Computational Biology

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The recent completion of the human genome project underlines the need for new computational and theoretical tools in modern biology. The tools are essential for analyzing, understanding and manipulating the detailed information on life we now have at our disposal.

Problems in computational molecular biology vary from understanding sequence data to the analysis of protein shapes, prediction of biological function, study of gene networks, and cell-wide computations.

Cornell has a university-wide plan in the science of genomics; the Department of Computer Science is playing a critical role in this initiative. Researchers in the computer science department are engaged in a wide range of computational biology projects, from genetic mapping, to advanced sequence analysis, fold prediction, structure comparison algorithms, protein classification, comparative genomics, and long-time simulation of protein molecules.

Faculty and Researchers

Paul Chew 's primary interest is in geometric algorithms with an emphasis on practical applications, such as placement, motion planning, shape comparison, vision, sensing, and mesh generation. In computational biology Paul has been working on algorithms for matching and identifying structural similarities in proteins. Together with Prof. Klara Kedem, Prof. Jon Kleinberg and Prof. Dan Huttenlocher he developed a new algorithm for structure comparison, URMS, which is an efficient and accurate measure of protein structure similarity.

Dan Huttenlocher 's research focus is on computer vision. Specifically he is working on problems of model-based recognition, geometric shape comparison, and the computation of visual correspondence, and the application of computer vision techniques for analysis of protein shapes.

Klara Kedem 's research area is Computational Geometry. Her current research interest include applications of pattern matching and shape matching to ares bioinformatics, computer vision, and historical document analysis. With colleagues she discovered the minimum Hausdorff distance for image comparison, and developed a new metric, the URMS, to compare protein shapes. Recent extensions to this method, based on geometric dynamic programming, were applied to compute consensus shapes for protein families. At Ben-Gurion University Klara studied dendrite shapes with colleagues from the Life Sciences Dept. and collaborates with the Chemistry Dept. on finding similarities between conformational polymorphs. Recently she has been working on searching structural RNA motifs in large databases.

Uri Keich is working on statistical and algorithmic problems that arise in amino acid and DNA sequences analysis. He worked on finding motifs or highly preserved sub-sequences which are presumably of biological importance. This problem poses an algorithmic challenge as well as a statistical one: classical statistical approximations break down when applied to analyzing the statistical significance of a detected motif. While Uri worked on both aspects of the problem his current research focus is mostly on designing computational statistical tools that would be robust enough to correctly handle the extreme values one typically encounters in this context. Some of these techniques are brought to bear in a recently established collaboration with biologist Bik Tye studying replication initiation in strains of yeast.

Jon Kleinberg 's research is concerned with the design of efficient algorithms for combinatorial problems. In the area of computational biology, he has worked on algorithms for the construction of comparative genomic maps (jointly with Prof. Susan McCouch from Plant Breeding and Ph.D. student Debra Goldberg). He is also studying sequence-structure relationships in proteins and the `connectivity' of protein families via mutations in sequence space (joint work with Prof. Ron Elber).

David Shmoys is studying approximate algorithms for genetic linkage mapping (identifying the locations of markers on the genome) to reduce the cost of wet lab experiments and improve the accuracy of the resulting maps.

Adam Siepel Adam Siepel has worked on various problems in computational biology, including the detection of recombinant viruses, the reconstruction of evolutionary histories based on genome rearrangements, and the integration of heterogeneous bioinformatics software tools. His most recent work has been in comparative genomics, particularly of mammals, and has included a mixture of statistical modeling, algorithms development, software implementation, and scientific discovery. Adam likes to tackle problems of practical importance in genomics, such as gene finding and conserved element identification, using methods from machine learning and statistics. He is an active participant in several large-scale comparative genomics projects, including the Mammalian Gene Collection project, the ENCODE project, and the Rhesus Macaque Sequencing and Analysis Consortium.

Collaborations outside the department

Another key element of the computational biology initiative is the Computational Genomics Institute that combines faculty from all over campus who do active research in computational biology and statistical genomics.
Computational biology is also a crucial part of the recently announced $160M collaboration between Cornell and the Rockefeller and Sloan-Kettering institutes. The computer science department plays an important role in establishing the collaboration and enhancing the intellectual links between the different institutes.

Education

A new graduate program in Computational Molecular Biology that crosses colleges was initiated with the participation of the computer science field. A concentration in Computational Biology for undergraduate students majoring in computer science was also established.

Researchers

Paul Chew
Dan Huttenlocher
Klara Kedem
Uri Keich
Jon Kleinberg
David Shmoys
Adam Siepel

Programs

General information
Graduate program in Computational Biology
Undergraduate program in Computational Biology
Tri-institutional Program in Computational Biology and Medicine