Henry Edwin Sever Professor of Engineering
After completing his PhD, Professor Brent served as Assistant and Associate Professor of Cognitive Science at the Johns Hopkins University, where his research focused on computational modeling of how children learn language.
He brought these interests to Washington University in St. Louis in 1999, where he developed a second research program in computational biology and eventually phased out computational linguistics. Since 2001, he has focused on computational and molecular methods for improving the accuracy of genome annotation.
The Brent Lab is focused on modeling the way in which the functional states of cells are determined by the control systems encoded in genomes. Professor Brent and his students construct quantitative models of biological control networks that will enable them to understand their functions and the conditions to which they are adapted. Such models will eventually make it possible to predict how modifications to the networks (i.e. engineering) will affect their behavior. Of particular interest are dynamic properties, such as response times and sensitivity to noise. The methods we use include probabilistic models such as Dynamic Bayes Nets, continuous physical models based on differential equations, and molecular experiments.
Professor Brent has studied the function and evolutionary dynamics of genomes using mathematical and computational sequence analysis. This work has led to significant improvements in genome annotation technology, enhancing the value of genome sequences for the scientific community. In particular, researchers in Professor Brent's lab are developing models that make it possible to accurately predict which regions of a genome are transcribed into pre-messenger RNA, how they are spliced, and which portions of the spliced transcript are translated into protein. One of the primary sources of information they use is the commonalities and differences among the genomes of different organisms. Comparing genome sequences reveals the patterns of evolutionary change since their most recent common ancestor. These patterns contain signatures of the different selective pressures on different components of genes, such as splice sites, coding regions, and translation initiation and termination sites. Ultimately, The Brent Lab would like to gain insight into how the differences between genomic sequences of different species give rise to the observable characteristics of those species. For example, how can differences between the human and chimp genomes help us understand the differences between human and chimp cognition?
Kuttykrishnan S, Sabina J, Langton LL, Johnston M and Brent MR
. A quantitative model of glucose signaling in yeast reveals an incoherent feed forward loop leading to a specific, transient pulse of transcription. Proc Natl Acad Sci U S A
. 2010 Sep 21;107(38):16743-8. Epub 2010 Sep 1.
Haynes BC and Brent MR
. Benchmarking regulatory network reconstruction with GRENDEL. Bioinformatics
2009 25: 801-807.
. How does eukaryotic gene prediction work? Nat Biotechnol
2007 25: 883-885.
. Steady progress and recent breakthroughs in the accuracy of automated genome annotation. Nat Rev Genet
2008 9: 62-73.
Keibler E, Arumugam M and Brent MR
. The Treeterbi and Parallel Treeterbi algorithms: Efficient, optimal decoding for ordinary, generalized, and Pair HMMs. Bioinformatics
2007 23: 545-554.
Linking Gene Regulation to Metabolism.
Improving Xylose Fermentation: A regulatory systems approach.
Capsule regulation and virulence in Cryptococcus neoformans, with Tamara Doering, Washington University.
Unraveling transcriptional networks in Type 2 – Diabetes Mellitus Induced by High Sugar Diets, Children’s Discovery Institute, with Thomas Baranksi Washington University.
Integrated Human Genome Annotation: generation of a reference gene set, NIH, with T. Hubbard of the Sanger Institute.