DIRECTOR: D. KORKIN (CS)
ASSOCIATE DIRECTOR: E. RYDER (BB)
PROGRAM COMMITTEE: A. Arnold (MA), L. Harrison (CS), A. Manning (BB), S. Olson (MA), R. Paffenroth (MA), R. Rao (BB), C. Ruiz (CS), B. Servatius (MA), S. Shell (BB), L. Vidali (BB), M. Wu (MA), Z. Wu (MA), E. Young (CHE)
AFFILIATED FACULTY: E. Agu (CS), T. Dominko (BB), M.Y. Eltabakh (CS), W.J. Martin (MA), A. Mattson (CBC), E.A. Rundensteiner (CS), E. Solovey (CS), J. Srinivasan (BB), D. Tang (MA), S. Walcott (MA), A. Yousefi (CS), J. Zou (MA)
With the advent of large amounts of biological data stemming from research efforts such as the Human Genome Project, there is a great need for professionals who can work at the interface of biology, computer science, and mathematics to address important problems involving complex biological systems. Graduates of this interdisciplinary program will be well versed in all three disciplines, typically specializing in one of them. Many opportunities for interdisciplinary research projects are available, both on the WPI campus, and through relationships with faculty at the University of Massachusetts Medical School. Graduates will be well-prepared for graduate study or for professional careers in industry.
Students graduating with a Bachelor of Science degree in Bioinformatics and Computational Biology:
- Have mastered foundational studies in biology, mathematics, and computer science
- Have mastered advanced principles and techniques in at least one of the three disciplines
- Can apply computational and mathematical knowledge to the solution of biological problems
- Can communicate effectively across disciplines both verbally and in writing
- Can locate, read, and interpret primary literature in bioinformatics and computational biology
- Can formulate hypotheses or models, design experiments to test these hypotheses, and interpret experimental data
- Can function effectively as members of an interdisciplinary team
- Adhere to accepted standards of ethical and professional behavior
- Will be life-long independent learners
Bioinformatics and Computational Biology Major,Bachelor of Science
Life scientists are generating huge amounts of data on many different scales, from DNA and protein sequence, to information on biological systems such as protein interaction networks, brain circuitry, and ecosystems. Analyzing these kinds of data requires quantitative knowledge and approaches using computer science and mathematics. In this project-based course, students will use case studies to learn about both important biological problems and the computational tools and algorithms used to study them. Students will study a sampling of topics in the field; recent topics included complex disease genetics, HIV evolution, antibiotic resistance, and animal migration behavior. In addition, students will hear from several guest speakers about their interdisciplinary research. Computational tools explored will include both freely-available tools to analyze sequences and build phylogenetic trees (e.g. BLAST, MUSCLE, MEGA) as well as guided programming using languages such as Python, R, and Netlogo. Students may not receive credit for both BCB / BB 100X and BCB / BB 1003. BBT majors may count this course as fulfilling part of their quantitative science and engineering requirement, but not as part of their BB 1000 level course requirement.
High school biology. Programming experience is not required.
Computer simulations are becoming increasingly important in understanding and predicting the behavior of a wide variety of biological systems, ranging from metastasis of cancer cells, to spread of disease in an epidemic, to management of natural resources such as fisheries and forests. In this course, students will learn to use a graphical programming language to simulate biological systems. Most of the classroom time will be spent working individually or in groups, first learning the language, and then programming simulation projects. We will also discuss several papers on biological simulations from the primary scientific literature. In constructing and comparing their simulations, students will demonstrate for themselves how relatively simple behavioral rules followed by individual molecules, cells, or organisms can result in complex system behaviors. This course will be offered in 2022-23, and in alternating years thereafter.
Students taking this course must have a solid background in a biological area they would like to simulate, at about the depth provided by a BB 3000 level class. No programming experience is assumed.
In an age when the amount of new biological data generated each year is exploding, it has become essential to use bioinformatics tools to explore biological questions. This class will provide an understanding of how we organize, catalog, analyze, and compare biological data across whole genomes, covering a broad selection of important databases and techniques. Students will acquire a working knowledge of bioinformatics applications through hands-on use of software to ask and answer biological questions in such areas as genetic sequence and protein structure comparisons, phylogenetic tree analysis, and gene expression and biological pathway analysis. In addition, the course will provide students with an introduction to some of the theory underlying the software (for example, how alignments are made and scored). This course will be offered in 2022-23, and in alternating years thereafter.
A working knowledge of concepts in genetics and molecular biology (BB2920 and BB2950 or equivalent), and statistics (MA 2610 or MA2611 or equivalent)
This course will use interactive visualization to model and analyze biological information, structures, and processes. Topics will include the fundamental principles, concepts, and techniques of visualization (both scientific and information visualization) and how visualization can be used to study bioinformatics data at the genomic, cellular, molecular, organism, and population levels. Students will be expected to write small- to moderately-sized programs to experiment with different visual mappings and data types. This course will be offered in 2022-23, and in alternating years thereafter.
CS 2102 or CS 2103, CS 2223, and one or more biology courses.
This course will investigate computational techniques for discovering patterns in and across complex biological and biomedical sources including genomic and proteomic databases, clinical databases, digital libraries of scientific articles, and ontologies. Techniques covered will be drawn from several areas including sequence mining, statistical natural language processing and text mining, and data mining. This course will be offered in 2021-22, and in alternating years thereafter.
CS 2102 or CS 2103, CS 2223, MA 2610 or MA 2611, and one or more biology courses.
This course provides students with knowledge and understanding of the applications of statistics in modern genetics and bioinformatics. The course generally covers population genetics, genetic epidemiology, and statistical models in bioinformatics. Specific topics include meiosis modeling, stochastic models for recombination, linkage and association studies (parametric vs. nonparametric models, family-based vs. population-based models) for mapping genes of qualitative and quantitative traits, gene expression data analysis, DNA and protein sequence analysis, and molecular evolution. Statistical approaches include log-likelihood ratio tests, score tests, generalized linear models, EM algorithm, Markov chain Monte Carlo, hidden Markov model, and classification and regression trees. This course will be offered in 2021-22, and in alternating years thereafter.
MA 2612, MA 2631 (or MA 2621), and BB 2920 or more biology courses.