Michael R. Brent, PhD

Professor
Computer Science and Engineering
Genetics
Biomedical Engineering

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

  • 4515 McKinley (at Taylor), Rm# 4307

  • brent@wustl.edu

  • http://mblab.wustl.edu

  • computational biology, systems biology, regulatory circuits, computational genomics, multi-omics integration, human genetics

  • Computational genomics, human genetics, systems biology, transcriptional regulatory networks

Research Abstract:

The Brent lab is focused on computational genomics and regulatory systems biology, with applications to human genetics and infectious disease.


In computational genomics, we develop methods for integrating multiple “omics” modalities, such as genome sequences, gene expression profiles, metabolomics, and methylomics. The goal of these methods is to synthesize the data in way that yields insights into the genetic causes of health-related phenotypes and their biological mechanisms. Machine learning techniques are playing an increasingly important role, both in our work and in this area of research. We are applying these methods as part of the Long Life Family Study consortium, which aims to identify genetic variants that contribute to long life and long health.


In regulatory systems biology, we develop methods for mapping transcriptional regulatory networks – that is, figuring out which transcription factors (TFs) regulate each gene in an organism. Inputs include data on changes in gene expression in response to TF perturbations, data on TF binding locations, and genome sequence. We are mapping TF networks in human, yeast (a model Eukaryote), and Cryptococcus neoformans, an opportunistic pathogen that causes fatal meningitis in people whose immune systems are not working well.

Our work includes both computational methods development and molecular data generation. We welcome trainees who are interested in computational methods development, data analysis, molecular experiments, and any combination of these.

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: 11/16/2022 11:43:16 AM

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