MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics
• MAE 550/MSE 560 Lessons from Biology for Engineering Tiny Devices
• MAT 586/APC 511/MOL 511/QCB 513 Computational Methods in Cryo-Electron Microscopy
• MOL 518 Quantitative Methods in Cell and Molecular Biology
• MSE 504/CHM 560/PHY 512/CBE 520 Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science
• NEU 437/537 Computational Neuroscience
• NEU 501 Cellular and Circuits Neuroscience
• NEU 560 Statistical Modeling and Analysis of Neural Data
• ORF 524 Statistical Theory and Methods
• PHY 561/2 Biophysics
• QCB 505/PHY555 Topics in Biophysics and Quantitative Biology
• QCB 508 Foundations of Statistical Genomics

Biological Courses 

• CHM 403 Advanced Organic Chemistry
• CHM/QCB 541 Chemical Biology II
• EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II
• EEB 507 Recent Research in Population Biology
• MAE 566 Biomechanics and Biomaterials: From Cells to Organisms 
• MOL 504 Cellular Biochemistry
• MOL 506 Cell Biology and Development
• MOL 521 Systems Microbiology and Immunology
• MOL 523 Molecular Basis of Cancer
• MOL 559 Viruses: Strategy & Tactics
• QCB 490 Molecular Mechanisms of Longevity
• QCB 535 Biological Networks Across Scales: Open Problems and Research Methods of Systems Biology

Selected undergraduate courses of interest (Note: these do not count towards course requirements)

• APC 350 Introduction in Differential Equations
• COS 226 Algorithms and Data Structures
• COS 343 Algorithms for Computational Biology
• EEB 324 Theoretical Ecology
• MOL/QCB 485 Mathematical Models in Biology
• ORF/MAT 309/380 Probability and Stochastic Systems
• QCB 302 Research Topics in QCB
• QCB 311 Genomics  

Additional pre-generals requirements

Research Colloquium: QCB Graduate Colloquium QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held weekly on an afternoon during the fall and spring terms. First, second, and fourth year graduate students have the opportunity to present their research to peers. 

Rotations All students are required to complete a minimum of three research rotations during their first year of graduate study, with a maximum of four, to explore possible research advisers.

General exam

The general examination is usually taken in January of the second year, and consists of a 7 page written thesis proposal and a 2-hour oral session on the student’s thesis proposal.

Qualifying for the M.A.

The Master of Arts (M.A.) degree is normally an incidental degree on the way to a full Ph.D. and is earned after a student successfully passes the general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided the student has completed all coursework, pre-generals requirements, and the written portion of the generals examination.

A student must teach a minimum of one full-time assignment (6 AI hours) or teach two part-time assignments of 2 or more AI hours each. Students will typically teach in year 4 of the program.

Post-Generals requirements

Committee Meetings Research progress is overseen by a thesis committee selected by the student after passing the general exam. The committee consists of the thesis adviser(s) and two additional faculty members. At least one member must be QCB faculty. The thesis committee must be approved by the DGS. Annual thesis committee meetings are mandatory. 

Dissertation and FPO

The dissertation and final public oral exam (FPO) are required for all Ph.D. students. All students must write and successfully defend their dissertation according to Graduate School rules and requirements. 

• Ned S. Wingreen

Director of Graduate Studies

Executive committee.

• Brittany Adamson, Molecular Biology
• Joshua Akey, Integrative Genomics
• Julien F. Ayroles, Ecology & Evolutionary Biology
• William Bialek, Physics
• Michelle M. Chan, Molecular Biology
• Thomas Gregor, Physics
• Sarah D. Kocher, Ecology & Evolutionary Biology
• Michael S. Levine, Molecular Biology
• Coleen T. Murphy, Molecular Biology
• Yuri Pritykin, Computer Science
• Joshua D. Rabinowitz, Chemistry
• Joshua W. Shaevitz, Physics
• Stanislav Y. Shvartsman, Chemical and Biological Eng
• Mona Singh, Computer Science
• John D. Storey, Integrative Genomics
• Olga G. Troyanskaya, Computer Science
• Eric F. Wieschaus, Molecular Biology
• Ned S. Wingreen, Molecular Biology
• Martin Helmut Wühr, Molecular Biology

Associated Faculty

• Mohamed S. Abou Donia, Molecular Biology
• Robert H. Austin, Physics
• Bonnie L. Bassler, Molecular Biology
• Clifford P. Brangwynne, Chemical and Biological Eng
• Mark P. Brynildsen, Chemical and Biological Eng
• Curtis G. Callan, Physics
• Daniel J. Cohen, Mechanical & Aerospace Eng
• Ileana M. Cristea, Molecular Biology
• Danelle Devenport, Molecular Biology
• Adji Bousso Dieng, Computer Science
• Tatiana Engel, Princeton Neuroscience Inst
• Jianqing Fan, Oper Res and Financial Eng
• Elizabeth R. Gavis, Molecular Biology
• Zemer Gitai, Molecular Biology
• Frederick M. Hughson, Molecular Biology
• Martin C. Jonikas, Molecular Biology
• Yibin Kang, Molecular Biology
• Andrej Kosmrlj, Mechanical & Aerospace Eng
• Andrew M. Leifer, Physics
• Simon A. Levin, Ecology & Evolutionary Biology
• Jonathan M. Levine, Ecology & Evolutionary Biology
• Lindy McBride, Ecology & Evolutionary Biology
• Tom Muir, Chemistry
• Mala Murthy, Princeton Neuroscience Inst
• Cameron A. Myhrvold, Molecular Biology
• Celeste M. Nelson, Chemical and Biological Eng
• Sabine Petry, Molecular Biology
• Catherine Jensen Peña, Princeton Neuroscience Inst
• Eszter Posfai, Molecular Biology
• Ben Raphael, Computer Science
• Mohammad R. Seyedsayamdost, Chemistry
• Corina E. Tarnita, Ecology & Evolutionary Biology
• Jared E. Toettcher, Molecular Biology
• Samuel S. Wang, Princeton Neuroscience Inst
• Haw Yang, Chemistry
• Ellen Zhong, Computer Science

For a full list of faculty members and fellows please visit the department or program website.

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

CHM 541 - Chemical Biology II (also QCB 541)

Cos 551 - introduction to genomics and computational molecular biology (also mol 551/qcb 551), cos 557 - artificial intelligence for precision health (also qcb 557), mat 586 - computational methods in cryo-electron microscopy (also apc 511/mol 511/qcb 513), qcb 501 - topics in ethics in science (half-term), qcb 505 - topics in biophysics and quantitative biology (also phy 555), qcb 508 - foundations of statistical genomics, qcb 515 - method and logic in quantitative biology (also chm 517/eeb 517/mol 515/phy 570), qcb 570 - biochemistry of physiology and disease, qcb 590 - extramural research internship in quantitative and computational biology.

• Chancellor’s Message

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2023-24 edition, mathematical, computational, and systems biology, ph.d..

The graduate program in Mathematical, Computational, and Systems Biology (MCSB) is designed to meet the interdisciplinary training challenges of modern biology and function in concert with existing departmental programs (Departmental option) or as an individually tailored program (stand-alone option) leading to a Ph.D. degree.

The degree program provides students with both opportunity for rigorous training toward research careers in areas related to systems biology and flexibility through individualized faculty counseling on curricular needs, and access to a diverse group of affiliated faculty and research projects from member departments. Current member departments include Biomedical Engineering, Biological Chemistry, Computer Science, Developmental and Cell Biology, Ecology and Evolutionary Biology, Mathematics, Microbiology and Molecular Genetics, Molecular Biology and Biochemistry, Chemistry, and Physics.

If you have any questions or would like to learn more about the MCSB Program, please email [email protected] .

Students interested in the MCSB Program apply to the Office of Graduate Studies (OGS). Applicants must specify that they wish to pursue the M.S. or Ph.D. Upon completion of the M.S., students who may wish to pursue a Ph.D. may request to be evaluated together with the pool of prospective Ph.D. candidates for admission to the Ph.D. program.

Applicants are expected to hold a Bachelor’s degree in one of the Science, Technology, Engineering, and Mathematics (STEM) fields. Applicants are evaluated on the basis of their prior academic record and their potential for creative research and teaching, as demonstrated in submitted application materials (official university transcripts, letters of recommendation, GRE scores, and statement of purpose).

Required Core Courses

Enrolled students participate in a common first-year “gateway” program and must complete the seven required core courses (listed above). Students are assigned an MCSB Advisory Committee consisting of two participating faculty members to oversee course and laboratory work. Subsequently, students select a thesis advisor and choose between the Departmental or Interdisciplinary (Stand-Alone) options for the remainder of their Ph.D. training.

Departmental Option

For students who select the Departmental option, a faculty member in a participating department must agree to serve as the student’s thesis advisor. Completion of the Ph.D. is subject to the degree requirements of the departmental Ph.D. program in which the student enrolls. Participating departments accept both the course work and research conducted during the “gateway” year in partial fulfillment of such requirements. Students are encouraged to consult with the department of choice for specific information on additional requirements. All department student advisory committees are established according to the rules of the participating department. In addition, the student’s MCSB Advisory Committee meets annually to follow progress and provide additional guidance. The normative time to degree for students in the Departmental option is five years.

To complete the coursework requirements for the Departmental option, students must:

• Attend first-year bootcamp
• Perform at least two laboratory rotations; one in an experimental (wet) lab and one in a computational (dry) lab
• Complete the seven required core courses, in addition to any departmental requirements.

Interdisciplinary (Stand-Alone) Option

For students who select the stand-alone option, the student’s thesis advisor assumes the role of the Committee Chair when a participating MCSB faculty member agrees to accept that role. Adjustments to the MCSB Advisory Committee may be made based on the area of the student’s research, or by request of the student, thesis advisor, or committee members. The student meets biannually with the Advisory Committee until an Advancement to Candidacy Committee has formed, which then assumes the duties until the M.S. or Ph.D. defense. The normative time to degree for students in the Stand-Alone option is five years.

To complete the coursework requirements for the Stand-Alone option, students must:

• Complete the seven required core courses, plus five elective courses selected from Breadth Categories I and II.

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2023-2024 Catalogue

A PDF of the entire 2023-2024 catalogue.

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	University of Southern California
	Jul 19, 2024   USC Catalogue 2022-2023     USC Catalogue 2022-2023 [ARCHIVED CATALOGUE] | The Department of Quantitative and Computational Biology offers a PhD in Computational Biology and Bioinformatics. The PhD in Computational Biology and Bioinformatics is awarded in conformity with the general requirements of the USC Graduate School. Study in the Computational Biology and Bioinformatics PhD program emphasizes original research that culminates in a doctoral dissertation. A separately published guide, available from the Department of Quantitative and Computational Biology, provides additional information on the topics listed below, along with other program policies. Application deadline: December 15 Course Requirements Students in the Computational Biology and Bioinformatics PhD program take graduate courses that cover topics from biology, computer science, mathematics, statistics and other disciplines. These courses guarantee a broad foundation in our field, and ensure students have sufficient scientific background and intellectual tools for success in research. A list of required courses can be found at the bottom of this page. Screening Procedure As per Graduate School requirements, all students in the Computational Biology and Bioinformatics PhD program undergo a screening procedure. This procedure consists of written tests taken by each cohort before the end of their first year. Advisement Each student in the Computational Biology and Bioinformatics PhD program is assigned an academic adviser from among the Department of Quantitative and Computational Biology’s faculty. This person will act as the student’s dissertation committee chair. Advisers are determined by the end of the first year based on shared research interests with the student. The primary role of the adviser is to guide the student as they work towards their dissertation. Qualifying Examination Students must pass a qualifying examination to advance to candidacy in the Computational Biology and Bioinformatics PhD program. The qualifying exam consists of a written part and an oral part. Both parts are evaluated by a faculty qualifying committee, which is formed separately for each student and is led by the student’s dissertation chair. Dissertation After advancing to candidacy, each student forms a faculty dissertation committee. Students work toward their dissertation research under the guidance of their adviser and with input from their dissertation committee. The dissertation committee meets annually to ensure appropriate degree progress. The central requirement of the doctorate is a dissertation based on the student’s original research that makes a substantial advance to scientific knowledge or technical capability in our field. Required Courses (30 units) BISC 593 Practicum in Teaching the Biological Sciences Units: 2 CSCI 570 Analysis of Algorithms Units: 4 MATH 505a Applied Probability Units: 3 MATH 541a Introduction to Mathematical Statistics Units: 3 QBIO 502 Molecular Biology for Quantitative Scientists Units: 4 QBIO 542 Seminar in Computational Biology Units: 1 * QBIO 547 Ethics and Professional Conduct in Computational Biology Units: 1 QBIO 577 Computational Molecular Biology Laboratory Units: 2 QBIO 578a Computational Molecular Biology Units: 3 QBIO 578b Computational Molecular Biology Units: 3 * Students register for QBIO 542 for 5 semesters. Elective Courses (6 units) Choose a minimum of 6 units from the following courses: BISC 502a Molecular Genetics and Biochemistry Units: 4 BISC 502b Molecular Genetics and Biochemistry Units: 4 BME 530 Introduction to Systems Biology Units: 4 CSCI 521 Optimization: Theory and Algorithms Units: 3 CSCI 559 Machine Learning I: Supervised Methods Units: 4 CSCI 567 Machine Learning Units: 4 CSCI 596 Scientific Computing and Visualization Units: 4 CSCI 670x Advanced Analysis of Algorithms Units: 4 MATH 502a Numerical Analysis Units: 3 MATH 505b Applied Probability Units: 3 MATH 555a Partial Differential Equations Units: 3 MATH 565a Ordinary Differential Equations Units: 3 PHYS 516 Methods of Computational Physics Units: 3 PHYS 518 Thermodynamics and Statistical Mechanics Units: 3 Research and Dissertation (4 units minimum) QBIO 794a Doctoral Dissertation Units: 2 QBIO 794b Doctoral Dissertation Units: 2 Students may register for additional units by using QBIO 790 or the remaining QBIO 794 courses. Skip to Content Catalog Home Institution Home Graduate Catalog / Perelman School of Medicine / Genomics and Computational Biology, PhD Genomics and computational biology are now at the center of biomedical research. These disciplines take a holistic approach to ask about the origins, functions, and interactions of whole systems, using both experimental and theoretical work. Therefore, these studies require knowledge, skills, and, most importantly, synthesis and integration of biology, computer science, mathematics, statistics, and engineering. This synthesis and integration requires a new generation of scientists that thrives in cross-disciplinary research. This can include molecular, cellular, and organismal biology (including genetics), mathematics, statistics, chemistry, and engineering. The goal of the GCB program is to train students that are experts in one or more of these disciplines and well versed in the others. We provide a comprehensive training program in Genomics and Computational Biology that gives students a broad foundation in the biological and quantitative sciences along with practical experience in computational and experimental genomics. The knowledge gained in this program will serve students in their careers as technology progresses. For more information: https://www.med.upenn.edu/gcb/ View the University’s Academic Rules for PhD Programs . Required Courses  Course List Code Title Course Units Coursework Statistics for Genomics and Biomedical Informatics Experimental Genome Science Biology course ( or ) Fundamentals of Computational Biology Machine Learning Approach elective Biological Specialty elective Select three additional electives Research Lab Rotation Pre-Dissertation Research Dissertation Applied Machine Learning The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation. Sample Plan of Study Course List Code Title Course Units Year 1 Fall Statistics for Genomics and Biomedical Informatics Experimental Genome Science Fundamentals of Computational Biology Lab Rotation Spring or ) Lab Rotation Lab Rotation Summer Pre-Dissertation Research Year 2 Fall Applied Machine Learning Pre-Dissertation Research Machine Learning Spring Pre-Dissertation Research Year 3 and Beyond Dissertation Print Options Print this page. The PDF will include all information unique to this page. Informatics Bioinformatics and computational biology phd course requirements. Academic PHD Bioinformatics Plan of Study Please check the Manual of Rules and Regulations of the Graduate College for a complete description of the Ph.D. guidelines and requirements of the Graduate College. The requirements described here, specific to the Bioinformatics Ph.D. program, are in addition to the University-wide requirements for doctoral degrees. The Ph.D. program in the Bioinformatics and Computational Biology subprogram inherits all course requirements of the Informatics Ph.D. program; that is, a total of 72 semester hours (37 semester hours of coursework) beyond the bachelor’s degree, consisting of 21 semester hours in core coursework (Bioinformatics and Computational Biology, Genetics, Biology, and Informatics), seminar hours in Ethics, and 6 hours in Grad Statistics coursework. The remaining 6 hours consist of electives selected in consultation with the student’s advisor. The Ph.D. also requires satisfactory performance on the comprehensive exam, and the production and formal defense of a dissertation that describes original research results. Every Ph.D. student must have a faculty advisor with an appointment in the Bioinformatics program. Upon admission, each student is assigned a temporary academic advisor who can assist in guiding the individual’s curriculum and plan of study. During their first year in the program, it is expected that the student will choose a faculty member whose research interests align with their own to serve as academic and research advisor, and chair of the student’s thesis committee. The advisor / advisee relationship requires the consent of both parties and can be terminated by either upon notice. It is required that a student will complete a Plan of Study form in consultation with his/her advisor every semester, and submit the completed form to the IGPI office for approval. The Plan of Study form is located at the bottom of this page. Examination committees must be composed of a minimum of five faculty members, per graduate college guidelines. IGPI-BCB students’ committees must include their research Advisor and at least 2 program-affiliated faculty from any of the following disciplines: Engineering, Genetics, Biology, Biosciences, and IGPI. At minimum, two committee members must have never coauthored a peer-reviewed journal article with the student. Ph.D. Guidelines & Milestones Complete by fall of Year 2 Minimum committee of 5 professors, including the advisor Format: Research or project in the form of a paper with a prepared presentation, turn in the written portion of the exam to your committee at least 2 weeks prior to oral exam – pass or fail Students must choose one of the following qualifier formats: A) An NIH-style proposal (typically proposed by the student and advisor; may also be assigned by the committee). Students are encouraged to submit the proposal as a pre-doctoral NIH Fellowship (F31). The purpose of this individual pre-doctoral research training fellowship is to provide support for promising doctoral candidates who will be performing dissertation research and training in scientific health-related fields relevant to the missions of the participating NIH Institutes and Centers (ICs) during the tenure of the award. These fellowships allow a student to develop a research idea and provide support for typically 2-3 years. The advisor and/or committee may decide to have on- or off-topic proposals. The committee (which includes the advisor), has the final say regarding on- or off-topic proposals by majority vote. The format gives students a year to polish and improve the proposal for submission to the NIH (http://grants.nih.gov/training/F_files_nrsa.htm) . B) Open-ended research question:  Students who select this qualifier option will be assigned, by their examining committee, an open-ended problem in the area of the student's intended thesis research. Ideally, the student will be presented with this problem no later than the end of the spring semester of the first year of study, along with a designated date/time for the examination (typically at the beginning of the fall semester), and the student will be expected to develop a solution during the summer. On the assigned examination date, the student will submit a written response to the assigned problem. The student will be expected to defend this response at an oral examination during the fall semester before the Examining Committee. The actual format of the response will depend on the specific problem assigned and specified by the Committee in the problem description, but is expected to involve the application of bioinformatics techniques towards the solution of a specific problem within the student's research area. Under certain circumstances, these guidelines, with respect to the problem due date and oral examination, might require modification to suit extenuating circumstances of the student or the Committee. Successful completion of the Ph.D. Qualifying Exam is required before a student is to be considered a Ph.D. graduate student. Students will have two chances to pass the Ph.D. Qualifying Exam. If students receive an evaluation of “unsatisfactory” on their first Qualifying Exam attempt, a different topic will be selected for the open-ended problem. The Qualifying Exam is designed to ensure that the student starts early in gaining research experience; it also ensures that the student has the potential to conduct doctoral-level research. The student may retake the qualifier once. A second failure will result in termination from the program. Comprehensive Exam Expected in Fall Year 3, allowing flexibility to accommodate scheduling of specific courses Format: On-topic thesis proposal Committee: Minimum of five professors, two of whom are affiliated IGPI faculty. Note: This examination satisfies the University’s comprehensive exam requirement. After 2-3 years of working in conjunction with a research advisor on a problem in Bioinformatics, the size and scope of the research project becomes evident to the student, as well as the advisor. To clearly define a problem or hypothesis under examination and describe a clear, logical process to solve that problem or test the hypothesis, the student will develop a written document describing the problem/hypothesis and solution/experiments. This document represents the Ph.D. Comprehensive Exam. The Ph.D. Comprehensive Examination consists of a proposal, formatted in the style of an NIH grant proposal, outlining the student's Ph.D. research. It is expected that the Ph.D. Comprehensive Exam will be completed one year after the Ph.D. Qualifying Exam, but might be completed later, at the discretion of the student's examining committee. The quality of the proposal will be determined through its assessment by the Examining Committee, and a formal oral presentation is required. The final document will consist of a 12-page NIH-style grant application on the thesis, plus a comprehensive literature review (no page limit). The proposal should include the following: Student name Committee members and their academic departments Committee chair (research adviser) Specific Aims Background and Significance Preliminary Studies (optional, but recommended) Research Plan (include expected results and their significance, and a discussion of potential pitfalls / workarounds) Provide a specific schedule for the completion of the proposed studies, with explicit reference to the work proposed in the Research Plan. A complete list of cited references. Proposal Defense The proposal defense is part of the Comprehensive Exam. Final Oral Defense/ Final Examination (Thesis Defense) Format: Written thesis and presentation Committee: Minimum of five professors, with at least two affiliated with the IGPI. Upon satisfactory completion of the Ph.D. thesis, the student will submit a final draft of the dissertation to the members of their Examining Committee. Following an assessment of the dissertation by the student's Examining Committee, the student will defend it orally in an open and public forum. The Examination Committee may then ask additional questions in a meeting between the candidate and the committee. Satisfactory performance in this final examination will result in a recommendation by the Committee to the Graduate College that the student be awarded a Ph.D. in Interdisciplinary Graduate Program in Bioinformatics and Computational Biology. In addition to the formal examination process, students in the Ph.D. program are evaluated on a yearly basis to ensure that they are making satisfactory academic progress. By September 15th, each student and the student’s advisor are required to submit an evaluation assessment of the student’s progress, outlining past year accomplishments and plans for the current year, including Ph.D. milestones. The Advisory Board reviews these summaries and sends the student a letter summarizing their status in the program. Students who are failing to make satisfactory progress are expected to correct any deficiencies and move to the next milestone within one year. Failure to do so will result in dismissal from the program. Apply to BBSP Bioinformatics & Computational Biology Program website :  https://bcb.unc.edu/ director of graduate studies : will valdar, phd student services specialist: john cornett, program overview. Modern biology is being greatly enriched by an infusion of ideas from computational and mathematical fields, including computer science, information science, mathematics, operations research and statistics. In turn, biological problems are motivating innovations in these computational sciences. There is a high demand for scientists who can bridge these disciplines. The goal of the Curriculum in Bioinformatics and Computational Biology is to train such scientists through a rigorous and balanced curriculum that transcends traditional departmental boundaries. The required coursework is designed with three tiers of formal training: foundational (introductory) courses, core modules, and electives. Since incoming students come from a broad range of disciplines (e.g., math, computer science, biology, genetics, statistics), it is important to ensure that all students have a common foundation on which to build their BCB training. The first year is dedicated to establishing this foundation and training all students with a common set of core BCB courses. Written Exam : The written component is taken at the end of the first year (May).  The exam may be postponed until the end of the second year if necessary with the approval of the BCB Director.  Students are assigned a reading list of about 10 research papers focusing on key analytical skills that should have been acquired from completion of core coursework during the first year.  These papers are released to during a study period two weeks prior to the exam date.  The exam itself is administered as a take-home exam over the course of four days.  Exams are graded as either pass or fail.  Students who do not pass have the option to take the exam again the following year. Students who do not pass on a second attempt do not proceed to Ph.D. candidacy. Oral Exam : The oral component of the exam must be taken before the end of the third academic year.  Students are expected to submit a 6 page written proposal in the form of a fellowship application (e.g. NIH or NSF) to their thesis committee describing their dissertation research project.  Details regarding the content of the proposal should be discussed no later than the annual thesis committee meeting at the beginning of the third year.   Detailed guidelines for conducting the exam may be found here .  Once the proposal is submitted to the thesis committee, students are required to defend their proposals during an oral exam given by their thesis committee.  Students are expected to demonstrate sufficient knowledge in their chosen research area and feasibility in completing their research plan by the end of the fifth year.  The specific content of these oral exams is dictated by the thesis committee and moderated by the committee chair.  Students who fail the exam have the option to take the exam again at a later date under terms and conditions set by their committee.  Students who fail a second time will be dismissed from the program. All Programs Online Minors Graduate Programs Online Courses Apply for Admissions Financial Aid Academic Integrity Academic Policies Accessibility Advising Services Career Services Health and Wellness Register for Classes State Authorization Student Complaint Policy Student Life Writing Center Online Learning Basics Course Delivery Methods Strategies for Online Learning Success What Technologies Do I Need? Student Ambassador Program Request Info Bioinformatics and Computational Biology (PhD) Program description. Researchers in the field of bioinformatics and computational biology collect, store, analyze, and present complex biological data using high-performance computing. Through this work, critical contributions are made to disease detection, drug design, forensics, agriculture, and environmental sciences. This research-oriented program trains a new generation of computational biologists for careers in private industry, academia, and government agencies. The program provides students with interdisciplinary academic curriculum that includes fundamental bioscience courses as well as advanced courses in bioinformatics. In general, course requirements are completed within the first two years of study. Completion of coursework, the comprehensive exam, and a successful dissertation proposal results in advancement to candidacy status. In the next phase, students conduct research guided by a faculty member. The program is concluded with the defense of PhD dissertation. The program is structured to be accessible for full- and part-time students. The courses are offered in a hybrid or distance learning format without requiring students to travel to campus. Because the research work is computational, students may choose to earn their doctoral degree entirely remotely or in a traditional face-to-face format. What it costs The total cost for this program is  $793.25  per credit hour for VA state residents or  $1,681.25 per credit hour for Non-VA residents. New Student and other mandatory university and course fees may apply. Application Deadline For application information and deadlines, see the Admissions site . Program Summary 100 Percent Online Program: Bioinformatics and Computational Biology Degree: PhD College/School: College of Science Credits: 72 total  Who should apply? The program is inherently interdisciplinary and consequently the students coming to the program are likely to have a background in biology, computer science, or chemistry. Students select one of the areas in bioinformatics and computational biology studied by a faculty member, who will serve as their advisor.  Thus, the students interested in studying biological problems utilizing computational methods are encouraged to apply. Why choose George Mason? Life science is a rapidly developing field demanding highly trained computational biologists. Our program addresses the growing demand by preparing a new generation of bioinformaticians to be employed in industry, academia, or government. By joining the program, students will participate in advanced, cutting-edge research. Students benefit from cooperation with neighboring world-class research institutions, including the NIH, USDA, FDA, FBI, and many other universities, research institutes, museums, government, and military laboratories in the Greater Washington, DC area. Courses in this program are taught by acclaimed professors and experts in the field, including Saleet Jafri, Dmitri Klimov, Donald Seto, Jeffrey Solka, Aman Ullah, and Iosif Vaisman as well as faculty from other departments. The program offers excellent opportunity for professionals with MS degree to complete PhD program on a part-time basis. The entire doctoral degree can be earned online without the need to travel to campus. Program requirements are subject to change; please refer to the Mason Course Catalog for the most up-to-date requirements. Requirements: 72 credits total 6 credits fundamental biosciences courses 13 credits core courses 3 credits colloquium course (1 credit each instance) 3 credits lab rotation (1 credit each instance) 12-24 credits research course 23-35 credits elective courses All the courses needed to complete the entire program are offered online. Most online courses are  delivered synchronously . Classes are scheduled 4:30 p.m. (eastern standard time) or later to accommodate students employed full-time. Offerings vary by semester. Fundamental Biosciences Courses BINF 701 Systems Biology | 3 credits BINF 702 Biological Data Analysis | 3 credits Core Courses BINF 690 Numerical Methods for Bioinformatics | 3 credits BINF 705 Research Ethics | 1 credit BINF 730 Biological Sequence and Genome Analysis | 3 credits BINF 731 Protein Structure Analysis | 3 credits BINF 740 Introduction to Biophysics | 3 credits Colloquium (3 instances) BINF 704 Colloquium in Bioinformatics | 1 credit Bioinformatics Lab Rotation (3 instances) BINF 703 Bioinformatics Lab Rotation | 1 credit Dissertation Research BINF 998 Doctoral Dissertation Proposal | 1-12 credits BINF 999 Doctoral Dissertation | 1-12 credits (must earn minimum 3 credits) Electives may be graduate level coursework selected from bioinformatics, biology, biotechnology, statistics, computer science, and information systems courses. Review class schedule for latest course offerings. Contact your faculty advisor for approval of elective course selection. Tuition (2021-2022) TUITION CLASSIFICATION COST PER CREDIT HOUR Virginia Resident $643.00 Non-Virginia Resident $1,531.00 Mandatory Student Fee $150.25 Total Cost per Credit for Virginia Residents Total Cost per Credit for Non-Virginia Residents Graduate New Student Fee $60 (non-refundable, one-time fee) Please Note : In addition to the tuition and fees described above, various course and lab fees may be assessed depending on the course(s) in which the student is enrolled. Please refer to the Students Accounts Office  website for more information on tuition and fees. For information on loans and scholarships, visit the  Office of Student Financial Aid . For information regarding grants, tuition waivers and other merit aid, please inquire with your graduate department. Career Descriptions Because of explosive growth in life sciences, biotechnology, and drug design, there is a strong demand for bioinformatics scientists employing computational methods to advance the scientific understanding of living systems. Broadly speaking, the bioinformatics employment falls into several categories. Researchers: Academia, government, and commercial sector are hiring employees trained in bioinformatics to support their research. People in these positions generally work in a specific area of research. Instruction: There is a robust demand for teaching bioinformatics and computational biology. Some PhD level bioinformaticians will pursue an academic career developing their own research agenda and teach at universities. To this end, they typically receive an additional post-doctoral training in one of research labs for few years. Many institutions are also seeking dedicated instructors to teach bioinformatics. Support of core facilities: Universities or government institutes tend to establish central resources for their labs referred to as core facilities. The personnel supporting such core facilities often require research experience and PhD level degrees. Software developers: Many companies within the Greater Washington area and nationally are interested in developing and deploying computational algorithms and tools. Such jobs often require research experience and advanced skills only provided at PhD level. Students are strongly encouraged to check online postings for job opportunities in bioinformatics and computational biology. As a Mason student, you may also contact  Mason Career Services  for more career information and counseling. USEFUL RESOURCES   Email: Graduate Admissions Phone: (703) 993-9532 Email: All applicants should review School of Systems Biology Application Information . For international applicants, see guidelines for  International Graduate Requirements. Prerequisites Hold a bachelor’s degree in biology, computer science, or related field Achieve a minimum GPA of 3.25 in the last earned degree Have taken courses in molecular biology, biochemistry, genetics, calculus, computer programming and data structures, and probability and statistics. Students with deficiencies in one or more of these areas may be admitted, but required to take additional courses, some of which may not be applicable to the degree’s course total. Have a working knowledge of a computer programming language Application Materials Graduate application Application fee In-state tuition form (if applicable) Two official transcripts from all colleges and universities attended Goals statement Three letters of recommendation from faculty members or individuals who have personal knowledge of your academic or professional capabilities If you have any questions during the application process, please contact COS Graduate Admissions at [email protected] or (703) 993-3430. Ready to apply? Already enrolled, interested in learning more. Equity & Inclusion Computational Biology Methods This domain emphasis will no longer be offered after Spring 2022. Any student wishing to declare or change to this domain emphases must submit their declaration or change request to Data Science Advising by Friday of the last week of Spring 2022. Students interested in this area who will declare after Spring 2022 are encouraged to consider the domain emphasis in  Computational Methods in Molecular and Genomic Biology . This domain emphasis will prepare students for work or graduate school in bioinformatics or computational biology.  Students with this emphasis will be able to understand how computational methods are used to elucidate the mechanisms of cellular processing of genetic data and will prepare them for computational analyses of DNA and other molecular biological data. From the lists shown below, students will select one course from the lower-division, and two courses from the upper-division. The lower division course is a required element of the Domain Emphasis. Courses in this domain emphasis are often restricted by major to enroll, and several have extensive prerequisites. It may be difficult to complete this emphasis given these restrictions. Students are advised to make appropriate alternate plans.  Prerequisites are shown within square brackets. Lower Division (select one) BIOLOGY 1A. General Biology Lecture (3 units) [Prerequisite: CHEM 1A; Co-requisite: BIOLOGY 1AL] BIOLOGY 1B. General Biology Lecture and Laboratory (4 units) MATH 53. Multivariable Calculus (4 units) Upper Division (select two) BIO ENG 131 or C131. Introduction to Computational Molecular and Cell Biology (4 units) [Prerequisites: BIO ENG 11 or BIOLOGY 1A, COMPSCI 61A or ENGIN 7] BIO ENG 134. Genetic Design Animation (4 units) BIO ENG 145. Introduction to Machine Learning in Computational Biology (4 units) CMPBIO C131. Introduction to Computational Molecular and Cell Biology (4 units) [Prerequisites: BIO ENG 11 or BIOLOGY 1A, COMPSCI 61A or ENGIN 7] CMPBIO 156. Human Genome, Environment, and Public Health (4 units) COMPSCI 176. Algorithms for Computational Biology (4 units) [Prerequisites: COMPSCI 70, COMPSCI 170] MATH 127. Mathematical and Computational Methods in Molecular Biology (4 units) [Prerequisites: MATH 53, MATH 55] MCELLBI 137L. Physical Biology of the Cell (3 units) INTEGBI 161. Population and Evolutionary  Genetics (4 units) [Prerequisites: BIO 1A, BIO 1B, MATH 10A/16A] These courses are no longer expected to be offered, but are still accepted for the domain emphasis for those students who have previously completed them: BIO ENG 144. Introduction to Protein Informatics (4 units) Unit values and prerequisites are subject to change. Please refer to guide.berkeley.edu for the most up-to-date course information. Bioinformatics & Computational Biology Graduate Program Search Submit search PhD Requirements BCB core courses BCB 567 (Alt S) BCB 568 (S) BCB 570 (F) Bioinformatics Algorithms Statistical Bioinformatics Systems Biology 3 cr 3 cr 3 cr Advanced Biology Core Requirement Variable GDCB 511, Molecular Genetics AnSci 556, Current Topics in Genome Analysis EEOB 561, Evolutionary and Ecological Genomics EEOB 563, Molecular Phylogenetics 3 cr. Advanced group requirements Variable Variable 6 cr. Workshops and symposia BCB 593 (F) Workshop in Bioinformatics and Computational Biology 1 credit Student research seminar BCB 690 (S) BCB Student Research Seminar 2 times Faculty seminar BCB 691 (F) BCB Faculty Research Seminar 1 time Research Rotation (first year only) BCB 697 (F S) BCB Research Rotations 3 credits per semester Research BCB 699 (F S SS) Research Minimum research credits for PhD is 10 credits.  There is no maximum. Bioethics training GR ST 565 (F, S) Responsible Conduct of Research in Science and Engineering 1 cr. Graduate English Variable (for non-native English speakers only) Determined by placement exam   Total Credit Hours     72 course credits required to obtain PhD Degree in Bioinformatics and Computational Biology Crimson Careers For Employers Harvard College Harvard Kenneth C. Griffin Graduate School of Arts & Sciences Harvard Extension School Premed / Pre-Health Families & Supporters Faculty & Staff Prospective Students First Generation / Low Income International Students Students of Color Students with Disabilities Undocumented Students Explore Interests & Make Career Decisions Create a Resume/CV or Cover Letter Expand Your Network Engage with Employers Search for a Job Find an Internship January Experiences (College) Find & Apply for Summer Opportunities Funding Prepare for an Interview Negotiate an Offer Apply to Graduate or Professional School Access Resources AI for Professional Development and Exploration Arts & Entertainment Business & Entrepreneurship Climate, Sustainability, Environment, Energy Government, Int’l Relations, Education, Law, Nonprofits Life Sciences & Health Technology & Engineering Still Exploring Talk to an Advisor How to Become an Industry Computational Biologist in a Year Share This: Share How to Become an Industry Computational Biologist in a Year on Facebook Share How to Become an Industry Computational Biologist in a Year on LinkedIn Share How to Become an Industry Computational Biologist in a Year on X Recently, MCS Advisors were able to attend the MassBioEd Life Sciences Workforce Conference where they discussed how the demand for the life sciences workforce in Massachusetts continues to grow, especially for those with interdisciplinary skillsets, like those of computational biologists. Read more in the 2023 Massachusetts Life Sciences Employment Outlook Report . Leveraging our Harvard network, we asked alumnus, Dean Lee, Compbio guru, to share his story, and insights into this exciting field. In February of 2017, I was officially stuck. I was in my late 20s, I had been working towards academic science for eight years, but I had also just decided to take a gap year from my graduate neuroscience program at Harvard. Grad school made it clear to me that I would not be happy if I continued with academic science, so I was back at the drawing board. As lost as I felt, I knew that I wasn’t navigating completely blindly; I still enjoyed the life sciences, and I just needed to find a way to work on interesting biological questions beyond the confines of academia. I knew only vaguely that I wanted to transition into computational biology (compbio). I noticed that data generation from biological experiments was becoming cheaper each year, but most biologists were not equipped to analyze this dramatically increasing amount of data. I guessed that if I could figure out how to analyze that data for them, then I can still work on exciting biological questions and maybe even get paid for it! For the next 2.5 years, I struggled to acquire the necessary skills for becoming a computational biologist in the biotech/pharma industry. The bar seemed high; I wasn’t sure if or how I would learn enough programming, math, and statistics to be qualified. No one I spoke with could give me clear guidance on how to make this transition. I found many master’s programs that claimed to be a direct path to industry roles, but upon closer inspection most of them seemed to be money grabs that offered content that was too generic to be useful and training that was outdated (Ex. programming in Perl, analysis with Galaxy ). Even if some of those master’s programs were useful, I couldn’t afford them anyway. Eventually, I was able to navigate to my first industry compbio role, but only by trial and error. In retrospect, I probably could have made this transition in less than a year if I had proper guidance. I hope to provide a bit more clarity to this process so you don’t have to spend as much time as I did groping in the dark. I will highlight several practical skills/experiences that will help you prepare for a compbio job (my examples will be a bit biased towards omics-related compbio). These components can be acquired simultaneously, and at any stage in your postsecondary education: bachelor’s, master’s, PhD, or postdoc. Those with more years of education might reasonably require less time to acquire these components, while those with fewer years of education might require more time. 1. Python and R (6 months- 1 year) Want to know our little secret? Most industry computational biologists are not expert coders. I would be ashamed to admit how many for loops I write. Our product is not code; our product is biological insights we extract from data. We tend to perform ad hoc, highly customized analyses to answer niche questions. We are often superusers of a finite set of powerful Python/R packages that do all the heavy lifting for us in a particular domain of biology, rather than general programming maestros. We are very good at debugging by googling. We usually don’t need to code at the level of Google programmers. With that in mind, your goal then is to become comfortable enough with Python and R such that you can quickly adopt any set of packages designed for biological data analysis. This familiarity should not require years and years of time. There are countless free online resources from which you can learn standard Python and R syntax. Start with one language, then eventually you can pick up the other. I personally think Python is the more efficient language and that compbio is slowly shifting towards Python. But for now many of the most popular packages for analyzing biological data are still in R, so it’s good to just learn both. Make sure you learn how to make informative plots. Keep it simple. Boxplots, scatterplots, and heatmaps made with seaborn, matplotlib, or ggplot2 can go a long way. 2. Statistics (1 year) I know machine learning is all the rage, but before you sink your teeth into the fancier techniques of machine learning, you should master the more traditional but still powerful approaches from statistics. Be very comfortable with foundational statistical topics/techniques such as probability theory, basic discrete and continuous distributions, hypothesis testing, p-values, multiple testing correction, various ways of normalizing data, measures of correlation, linear regression, logistic regression, principal component analysis, and cluster analysis. Your standard year-long college-level statistics course series should do the trick. Many free online courses also will teach you well. Don’t just watch videos, however. Work out problems by hand so you learn these concepts deeply. Your future self will thank you. 3. Deep Understanding of a Field of Biology (1-2 years) Industry computational biologists never work alone. We always work with bench scientists who generate the data we analyze. So we must speak their language. We need to understand the field in biology they are speaking from. We must understand why they designed their experiments a certain way, because it informs how we analyze their data. Being able to sympathize with the challenges faced by bench scientists also helps us to build positive working relationships with them. For this reason, experience as a bench scientist is highly relevant preparation for compbio roles. The better we can bridge the data-to-analysis-to-insight gap, the more valuable we are as computational biologists. To gain deep understanding of a field in biology, read lots of primary literature in that domain. This is the most time-consuming piece of your preparation for a compbio role, but also the most fun! If you are a PhD student or postdoc in the life sciences, you should already have this skill; little to no further preparation is needed here. For undergrads and master’s students, please make sure that you learn how to dissect primary literature. It doesn’t matter how many or few biology classes you take; at the end of the day, you should be able to judge a Nature/Cell/Science article on its merits. There is no shortcut to learning this skill. You just have to sit down and read. Google is your friend. Joining a journal club can help. Your first scientific papers may take 10-20 hours each to digest. One way to measure your ability to digest biology papers is to see whether you can pick up any Nature/Cell/Science paper in your chosen biological field and glean the gist of it in 15 minutes. You should be able to give an overview of the paper to a scientifically literate friend by drawing/writing on a single sheet of paper. The ability to do this implies you are familiar with the fundamental biology being addressed, the most popular/powerful experimental methods in that field, and the plots typically used to visualize results. In addition to learning a field of biology, such as immunology or microbiology, we also have to follow the most recent technical advances in our own field of computational biology. New methods are published pretty much daily, and part of our jobs is to quickly decide which methods make sense and which do not. Having the ability to parse compbio primary literature will give you an additional edge in your preparation for an industry compbio role. 4. Compbio Project (3-6 months) You need to complete a meaningful analysis of biological data as the final part of your preparation to become a computational biologist. The most direct way to do this is to join a research lab that already has datasets you can play with. This might be imaging data, any kind of omics data (genomics, epigenomics, transcriptomics, proteomics) usually obtained by some sequencing approach (DNA-seq, RNA-seq, ATAC-seq), or data about DNA/RNA/protein structure. The variety of data types you might work with is too long to completely list here. Mentoring matters a lot . Join a lab with a supportive graduate student or postdoc skilled in computational methods who can guide your data analysis. This person will save you countless hours banging your head against your MacBook when you are stuck. This person will also be your reference when you apply for a job. Working on this project is where you specialize in certain compbio analyses. This often looks like becoming an expert user of certain Python or R packages designed to parse a specific type of data. You might find this blog by Tommy Tang, a personal hero of mine, helpful for some of your omics data analysis. When you have completed your analysis, put it together into a PowerPoint presentation that tells a story in 30 minutes. You will need to convey the background on your chosen topic of study (Ex. mechanical sensation in developing fruit flies, mechanisms of resistance in gastric cancer), the exact questions/hypotheses you address, the data generated to test your hypotheses, the computational method used and why, any positive or negative findings, the implications of your findings for your field, any caveats in your data or analysis the audience should be aware of, and which experiments or additional analyses you propose to do next. Practice really does make perfect. Get lots of feedback from your research mentor. If joining a research lab is not accessible to you, you might also complete your compbio project as part of an industry internship. For those who are extremely motivated, you could also complete this compbio project on your own free time by analyzing published data. For example, you might find this paper on synovial sarcoma interesting and decide to download the associated data here for your own analysis. 5. Apply and Interview! Once you have your story, you are ready to start applying to compbio roles. This blog post by bitsinbio does a good job of broaching the variety of compbio roles; it is written for PhD-holders, but its content is helpful for job seekers at any stage in their education. In your job search, you should be aware that there is a type of computational biologist for every flavor of biology. For example, a compbio role for evolutionary biology will share very few technical requirements with a compbio role for protein structure modeling, even though they may be advertised under the same job title. So read the job description closely to find out the skills required. Lots of nuances between compbio roles make it difficult for the hiring manager to identify the right candidate, so the more intentional candidate will be more successful in landing interviews. If the data types and compbio analyses you specialized in for your compbio project are a match for the job description, you may get invited for interviews, which will typically involve 1) giving a short presentation on your project to showcase your scientific critical thinking abilities and technical skills and 2) one-on-one interviews with the hiring manager and your potential teammates to assess fit. Most compbio jobs in the Boston area are hybrid; some WFH flexibility is the norm. Currently, the base salary for these industry compbio jobs are roughly $75-90K out of college , $80-110K out of a master’s , and $110-150K out of PhD/postdoc . The Broad Institute also hires many computational biologists, but their salaries are lower compared to their industry counterparts. And there you have it! I hope that this general guide provides a bit more clarity to what it takes to work in computational biology and dispels some myths about entering this field. You don’t need to have years and years of advanced biology, statistics, computer science, and math training to begin meaningful contributions as a computational biologist. I currently work as a computational biologist in Cambridge, MA. I am always open to connect with aspiring computational biologists at any stage in your education, so don’t hesitate to message me on LinkedIn . About the Author: Dean started his graduate training in neuroscience (GSAS ’18) studying the molecular rules directing the developing mammalian cortex. But he decided to change course to computational biology as he witnessed the data revolution in the life sciences being accelerated by next-generation sequencing technologies. He now queries this data to guide immuno-oncology drug development in biotech/pharma. He thinks a lot about how scientists grow professionally and the organizational ingredients that enable scientists to realize their full positive impact on human health. 183 IMAGES Ph.D. Programme in Computational Biology Molecular & Computational Biology phd program What is Computational Biology? Computational Biology Phd Programs PPT IMSC Computational Biology PhD Program 2020 VIDEO COMPUTATIONAL BIOLOGY II ASSIGNMENT Integrative Biology PhD Defense Integrative Biology PhD Defense Introduction to computational Biology Computational Biology and Cancer Research at Dana-Farber CSAIL Computational Biology Lab Tour COMMENTS Computational Biology PhD Teaching Requirements. Computational Biology PhD students are required to teach at least two semesters (starting with Fall 2019 class), but may teach more. The requirement can be modified if the student has funding that does not allow teaching. Starting with the Fall 2019 class: At least one of those courses should require that you teach a section. Computational Biology The Ph.D. program in Computational Biology draws on course offerings from the disciplines of the Center's Core faculty members. These areas are Applied Mathematics, Computer Science, the Division of Biology and Medicine, the Center for Biomedical Informatics, and the School of Public Health. Our faculty and Director of Graduate Studies work ... Ph.D. in Computational Biology and Bioinformatics The mission of the Graduate Program in Computational Biology and Bioinformatics (CBB) is to train predoctoral students to become leaders at the interdisciplinary intersection of quantitative and biomedical sciences. ... Graduate School Application Requirements See the Application Instructions page for important details about each Graduate ... Computational Biology Designated Emphasis Requirements Curriculum/Coursework. Please refer to the DE page on the CCB website for the most up-to-date requirements and information.. 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The graduate program in Computational and Systems Biology trains the next generation of scientists in technology-intensive, quantitative, systems-level approaches to molecular biology. As technological changes generate exponentially larger amounts of data, the scale of the biological questions under ... Ph.D. programs in Computational Biology at JHU All students in the Biostatistics Ph.D. program have to complete the core requirements: A two-year sequence on biostatistical methodology (140.751-756) ... The Hopkins Biology Graduate Program, founded in 1876, is the oldest Biology graduate school in the country. ... Students in computational biology can use their electives to take more ... Ph.D. Program Requirements QCB Graduate Program Requirements. See QCB Student Handbook for program details.. Core courses . 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Genomics and Computational Biology, PhD Genomics and computational biology are now at the center of biomedical research. These disciplines take a holistic approach to ask about the origins, functions, and interactions of whole systems, using both experimental and theoretical work. Therefore, these studies require knowledge, skills, and, most importantly, synthesis and integration of ... Bioinformatics and Computational Biology PhD Course Requirements The Ph.D. program in the Bioinformatics and Computational Biology subprogram inherits all course requirements of the Informatics Ph.D. program; that is, a total of 72 semester hours (37 semester hours of coursework) beyond the bachelor's degree, consisting of 21 semester hours in core coursework Bioinformatics & Computational Biology Modern biology is being greatly enriched by an infusion of ideas from computational and mathematical fields, including computer science, information science, mathematics, operations research and statistics. In turn, biological problems are motivating innovations in these computational sciences. There is a high demand for scientists who can ... Bioinformatics and Computational Biology (PhD) Researchers in the field of bioinformatics and computational biology collect, store, analyze, and present complex biological data using high-performance computing. Through this work, critical contributions are made to disease detection, drug design, forensics, agriculture, and environmental sciences. This research-oriented program trains a new ... Computational Biology Methods This domain emphasis will prepare students for work or graduate school in bioinformatics or computational biology. Students with this emphasis will be able to understand how computational methods are used to elucidate the mechanisms of cellular processing of genetic data and will prepare them for computational analyses of DNA and other molecular biological data. PhD Requirements PhD Requirements. Requirement Course Number (Semester Offered) Course Name Ph.D. BCB core courses: BCB 567 (Alt S) BCB 568 (S) BCB 570 (F) ... 72 course credits required to obtain PhD Degree in Bioinformatics and Computational Biology . 11/10/2023. Bioinformatics & Computational Biology 2014 Molecular Biology Building Ames IA 50011 How to Become an Industry Computational Biologist in a Year About the Author: Dean started his graduate training in neuroscience (GSAS '18) studying the molecular rules directing the developing mammalian cortex. But he decided to change course to computational biology as he witnessed the data revolution in the life sciences being accelerated by next-generation sequencing technologies. assignment essay homework paper phd report resume review speech thesis