Bioinformatics & Computational Biology

Undergraduate Courses

BB 1003. Exploring Bioinformatics and Computational Biology

Cat I (offered at least 1x per Year).
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.
Recommended Background: High school biology. Programming experience is not required.

BB 3010. Simulation in Biology

Cat II (offered at least every other Year).
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 technique called agent-based modeling (ABM) to simulate biological systems. Most of the classroom time will be spent working individually or in groups, first learning a language (either the block-based language Starlogo Nova, or the text-based language Netlogo), and then creating 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 academic years ending in even numbers.
Recommended Background: Students taking this course should have background in a biological area they would like to simulate, at about the depth provided by a BB 2000 or 3000 level class. No programming experience is assumed. 

BB 4001. Bioinformatics

Cat II (offered at least every other Year).
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 academic years ending in odd numbers.
Recommended Background: A working knowledge of concepts in genetics and molecular biology (BB 2920 and BB 3950 or equivalent), and statistics (MA 2610 or MA 2611 or equivalent)

BCB 1003. Exploring Bioinformatics and Computational Biology

BCB 100X. EXPLORING BIOINFORMATICS AND COMPUTATIONAL BIOLOGY

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, including such areas as complex disease genetics, analysis of a flu epidemic, investigating antibiotic resistance, and understanding the behavior of swarms, such as schooling fish. Computational tools explored will include both freely-available web-based tools as well as guided programming using Python.
Recommended background: High school biology. Programming experience is not required.

BCB 3010. Simulation in Biology

BCB 4001. Bioinformatics

BCB 4004. Statistical Methods in Genetics and Bioinformatics

Cat II (offered at least every other Year).
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 academic years ending in even numbers.
Recommended Background: MA 2612, MA 2631 (or MA 2621), and BB 2920 or more biology courses.

MA 4603. Statistical Methods in Genetics and Bioinformatics

Graduate Courses

BB 581. Bioinformatics

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This course will provide an overview of bioinformatics, covering a broad selection of the most important techniques used to analyze biological sequence and expression data. Students will acquire a working knowledge of bioinformatics applications through hands-on use of software to ask and answer biological questions. In addition, the course will provide students with an introduction to the theory behind some of the most important algorithms used to analyze sequence data (for example, alignment algorithms and the use of hidden Markov models). Topics covered will include protein and DNA sequence alignments, evolutionary analysis and phylogenetic trees, obtaining protein secondary structure from sequence, and analysis of gene expression including clustering methods. Students may not receive credit for both BB 581 and BB 4801.

BCB 501. Bioinformatics

BCB 502. Biovisualization

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This course uses interactive visualization to explore and analyze data, structures, and processes. Topics include the fundamental principles, concepts, and techniques of visualization and how visualization can be used to analyze and communicate data in domains such as biology. Students will be expected to design and implement visualizations to experiment with different visual mappings and data types, and will complete a research oriented project.

BCB 503. Biological and Biomedical Database Mining

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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.

BCB 504. Statistical Methods in Genetics and Bioinformatics

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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. Students may not receive credit for both BCB 4004 and BCB 504.

BCB 510. BCB Seminar

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This seminar provides an opportunity for students in the BCB program to present their research work, as well as hear research talks from guest speakers.

BCB 555. Journal Club in Quantitative Cell Biology

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This course is offered every other semester, discussing topics on quantitative cell biology that advance our understanding of the function of cellular systems. The focus is on reading, presenting, and discussing the most recent literature in the field. Graduate students and advanced undergraduate students with an interest in quantitative biology are encouraged to participate. Topics covered change with each offering of this course and students may register and receive credit for more than one offering.

BCB 590. Special Topics in Bioinformatics and Computational Biology

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An offering of this course will cover a topic of current interest in detail. See the Supplement section of the online catalog at www.wpi. edu/+gradcat for descriptions of courses to be offered in this academic year. Prerequisites will vary with topic.

BCB 5900. Graduate Internship

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A graduate internship is carried out in cooperation with a sponsor or industrial partner. It must be overseen by a faculty member affiliated with the Bioinformatics and Computational Biology Program. The internship will involve development and practice of technical and professional skills and knowledge relevant to Bioinformatics and Computational Biology. At the completion of the internship, the student will produce a written report, and will present their work to BCB faculty and internship sponsors.

BCB 596. Independent Study

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This course will allow a student to study a chosen topic in Bioinformatics and Computational Biology under the guidance of a faculty member affiliated with the Bioinformatics and Computational Biology program. The student must produce a written report at the conclusion of the independent study.

BCB 597. Directed Research

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Directed research conducted under the guidance of a faculty member affiliated with the BCB Program.

BCB 599. M.S. Thesis Research

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A Masters thesis in Bioinformatics and Computational Biology consists of a research and development project worth a minimum of 9 graduate credit hours advised by a faculty member affiliated with the BCB Program. A thesis proposal must be approved by the BCB Program Review Board and the students advisor before the student can register for more than three thesis credits. The student must satisfactorily complete a written thesis document, and present the results to the BCB faculty in a public presentation.

BCB 699. Ph.D. Dissertation Research

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A Ph.D. thesis in Bioinformatics and Computational Biology consists of a research and development project worth a minimum of 30 graduate credit hours advised by a faculty member affiliated with the BCB Program. Students must pass a qualifying exam before the student can register for Ph.D. thesis credits. The student must satisfactorily complete a written dissertation, and defend it in a public presentation and a private defense.