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Mike Langston

Professor

UT


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.


Education

  • PhD: Texas A & M University

Publications

"Identifying Genetic Loci and Spleen Gene Coexpression Networks Underlying Immunophenotypes in BXD Recombinant Inbred Mice," Physiological Genomics 41 (2010), 244-253, with E. J. Chesler, S. Das, S. A. Kania, R. M. Lynch, S. Naswa, G. L. Rogers, A. M. Saxton and B. H. Voy.

"Ontological Discovery Environment: A System for Integrating Gene-Phenotype Associations,” Genomics94 (2009), 377-387, with E. J. Baker, E. J. Chesler, J. J. Jay; R. Kirova, Z. Li, V. M. Philip, and Y. Zhang.

“Threshold Selection in Gene Co-Expression Networks Using Spectral Graph Theory Techniques,” BMC Bioinformatics10 (2009), with A. D. Perkins.

“A Systems Genetic Analysis of Chronic Fatigue Syndrome: Combinatorial Data Integration from SNPs to Differential Diagnosis of Disease,” in Methods of Microarray Data Analysis VI (J. Cuticchia and S. M. Lin, editors), CreateSpace Publishing, 2009, 81-98, with E. J. Chesler, R. Kirova, X. Peng and A. D. Perkins.

“Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation,” The Computer Journal51 (2008), 26-38, with A. D. Perkins, A. M. Saxton, J. A. Scharff and B. H. Voy.

“Combinatorial and Algorithmic Issues for Microarray Analysis,” in Approximation Algorithms and Metaheuristics (T. F. Gonzales, editor), Taylor & Francis, 2007, 74.1-74.14, with C. Cotta and P. Moscato.

"Extracting Gene Networks for Low Dose Radiation using Graph Theoretical Algorithms," PLoS Computational Biology 2 (2006), e89, with B. R. Borate, L. K. Branstetter, E. J. Chesler, A. D. Perkins, A. M. Saxton, J. A. Scharff and B. H. Voy.

“Scalable Parallel Algorithms for FPT Problems,” Algorithmica 45 (2006), 269-284, with F. N. Abu-Khzam, P. Shanbhag and C. T. Symons.

“Complex Trait Analysis of Gene Expression Uncovers Polygenic and Pleiotropic Networks that Modulate Nervous System Function,” Nature Genetics37 (2005), 233-242, with N. E. Baldwin, E. J. Chesler, J. Gu, J. B. Hogenesch, H. C. Hsu, L. Lu,  K. F. Manly, J. D. Mountz, Y. Qu,  S. Shou, D. W. Threadgill, J. Wang and R. W. Williams.

“Computational, Integrative and Comparative Methods for the Elucidation of Genetic Co-Expression Networks,” Journal of Biomedicine and Biotechnology2 (2005) 172-180, with N. E. Baldwin, E. J. Chesler, S. Kirov, J. R. Snoddy, R. W. Williams and B. Zhang.


Contact Information

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