Michael R. Brent, Ph.D.

Computer Science and Engineering
Biomedical Engineering

Computational and Systems Biology Program
Human and Statistical Genetics Program
Molecular Genetics and Genomics Program

  • 314-286-0210

  • 4515 McKinley (at Taylor), Rm# 4307

  • brent@cse.wustl.edu

  • http://mblab.wustl.edu

  • computational biology, systems biology, gene regulation, kinetics, mathematical modeling, regulatory circuits, genomics

  • Systems biology, transcriptional regulatory networks, network inference, yeast

Research Abstract:

The Brent lab is focused on regulatory systems biology using computational and experimental methods. We are interested in modeling networks that regulate cell state on both a detailed, kinetic level and a genome-wide level.

Detailed models are aimed at understanding how the responses of specific gene regulatory networks unfold over time and predicting how their responses will be affected by modifications made through genetic engineering. Currently, our kinetic modeling efforts are focused on understanding the dynamic responses of budding yeasts to fluctuations in extracellular glucose concentration on a quantitative, molecular, and evolutionary basis. The long term goals of this project are (1) to develop validated, integrated kinetic models of regulatory networks using S. cerevisiae as a model organism, and (2) to develop efficient, streamlined methods that will allow such models to be created more easily and on a larger scale in the future.

Our genome-wide modeling efforts are aimed at mapping regulatory networks in poorly mapped organisms such as the fungal pathogen Cryptococcus neoformans. We have developed the current best algorithm for this and are continuing to improve it with the goal of making network mapping as straightforward as expression profiling. Indeed, the primary inputs to our algorithms are genome sequences and gene expression profiles from cells in which transcription factors have been perturbed by deletion from the genome, knockdown, or overexpression.

An important aspect of our approach is applying new methods to real problems as we develop and improve them. Our applications work currently focuses on S. cerevisiae (using data from our own wet lab) and C. neoformans (in collaboration with Tamara Doering`s lab).

Selected Publications:

Kang, Y, Patel, NR, Shively, C, Recio, PS, Chen, X, Wranik, BJ, Kim, G, McIsaac, RS, Mitra, R, Brent, MR. 2020. Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses. Genome Res. doi: 10.1101/gr.259655.119

Kang, Y., Liow, H.H., Maier, E.J. & Brent, M.R. NetProphet 2.0: Mapping Transcription Factor Networks by Exploiting Scalable Data Resources. Bioinformatics 34, 249-257 (2017).

Michael, D.G. et al. Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast. Proc Natl Acad Sci U S A 113, E7428-E7437 (2016).

Brent, M.R. (2016) Past roadblocks and new opportunities in transcription factor network mapping. Trends in Genetics (In press).

Haynes, BC, Maier, EJ, Kramer, MH, Wang, PI, Brown, H, & Brent, MR. (2013). Mapping Functional Transcription Factor Networks from Gene Expression Data. Genome Res. doi: 10.1101/gr.150904.112

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.

Last Updated: 2/26/2020 4:36:28 PM

Transcriptional regulation in response to glucose availability in yeast utilizes conserved regulators AMPK and PKA.
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