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 Initiative and the Brown Center for Biomedical Informatics.
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.
Standard program for the A.B. degree
Please see the bottom of the page for more information regarding the University Writing Requirement, the Capstone Experience, and the Computational Biology Honors Program.
| Prerequisites: | 2 | |
| Single Variable Calculus, Part II | ||
or MATH 0170 | Single Variable Calculus, Part II (Accelerated) | |
| 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 | 2-3 | |
| Choose one of the following groupings of introductory courses: | ||
Group A | ||
| Computing Foundations: Data and Computing Foundations: Program Organization and Program Design with Data Structures and Algorithms 1 | ||
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 any full-credit computer science course above CSCI 0190) | ||
| General Core Requirements: Probability & Statistics | 1 | |
| Statistical Inference I | ||
OR | ||
| Advanced Introduction to Probability for Computing and Data Science | ||
OR | ||
| Probability | ||
| Comp Bio Core Course Requirements | 4 | |
| Computational Molecular Biology | ||
| Inference in Genomics and Molecular Biology | ||
AND two of the following: | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Human Population Genomics | ||
| Methods in Informatics and Data Science for Health | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
Additional course with Director approval | ||
| Capstone Experience | ||
Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision (i.e: BIOL 1950/1960, CSCI 1970, APMA 1970). 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. | ||
| Total Credits | 12-13 | |
Standard program for the Sc.B. degree
Please see the bottom of the page for more information regarding the University Writing Requirement, the Capstone Experience, and the Computational Biology Honors Program.
| Prerequisites | 2 | |
| Single Variable Calculus, Part II (or equivalent) | ||
or MATH 0170 | Single Variable Calculus, Part II (Accelerated) | |
| 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: Chemisty | 1 | |
| Equilibrium, Rate, and Structure | ||
or CHEM 0350 | Organic Chemistry I | |
| General Core Requirements: Computer Science | 3-4 | |
| Introduction to Discrete Structures and Probability | ||
| AND complete one of the following groupings of introductory courses: | ||
Group A | ||
| Computing Foundations: Data and Computing Foundations: Program Organization and Program Design with Data Structures and Algorithms 1 | ||
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 any full-credit computer science course above CSCI 0190.) | ||
| General Core Requirements: Probability & Statistics | 1 | |
| Statistical Inference I | ||
or CSCI 1450 | Advanced Introduction to Probability for Computing and Data Science | |
or MATH 1210 | Probability | |
| General Core Requirements: Computational Biology | ||
| CSCI 1810 | Computational Molecular Biology | 1 |
| APMA 1080 | Inference in Genomics and Molecular Biology | 1 |
| Capstone Experience | 1 | |
| Students enrolled in the computational biology concentration will complete a research project in their senior year under faculty supervision (i.e: BIOL 1950/1960, CSCI 1970, APMA 1970). 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. | ||
| Six Courses in one of the following 3 tracks | 6 | |
| Computer Science Track: | ||
Three of the following: | ||
| Introduction to Computer Graphics | ||
| Database Management Systems | ||
| Artificial Intelligence | ||
| Machine Learning | ||
| Deep Learning | ||
| Probabilistic Methods in Computer Science | ||
| Design and Analysis of Algorithms | ||
or other Computer Science courses approved by the concentration advisor. | ||
AND three of the following: | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Human Population Genomics | ||
| Introduction to Software Engineering and Introduction to Computer Systems | ||
| Algorithmic Foundations of Computational Biology | ||
| Statistical Methods in Bioinformatics, I | ||
| Biological Sciences track | ||
At least four courses comprising a coherent theme in one of the following areas: Biochemistry, Ecology, Evolution, or Neurobiology. | ||
AND two courses from the following: | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Human Population Genomics | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
| Statistical Methods in Bioinformatics, I | ||
| Applied Mathematics & Statistics Track: | ||
At least three courses from the following: | ||
| Statistical Inference II | ||
| Computational Probability and Statistics | ||
| Artificial Intelligence | ||
| Methods of Applied Mathematics I and Methods of Applied Mathematics II | ||
| Applied Ordinary Differential Equations and Applied Partial Differential Equations I | ||
At least three of the following: | ||
| Quantitative Models of Biological Systems | ||
| Foundations of Population Genetics | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Human Population Genomics | ||
| Machine Learning | ||
| Deep Learning | ||
| Algorithmic Foundations of Computational Biology | ||
| Statistical Methods in Bioinformatics, I | ||
| Total Credits | 18-19 | |
University Writing Requirement:
As part of Brown’s writing requirement, all students must demonstrate that they have worked on their writing both in their general studies and their concentration. There are a number of ways for Computational Biology concentrators to fulfill these requirements:
- Enrolling in an independent study: CSCI 1970, BIOL 1950, APMA 1970
- Writing an Honors Thesis
- Taking a “WRIT” course in the final two years
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, with no "C's" in concentration courses and with a minimum of an "A-" average in concentration courses. In addition, students should take at least one semester, and are strongly encouraged to take 2 semesters, of reading and research with a CCMB faculty member or approved advisor. Students must submit to a public defense of their theses to be open to the CCMB community.
- Students seeking honors are advised to choose a Thesis Advisor prior to the end of their Junior year
- Students must complete the Registration form for Comp Bio and submit it to CCMB@BROWN.EDU
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/)