May 25, 2024  
Rensselaer Catalog 2023-2024 
Rensselaer Catalog 2023-2024

Computational Biology

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Director, Undergraduate and Graduate Degree Programs: Chris Bystroff

Program Home Page:

Revolutions in biotechnology and information technology are changing the world. Advances in molecular genetics, coupled with improved capability in robotics, computer science, and other technologies, have made high throughput gene sequencing an integral part of the scientific landscape. High throughput sequencing has produced complete genome sequences of viruses, bacteria, and increasingly complex eukaryotic organisms. The human genome was completed in 2003, comprising 3 billion bases and an estimated 20,000 genes. Today thousands of human genomes and the genomes of thousands of organisms have been sequenced.

The enormous treasure trove of information that the sequence databases and their structural counterparts represent has spawned the creation of computational methods for drug discovery, protein structure prediction, functional inference, molecular evolution, protein design, functional genomics, and metagenomics, among others. Timely advances in computer science have made the storage, organization, mining, modeling, searching, and knowledge extraction of these very large data collections possible.

Undergraduate Program

Rensselaer’s computational biology undergraduate curriculum includes training in mathematics, chemistry, computer science, and physics. At the program’s core are courses in the theory and practice of sequence analysis, including database search algorithms, alignment, molecular evolution, RNA structure, systems dynamics and phylogenetics; and molecular modeling, including comparative modeling of proteins, docking, sequence analysis, molecular dynamics, protein design, and drug design. It allows students to focus on ecological or computational problems in this vast and emerging field. 

Outcomes of the Undergraduate Curriculum

As a result of completing this program students will:

  • demonstrate proficiency in the foundational topics of cell and molecular biology, genetics and evolution, biochemistry, and ecology and the environment.
  • demonstrate additional competency in advanced Biological Sciences topics relative to their academic interest.
  • be able to apply skills such as reading primary literature, developing a testable hypothesis, designing an experiment, collecting and analyzing data, and using statistical and quantitative methods.
  • be able to communicate effectively on scientific topics in both written and oral forms.
  • be able to apply knowledge and skills from across the curriculum to current problems in biological sciences to generate integrative papers, proposals, or other types of projects.

Associated Graduate Programs

The Master of Science in Biology with a concentration in computational biology is available for those desiring an M.S. degree before proceeding to professional work or doctoral programs. It is possible to enter the doctoral programs in Biology, Biochemistry and Biophysics, or Computer Science with a concentration in computational biology.

Research Innovations and Initiatives

There are extensive opportunities for computational biology majors to pursue undergraduate research in faculty laboratories, sometimes adding a computational capacity to experimental labs, and sometimes by serving as a bridge between computational and experimental biology. Several experimental research programs have benefited from undergraduate researchers who are trained in sequence analysis, molecular modeling, and database design. Computational Biology research at Rensselaer includes the design and application of algorithms for sequence database searching, sequence alignment, and sequence analysis, molecular modeling, and allied areas in computational chemistry and simulation of biological processes. Closely related research in molecular genetics and biochemistry provides concrete applications for graduate and undergraduate students.

Research in computational biology is funded by a diverse group of agencies including NIH, NSF, the American Diabetes Association, and NASA. Computational biology research projects range from data mining, machine learning, molecular dynamics, drug discovery, computational biology, chemoinformatics, enzymology, signal transduction, protein engineering, protein folding, protein design, biosensing, systems dynamics, phylogenetics, DNA nanostructure, and studies on environmental adaptations of microorganisms.


Biology: C. Bystroff, S. Gilbert, L. Ligon, D. Swank, B. Barquera, G. Makhatadze, C. Wang, S. Nierzwicki-Bauer, D. Crone, J. Hurley, K. Rose, C. Royer

Computer Science: B. Szymanski, M. Zaki

Chemistry and Chemical Biology: C. Breneman, W. Colon, R. Lindhart, K. Lakshmi, L. McGown, X. Wang, P. Dinolfo

Mathematical Sciences: K. Bennett; P. Drineas, P. Kramer

Chemical and Biological Engineering: J. Dordick, S. Garde, M. Koffas, C. Collins, P. Karande


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