Research Areas - Computational Molecular Biology in the Computer Science Department at Carnegie Mellon

CSD faculty: Ziv Bar-Joseph, Jaime Carbonell, Dannie Durand (Bio), Michael Erdmann, Chris Langmead, Russell Schwartz (Bio), Eric Xing (ML/LTI)

Computational molecular biology is an active area of research at Carnegie Mellon, carried out by faculty members who make this their primary research area as well through collaborations between computational and biological scientists. Areas of emphasis include the application of machine learning and data mining techniques to large biological knowledge bases, biological modeling, computational aspects of high-throughput laboratory methodologies for large-scale, systematic studies of protein structure and function, analysis of biological image data and computational genomics.

Carnegie Mellon University offers educational programs in computational biology through the undergraduate degree in Biological Sciences with a Computational Option or the B.S. Degree in Biological Sciences/Computational Biology, the Masters Program in Computational Biology, and the Merck Graduate Program in Computational Biology and Chemistry . Graduate student applicants wishing to participate in the Merck Computational Biology and Chemistry graduate program at Carnegie Mellon must apply to and be accepted into one of the graduate programs of the participating departments: Biological Sciences, Chemistry and Computer Science .

Pittsburgh is a fertile environment for research combining biology with other disciplines, including activities at centers such as the Center for ALgorithm ADaptation Dissemination and INtegration (ALADDIN) , the Center for Biological Language Modeling, the Center for Light Microscope Imaging and Biotechnology , the Center for the Neural Basis of Cognition , Faculty of Biomedical Engineering , The Pittsburgh NMR Center for Biomedical Research , and the Pittsburgh Supercomputing Center .

Below we include a list of the CSD faculty working in Computational Molecular Biology, along with a brief summary of their research interests.

Ziv Bar-Joseph analyzes high throughput biological datasets (especially gene expression and time series gene expression data, and protein-DNA binding data). His work addresses issues ranging from the experimental design level to the systems biology level.

Jaime Carbonell is investigating machine learning and language-technologies inspired methods for predicting 3D protein structure from protein primary sequences, and in automated classification of protein sequences into functional categories.

Dannie Durand studies the evolution of vertebrate genome organization and functional diversity through gene duplication, using combinatorial methods to analyze data obtained from diverse biological data sets via web-based information retrieval techniques.

Michael Erdmann studies problems in protein structure comparison.

Chris Langmead develops algorithms for high-throughput structural biology, accelerating critical steps in determining and modeling protein structures and their dynamics, thereby facilitating faster and better techniques for drug design. Langmead also develops tools for the analysis of large-scale protein and gene expression data. This work will lead to early and better disease diagnosis, as well as the design of targeted therapies against illnesses like cancer.

Russell Schwartz develops mathematical models and algorithms for studying genome variations between members of a species. He also develops methods for modeling self-assembling biological systems.

Eric Xing develops probabilistic inference and learning algorithms for computational biology and statistical genetics, and for generic intelligent systems of a wide range of applications. His works addresses problems ranging from network modeling in systems biology to genetic polymorphisms associated with diseases.