|
Background
|
Dramatic growth of biological data including genome sequences, gene expression data, large-scale
protein interaction screens, and experimentally determined 3D
structures of biological macromolecules (DNAs, RNAs, proteins) and their complexes has led to
the increasing need for efficient computational
approaches. A full understanding of these large data-sets aided by
computational methods, will accelerate the path to biological
discoveries, provide a number of new diagnostic and treatment tools, and initiate genomic person-specific medicine development.
|
|
Goals
|
Within the broad research area of computational biology I intend to
develop a versatile research program linking structural biology with genome research focusing on: (1) understanding of folding, (2) interactions, and (3) design of bio-molecules with desired
properties and (4) genome-wide analysis and inter-genome comparison of bio-molecules and their functions leading to better understanding of life.
|
|
Methods
|
One of the fundamental approaches for combinatorial problem solving,
the randomized search paradigm underlies many of the best-performing
algorithms for solving difficult computational problems from many
problem domains. To elucidate the above problems, my research will
employ the following computational methods: (1) efficient randomized
search methods; (2) machine learning techniques for optimization; and
(3) application of graph-theoretical and computational geometry
methods. I will focus on three tightly related methodological goals: obtaining an improved understanding of search behavior and performance, developing better search algorithms, and applying advanced search methods to problems in bioinformatics relevant to human health. In addition, I intend to forge collaborative research interests with biologists and biophysicists.
|