Mike Langston

Keywords:

Computational genomics, discrete optimization, graph algorithms, high-performance platforms, genome-scale implementations, microarray analysis, pathway homology, regulatory network analysis.

Research Area:

Analysis of high-throughput proteomic, transcriptomic and other sorts of -omic data.

Description of Research:

Professor Langston's research is focused novel combinatorial optimization methods and their application in the analysis of high-throughput biological data. He and his team of students labor to unravel regulatory messages hidden in proteomic, transcriptomic and other sorts of -omic data. In the context of microarray data, for example, graph algorithms, genome-scale implementations and high-performance platforms are used in the genetic analysis of gene transcription. With them it is possible to address foundational questions such as: “are the sets of genes co-expressed under one type of conditions the same as those sets co-expressed under another?” Innovative algorithms for the extraction of dense subgraphs derived from correlation matrices help identify which, among a set of candidate genes, are the most likely regulators of trait variation. We seek to discover large groups of genetically co-expressed genes, and to extrapolate the consequences of this genetic variation on phenotypes observed across many levels of biological scale. This approach is furthermore applied to definitions of homology-based gene sets, and the incorporation of categorical data such as known gene pathways. In all these tasks discrete mathematics and combinatorial algorithms form organizing principles upon which methods and implementations are based.

Contact Information

Mike Langston
Bioinformatics
Professor, Department of Computer Science

UT
203 Claxton Complex
1122 Volunteer Blvd
Knoxville, TN 37996
(865) 974-3534

Email: langston@cs.utk.edu

Degrees

PhD: Texas A & M University