Master of Science
Bioinformatics and Computational Biology
Faculty with Research Interests
S. Walcott, Professor, Mathematical Sciences, and BCB Program Director; Ph.D., Cornell University, 2006. Systems biology, molecular modeling, mathematical biology.
A. Manning, Associate Professor, Biology and Biotechnology, and BCB Graduate Coordinator; Ph.D., Geisel School of Medicine at Dartmouth University, 2008. Cancer Cell Biology, cell cycle regulation, mitotic progression and chromosome segregation, chromatin regulation, and genome stability.
E. F. Ryder, Professor, Biology and Biotechnology, and BCB Undergraduate Coordinator; Ph.D., Harvard University, 1993. Computational biology, simulation of biological systems, high school curriculum design integrating biology and computer science.
A. Arnold, Associate Professor, Mathematical Sciences; Ph.D., Case Western University, 2014. Mathematical biology, Bayesian inference, parameter estimation in biological systems.
N. Farny, Assistant Professor, Biology and Biotechnology; Ph.D., Harvard University, 2009. Synthetic biology, cellular biology; biosensors, bioremediation, cell responses to environmental stress.
D. Korkin, Professor, Computer Science; Ph.D., University of New Brunswick, Canada, 2003. Bioinformatics of disease, biomedical data-analytics, computational multi-omics, systems biology, structural bioinformatics, data mining, machine learning.
C. Ruiz, Professor, Computer Science; Ph.D., University of Maryland, 1996. Data mining, machine learning, artificial intelligence, biomedical data mining.
B. Servatius, Professor, Mathematical Sciences; Ph.D., Syracuse University, 1987. Combinatorics, matroid and graph theory, geometry and motions of molecules, protein folding.
L. Vidali, Professor, Biology and Biotechnology; Ph.D., University of Massachusetts-Amherst. Plant cell biology and molecular genetics, live cell microscopy, molecular motors/cytoskeleton.
E. M. Young, Associate Professor, Chemical Engineering; Ph.D., University of Texas at Austin. Synthetic biology, metabolic pathway engineering, yeast gene expression, transport protein engineering.
Affiliated Faculty with Research Interests
E.O. Agu, (Computer Science); Ph.D., University of Massachusetts-Amherst, 2001. Mobile Health, Medical Imaging, Ubiquitous computing, Machine learning, Smartphone and wearables as platforms to deliver better healthcare.
J. B. Duffy, (Biology and Biotechnology); Ph.D., University of Texas. Signal transduction dynamics and modeling, computational identification of intracellular protein motifs.
M. Eltabakh, (Computer Science); Ph.D., Purdue University, 2010. Database management systems, information management.
L. Harrison, Associate Professor, Computer Science; Ph.D., UNC-Charlotte, 2013. Information visualization, visual analytics, human-computer interaction.
A. Mattson, (Chemistry and Biochemistry), Ph.D. Northwestern University: drug design, molecular modeling.
S. G. McInally, (Biology and Biotechnology); Ph.D. University of California, Davis, 2019. Quantitative cell biology, cytoskeletal dynamics, size control, mathematical modeling, microscopy.
J. Mortensen, (Computer Science); Ph.D., Yale University, 2018. Computational Biology, simulations of biological systems, physics of living systems.
B. Nephew, (Biology and Biotechnology); Ph.D. Tufts University, 2003. Neurobehavioral mechanisms of mental illness, fMRI, neural correlates of mindfulness, machine learning based early predictors of severe depression and suicidality.
S. D. Olson, (Mathematical Sciences); Ph.D. North Carolina State University, 2008. Mathematical biology, chemical signaling, mechanics, and hydrodynamics.
R. Paffenroth, (Mathematical Sciences); Ph.D., University of Maryland, 1999. Large scale data analytics, statistical machine learning, compressed sensing, network analysis.
R. Prusty Rao, (Biology and Biotechnology); Ph.D., Penn State University-Medical School. Genomic studies and high throughput screening to understand and manage fungal diseases in humans.
E. A. Rundensteiner, (Computer Science); Ph.D., University of California, Irvine, 1992. Machine Learning for Health Care, data and information management, big data analytics, visual data discovery, fair and explainable AI.
S. Scarlata, (Chemistry and Biochemistry); Ph.D., University of Illinois, Urbana-Champaign, 1985. G proteins, signaling pathways, small molecules controlling cellular behavior.
S. Shell, (Biology and Biotechnology); Ph.D., University of California San Diego. Bacterial pathogenesis, bacterial stress response, prokaryotic gene regulation, prokaryotic genomics and transcriptomics.
E. T. Solovey, (Computer Science); Ph.D., Tufts University, 2012. Human-computer interaction, user interface design, novel interaction modalities, human-autonomy collaboration, machine learning.
J. Srinivasan, (Biology and Biotechnology); Ph.D., University of Tuebingen, Germany. Genetics, behavioral neuroscience, molecular neurobiology, chemical biology, evolutionary ecology.
D. Tang, (Mathematical Sciences); Ph.D., University of Wisconsin, 1988. Biofluids, biosolids, blood flow, mathematical modeling, numerical methods, scientific computing, nonlinear analysis, computational fluid dynamics.
Q. Wen, (Physics); Ph.D., Brown University, 2007. Experimental biophysics; mechanical interaction between cells and ECM, signal transduction, control of cell morphology, migration, and differentiation.
M. Wu, (Mathematical Sciences); Ph.D., University of California, Irvine, 2012. Mathematical biology, modeling of living systems.
Z. Wu, (Mathematical Sciences); Ph.D., Yale, 2009. Biostatistics, high-dimensional model selection, linear and generalized linear modeling, statistical genetics, bioinformatics.
J. Zou, (Mathematical Sciences); Ph.D., University of Connecticut, 2009. Financial time series (especially high frequency financial data), spatial statistics, biosurveillance, high dimensional statistical inference, Bayesian statistics.
Programs of Study
The Bioinformatics and Computational Biology (BCB) Program offers graduate studies toward the B.S./M.S., M.S., and Ph.D. degrees. 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 working at the interface of biology, computer science, and mathematics. A truly interdisciplinary program, the BCB degree requires advanced course work in all three of these areas. Our faculty and strong relationships with the University of Massachusetts Medical School provide students with the resources to perform innovative scientific research at the highest level.
The diverse learning environment that characterizes our program promotes easy exchange of ideas, access to all the necessary resources, and encourages creative solutions to pressing scientific questions.
Admissions Requirements
Students applying to the M.S. or Ph.D. Degree Programs in Bioinformatics and Computational Biology (BCB) are expected to have a bachelor’s degree in either biology, computer science, mathematics, or a related field, and to have taken introductory courses in each of the three disciplines: biology, computer science, and mathematics. For example, a student with a bachelor’s degree in biology is expected to have also completed courses in programming, data structures, calculus, and statistics prior to submitting an application. A strong applicant who is missing background in one of the three areas may be provisionally admitted, with the expectation that he or she will take and pass one or more undergraduate courses in the area of deficiency either during the summer prior to admission or within the first semester after admission. The determination of what course or courses will satisfy this provision will be made by the Program Review Committee.
Certificate Requirements
A certificate program in BCB is not offered at present.
Facilities/Research Labs/Research Centers
The BCB Program is supported by a wide assortment of resources within the participating departments, WPI Computing and Communication Center (CCC), and the research laboratories at Gateway Park and UMMS. Grid and cloud computing, along with high-speed networking, provides exceptional computational infrastructure. Access to most major biological databases is available to students and researchers, and a wide range of bioinformatics software packages are installed and maintained. Wet labs at Gateway Park and UMMS are available by permission of BCB faculty members and affiliates.
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M.S. in Bioinformatics and Computational Biology, -
Ph.D. in Bioinformatics and Computational Biology, Ph.D.
Classes
BCB 501/BB 581: Bioinformatics
Credits
3.0
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 BCB 4001 and BCB 501.
Prerequisites
knowledge of genetics, molecular biology, and statistics at the undergraduate level
BCB 502/CS 582: Biovisualization
Credits
3.0
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.
Prerequisites
experience with programming (especially JavaScript), databases, and data structures. Students may not receive credit for both BCB 502 and BCB 4002.
BCB 503/CS 583: Biological and Biomedical Database Mining
Credits
3.0
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.
Prerequisites
Strong programming skills, an undergraduate or graduate course in algorithms, an undergraduate course in statistics, and one or more undergraduate biology courses
BCB 504/MA 584: Statistical Methods in Genetics and Bioinformatics
Credits
3.0
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.
Prerequisites
knowledge of probability and statistics at the undergraduate level
BCB 510: BCB Seminar
Department
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
Department
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.
BCB 590: Special Topics in Bioinformatics and Computational Biology
Department
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 596: Independent Study
Department
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
Department
Directed research conducted under the guidance of a faculty member affiliated with the BCB Program.
BCB 599: M.S. Thesis Research
Department
A Master’s 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 student’s 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
Department
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.
BCB 5900: Graduate Internship
Department
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.