Computational Biology deals with computational problems arising from
biological systems. This is an active area of research at the
Computer Science Department.
Richard Bonneau's research focusses is centered on two main areas:
1) developing computational and mathematical solutions to problems
arising from functional genomeics
or high throughput biological datasets and 2) deriving functional
information from structure predictions and further development and
implementation of Rosetta.
Bud Mishra's research in computational biology focuses on
computational genomics. He has developed optical mapping technology,
a successful approach to physical genome mapping.
He aims to develop efficient practical algorithmic tools for genome
mapping and sequencing, based on a high level programming
language designed for this purpose.
Dennis Shasha is involved in three projects related to computational
biology: the discovery of transcription factor networks,
functional genomics and
pattern matching. The first project
aims to study natural analog circuits, consisting of special
type of proteins (transcription factors) that regulate
production of other proteins. Functional genomics aims to
identify similar genes in different species, which requires
very fast algorithms for sequence comparison. Approximate
pattern matching is essential for finding correlations in
biological data, as they are often obtained from experiments
without perfect repeatability. The goal of Prof. Shasha's
research is to produce algorithms and data structures that
allow rapid approximate search across a variety of structural
The work in Professor Schlick's group focuses on determining the
structural and dynamical properties of macromolecules by computer
simulation. Computational techniques are critically needed to link the
static structural information on proteins and nucleic acids obtained
by X-ray crystallography and nuclear magnetic resonance with
the wide range of biological activity and reactivity in the realistic
environment of the cell. In this goal, reliable numerical tools and
simulation protocols are critical. Needed are a variety of models for
spatial and temporal resolution of complex molecular systems, from the
quantum-mechanical to molecular mechanics treatments. The rapidly
advancing technology of faster computers, larger memories, and
parallel-processing environments also offers new opportunities for
theoretical advances and hence the impact of the field of structural
biology/biophysics as a whole.
De novo structure prediction using Rosetta, structure-function relationship,
regulatory network inference, biological pathway reconstruction, biclustering
of large functional genomics datasets, data integration and visualization.
Computational biology, including algorithms for
identifying DNA associated with cancers, and computational aspects of
the optical mapping approach to the Human Genome project.
Computational game theory. Robotics. Computational algebra.
information exploration for difficult data, data mining for
biology and finance.
Mathematical biology, molecular modeling, molecular dynamics, numerical methods,
DNA and protein structure.
Computational Math and Chemistry
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