The prime intellectual mission of Brown University’s Center for Computational Molecular Biology (CCMB) is to promote the development, implementation and application of analytical and computational methods to foundational questions in the biological and medical sciences. The research programs of the Core Faculty in CCMB lie fundamentally at the intersection of computer science, evolutionary biology, mathematics, and molecular and cellular biology.
Biological questions that currently unite the CCMB Core and Associate Faculty are: How do genotypes and genes interact to produce phenotypes, and how does this happen from womb to tomb? What drives the formation, maintenance and evolutionary transformations of communities of organisms over time? Quantitative questions that currently unite the CCMB faculty are: how can we design powerful algorithms to make sense of the sea of data produced in the genomic era? What principles are required for a theoretical framework to completely model cellular systems?
The research challenges at the heart of CCMB are a rich source of mathematical problems motivated by the complex nature of genomes, disease processes and evolutionary relationships. These challenges are both multi-scale (with units of interest ranging from molecules to communities of organisms) and large-scale (data-intensive, due to advances in sequencing technologies). Thus, CCMB rounds out the broader landscape of research in methodological development at Brown University by partnering with and complementing the Data Science Institute.
In addition to these research interests, CCMB Faculty members are actively involved in the operation of Brown’s NIH-funded COBRE Center for the Computational Biology of Human Disease, and administer both an undergraduate concentration and an interdisciplinary doctoral program in Computational Biology. This is a short video about our Ph.D. program in Computational Biology.
Computational Biology
Computational biology involves the analysis and discovery of biological phenomena using computational tools, and the algorithmic design and analysis of such tools. The field is widely defined and includes foundations in computer science, applied mathematics, statistics, biochemistry, molecular biology, genetics, ecology, evolution, anatomy, neuroscience, and visualization.
Students may pursue a Bachelor of Arts or a Bachelor of Science. Students pursuing the ScB have the option of electing a concentration in Computational Biology with one of three focus areas: Computer Sciences, Biological Sciences, or Applied Mathematics & Statistics. Both programs require a senior capstone experience that pairs students and faculty in creative research collaborations.
Due to the interdisciplinary nature of the Computational Biology field, courses for the concentration are drawn from a variety of departments across campus. Students should carefully review the prerequisites for courses that they are interested in and should consult the Bulletin footnotes for any clarifications. Questions about the Bulletin or the concentration may be directed to ccmb@brown.edu.
| Prerequisites (0-3 courses) | ||
| These prerequisites are widely required for many courses in the concentration. Students must either complete or place out of these prerequisites. | ||
| Single Variable Calculus, Part II | ||
or MATH 0170 | Single Variable Calculus, Part II (Accelerated) | |
| Linear Algebra and Multivariable Calculus for Applied Mathematicians | ||
or MATH 0180 | Multivariable Calculus | |
or MATH 0200 | Multivariable Calculus (Physics/Engineering) | |
or MATH 0350 | Multivariable Calculus With Theory | |
| The Foundation of Living Systems | ||
| General Core Requirements: Biology | 2 | |
| Genetics | ||
| Biochemistry | ||
or BIOL 0500 | Cell and Molecular Biology | |
| General Core Requirements: Chemistry | 1 | |
| Equilibrium, Rate, and Structure | ||
or CHEM 0350 | Organic Chemistry I | |
| General Core Requirements: Computer Science | 2 | |
| Choose one of the following groupings of introductory courses: | ||
Group A | ||
| Computing Foundations: Data and Program Design with Data Structures and Algorithms | ||
Group B | ||
| Introduction to Object-Oriented Programming and Computer Science and Program Design with Data Structures and Algorithms | ||
Group C | ||
| Computer Science: An Integrated Introduction and Program Design with Data Structures and Algorithms | ||
Group D | ||
| Accelerated Introduction to Computer Science and Program Design with Data Structures and Algorithms (or any full-credit computer science course above CSCI 0190) | ||
| General Core Requirements: Probability & Statistics | 1 | |
| Introduction to Probability and Statistics with Theory 1 | ||
or CSCI 1450 | Advanced Introduction to Probability for Computing and Data Science | |
or MATH 1210 | Probability | |
| Comp Bio Core Course Requirements | 2 | |
| Computational Molecular Biology | ||
| Inference in Genomics and Molecular Biology 1 | ||
| Computational Biology Electives | 2 | |
| Select two of the following electives. Note: Students should review any pre-requisites for these electives in C@B. | ||
| Quantitative Models of Biological Systems | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Current Topics in Functional Genomics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Pathogenomics: Analysis, interpretation and applications of microbial genomes | ||
| Methods in Informatics and Data Science for Health | ||
| Evaluation of Health Information Systems | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
| Principles of Biostatistics and Data Analysis | ||
| Using R for Data Analysis | ||
or other 1000+ level Computational Biology-related course with concentration advisor approval. | ||
| Capstone Experience | 1 | |
| Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision. The themes of such projects evolve with the field and the technology, but should represent a synthesis of the various specialties of the program. The requirements are either: | ||
2. A 2000-level Computational Biology course that covers an advanced topic within the Computational Biology field and includes an advanced research component. 3 | ||
| Total Credits | 11 | |
- 1
APMA 1655 has replaced APMA 1650 as the prerequisite for APMA 1080. In Fall 2026, students who have already taken APMA 1650 may request an override for APMA 1080. Starting in Fall 2027, students must have taken APMA 1655 (or APMA 1650 and the APMA 1655 bridgework exam) in order to register for APMA 1080.
- 2
A list of CCMB faculty can be found on the CCMB website. If a student's research advisor is not a CCMB faculty member, they should request approval from the Director of Undergraduate Studies or the program administrator.
- 3
Some 2000-level courses are not available to undergraduate students due to departmental restrictions but have 1000-level equivalents (such as BIOL 1575/2575) that can count for capstone credit with approval from the instructor and the student's faculty advisor. Please reach out to ccmb@brown.edu with any questions.
Capstone Experience:
Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision. The themes of such projects evolve with the field and the technology, but should represent a synthesis of the various specialties of the program. The requirements are either one semester of reading and research with a CCMB Faculty member or approved advisor, or a 2000-level Computational Biology course.
In order to be considered a candidate for honors, students will be expected to maintain an outstanding record. Students must have a majority of either As or S with distinction grades in concentration courses. In addition, students should take at least one semester, and are strongly encouraged to take 2 semesters semesters, of reading and research with a CCMB faculty member or approved advisor.
Students seeking honors are advised to choose a Thesis Advisor prior to the end of their Junior year. Students must complete the Comp Bio Honors Registration form and submit their honors proposal to ccmb@brown.edu by the first Friday in October of their senior year. Students must submit a honors thesis in April of their senior year and present a public defense of their theses to the CCMB community. More information about the honors guidelines and deadlines can be found here. Any deviation from these rules must be approved by the director of undergraduate studies, in consultation with the student's advisor.
Computational biology involves the analysis and discovery of biological phenomena using computational tools, and the algorithmic design and analysis of such tools. The field is widely defined and includes foundations in computer science, applied mathematics, statistics, biochemistry, molecular biology, genetics, ecology, evolution, anatomy, neuroscience, and visualization.
Students may pursue a Bachelor of Arts or a Bachelor of Science. Students pursuing the ScB have the option of electing a concentration in Computational Biology with one of three focus areas: Computer Sciences, Biological Sciences, or Applied Mathematics & Statistics. Both programs require a senior capstone experience that pairs students and faculty in creative research collaborations.
Due to the interdisciplinary nature of the Computational Biology field, courses for the concentration are drawn from a variety of departments across campus. Students should carefully review the prerequisites for courses that they are interested in and should consult the Bulletin footnotes for any clarifications. Questions about the Bulletin or the concentration may be directed to ccmb@brown.edu.
| Prerequisites (0-3 courses) | ||
| These prerequisites are widely required for many courses in the concentration. Students must either complete or place out of these prerequisites. | ||
| Single Variable Calculus, Part II (or equivalent) | ||
or MATH 0170 | Single Variable Calculus, Part II (Accelerated) | |
| Linear Algebra and Multivariable Calculus for Applied Mathematicians | ||
or MATH 0180 | Multivariable Calculus | |
or MATH 0200 | Multivariable Calculus (Physics/Engineering) | |
or MATH 0350 | Multivariable Calculus With Theory | |
| The Foundation of Living Systems (or equivalent) | ||
| General Core Course Requirements: Biology | 2 | |
| Genetics (prerequisite BIOL 0200 or equivalent) | ||
| Biochemistry | ||
or BIOL 0500 | Cell and Molecular Biology | |
| General Core Requirements: Chemistry | 1 | |
| Equilibrium, Rate, and Structure | ||
or CHEM 0350 | Organic Chemistry I | |
| General Core Requirements: Computer Science | 3 | |
| Introduction to Discrete Structures and Probability | ||
| AND complete one of the following groupings of introductory courses: | ||
| Group A | ||
| Computing Foundations: Data and Program Design with Data Structures and Algorithms | ||
| Group B | ||
| Introduction to Object-Oriented Programming and Computer Science and Program Design with Data Structures and Algorithms | ||
| Group C | ||
| Computer Science: An Integrated Introduction and Program Design with Data Structures and Algorithms | ||
| Group D | ||
| Accelerated Introduction to Computer Science and Program Design with Data Structures and Algorithms (or any full-credit computer science course above CSCI 0190) | ||
| General Core Requirements: Probability & Statistics | 1 | |
| Introduction to Probability and Statistics with Theory 1 | ||
or CSCI 1450 | Advanced Introduction to Probability for Computing and Data Science | |
or MATH 1210 | Probability | |
| General Core Requirements: Computational Biology | 2 | |
| Inference in Genomics and Molecular Biology 1 | ||
| Computational Molecular Biology | ||
| Six Courses in One Track | 6 | |
| Choose one of 3 tracks: Computer Science, Biological Sciences, or Applied Mathematics and Statistics. See track requirements below. | ||
| Capstone Experience | 1 | |
| Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision. The themes of such projects evolve with the field and the technology, but should represent a synthesis of the various specialties of the program. The requirements are either: | ||
2. A 2000-level Computational Biology course that covers an advanced topic within the Computational Biology field and includes an advanced research component. 3 | ||
| Total Credits | 16 | |
- 1
APMA 1655 has replaced APMA 1650 as the prerequisite for APMA 1080. In Fall 2026, students who have already taken APMA 1650 may request an override for APMA 1080. Starting in Fall 2027, students must have taken APMA 1655 (or APMA 1650 and the APMA 1655 bridgework exam) in order to register for APMA 1080.
- 2
A list of CCMB faculty can be found on the CCMB website. If a student's research advisor is not a CCMB faculty member, they should request approval from the Director of Undergraduate Studies or the program administrator.
- 3
Some 2000-level courses are not available to undergraduate students due to departmental restrictions but have 1000-level equivalents (such as BIOL 1575/2575) that can count for capstone credit with approval from the instructor and the student's faculty advisor. Please reach out to ccmb@brown.edu with any questions.
Tracks
Please review the prerequisites required for the courses below in CAB. Students should also be aware of the requirements for enrolling in a given CSCI course, which can be found on the Computer Science website.
| Computer Science Track: | ||
| Three of the following: | 3 | |
| Foundations of AI and Machine Learning 1 | ||
| Introduction to Computer Graphics | ||
| Database Management Systems | ||
| Machine Learning | ||
| Computer Vision | ||
| Computational Linguistics | ||
| Deep Learning | ||
| Probabilistic Methods in Computer Science | ||
| Design and Analysis of Algorithms | ||
| Design and Implementation of Programming Languages | ||
or other 1000+ level Computer Science course approved by the concentration advisor. | ||
| Three of the following: | 3 | |
| Quantitative Models of Biological Systems | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Pathogenomics: Analysis, interpretation and applications of microbial genomes | ||
| Methods in Informatics and Data Science for Health | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Computational Cognitive Neuroscience | ||
| Deep Learning in Brains, Minds and Machines | ||
| Algorithmic Foundations of Computational Biology | ||
| Principles of Biostatistics and Data Analysis | ||
| Using R for Data Analysis | ||
or another 1000+ level computational course approved by the concentration advisor. | ||
| Total Credits | 6 | |
- 1
CSCI 0410 is the undergraduate offering for CSCI 1411. CSCI 0410 and CSCI 1411 share the same course staff, lectures, discussions, and assignments
| Biological Sciences track | ||
| At least four 1000+ level courses comprising a coherent theme related to Computational Biology. Examples of themes include: Biochemistry, Ecology, Evolution, Genomics, Immunology, or Neurobiology. Other themes can be approved by your concentration advisor. | 4 | |
| And two of the following electives: | 2 | |
| Quantitative Models of Biological Systems | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Current Topics in Functional Genomics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Pathogenomics: Analysis, interpretation and applications of microbial genomes | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Deep Learning in Brains, Minds and Machines | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
| Principles of Biostatistics and Data Analysis | ||
| Using R for Data Analysis | ||
or other 1000+ level Computational Biology-related course approved by concentration advisor. | ||
| Total Credits | 6 | |
Please review the prerequisites required for the courses below in CAB and on APMA’s website and/or Public Health’s website.
| Applied Mathematics & Statistics Track: | ||
| At least three from the following: | 3 | |
| Applied Ordinary Differential Equations with Theory and Applied Partial Differential Equations I with Theory 1 | ||
| Quantitative Models of Biological Systems | ||
| Introduction to Computational Linear Algebra | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Recent Applications of Probability and Statistics | ||
| Mathematical Statistics | ||
| Principles of Biostatistics and Data Analysis | ||
| Applied Regression Analysis | ||
| Using R for Data Analysis | ||
or other 1000+ level APMA or STAT course approved by concentration advisor. | ||
| At least three of the following: | 3 | |
| Current Topics in Functional Genomics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Methods in Informatics and Data Science for Health | ||
| Foundations of AI and Machine Learning 2 | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
or other 1000+ level Computational Biology-related course approved by concentration advisor. | ||
| Total Credits | 6 | |
- 1
Students must take both courses in this set (APMA 0355 & APMA 0365) to fulfill one elective requirement within this track.
- 2
CSCI 0410 is now the undergraduate offering for CSCI 1411. CSCI 0410 and CSCI 1411 share the same course staff, lectures, discussions, and assignments.
Capstone Experience:
Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision. The themes of such projects evolve with the field and the technology, but should represent a synthesis of the various specialties of the program. The requirements are either one semester of reading and research with a CCMB Faculty member or approved advisor, or a 2000-level Computational Biology course.
Honors:
In order to be considered a candidate for honors, students will be expected to maintain an outstanding record. Students must have a majority of either As or S with distinction grades in concentration courses. In addition, students should take at least one semester, and are strongly encouraged to take 2 semesters semesters, of reading and research with a CCMB faculty member or approved advisor.
Students seeking honors are advised to choose a Thesis Advisor prior to the end of their Junior year. Students must complete the Comp Bio Honors Registration form and submit their honors proposal to ccmb@brown.edu by the first Friday in October of their senior year. Students must submit a honors thesis in April of their senior year and present a public defense of their theses to the CCMB community. More information about the honors guidelines and deadlines can be found here. Any deviation from these rules must be approved by the director of undergraduate studies, in consultation with the student's advisor.
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 (APMA), Computer Science (CS), the Division of Biology and Medicine (BioMed), Brown Center for Biomedical Informatics (BCBI), and the School of Public Health/Biostats (SPH). Our faculty and Director of Graduate Studies (DGS) 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 PhD 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.
- Ph.D. Program Overview & Handbook (pdf file)
- FAQ
The application process to the CCMB graduate program is run through the Graduate School (http://www.brown.edu/academics/gradschool/)