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
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.
Carla Gomes works on solutions to hard combinatorial problems, with an emphasis on planning and scheduling problems, combining techniques fromm Computer Science (CS), Artificial Intelligence (AI), and Operations Reserach (OR). Her research is leading to the creation of the new field of computational sustainability, which develops and applies computational methods to enable a sustainable environment, economy and society.
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interest include applications of pattern matching and shape matching to ares bioinformatics, computer vision, and historical document analysis.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.
Alon Keinan studies how human genetic variation has arisen from evolutionary history. His research focuses on elucidating the history of modern human populations and on developing computational methods for searching for genes important in human biology. With a background in computer science and statistics, Keinan develops theoretical tools and applies them to genomic data sets, bridging theoretical population genetics and empirical studies.
Jon Kleinberg 's research focuses on algorithmic issues at the interface of networks and information. 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.
A new graduate program in Computational Molecular Biology that crosses colleges was initiated with the participation of the computer science field.