Michael R. Brent

Michael R. Brent received the B.S. degree in mathematics and Ph.D. degree in computer science both from Massachusetts Institute of Technology, in 1985 and 1991, respectively. After completing his Ph.D., Professor Brent served as an 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 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. His research is in systems biology. 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.

Biography Updated on 10 March 2008

Personal Home Page

http://www.engineering.wustl.edu/facultybio.aspx?faculty=124&department=3

Articles in Scholarly Journals [Incomplete List]

  1. Steady progress and recent breakthroughs in the accuracy of automated genome annotation
    Nature Reviews Genetics, vol. 9, no. 1, Article ID nrg2220, 11 pages, 2008
  2. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project
    Nature, vol. 447, no. 7146, Article ID nature05874, 17 pages, 2007
  3. How does eukaryotic gene prediction work?
    Nature Biotechnology, vol. 25, no. 8, Article ID nbt0807-883, 2 pages, 2007
  4. Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping
    Nature Methods, vol. 4, no. 8, Article ID nmeth1063, 5 pages, 2007
  5. The Treeterbi and Parallel Treeterbi algorithms: efficient, optimal decoding for ordinary, generalized and pair HMMs
    Bioinformatics, vol. 23, no. 5, pp. 545–554, 2007
  6. A tale of two templates: Automatically resolving double traces has many applications, including efficient PCR-based elucidation of alternative splices
    Genome Research, vol. 17, no. 2, pp. 212–218, 2007
  7. Targeted discovery of novel human exons by comparative genomics
    Genome Research, vol. 17, no. 12, pp. 1763–1773, 2007
  8. BMC Bioinformatics, vol. 7, no. 1, p. 327, 2006
  9. Molecular Properties of Adult Mouse Gastric and Intestinal Epithelial Progenitors in Their Niches
    Journal of Biological Chemistry, vol. 281, no. 16, pp. 11292–11300, 2006
  10. Using Multiple Alignments to Improve Gene Prediction
    Journal of Computational Biology, vol. 13, no. 2, pp. 379–393, 2006
  11. Iterative gene prediction and pseudogene removal improves genome annotation
    Genome Research, vol. 16, no. 5, pp. 678–685, 2006
  12. Closing in on the C. elegans ORFeome by cloning TWINSCAN predictions
    Genome Research, vol. 15, no. 4, pp. 577–582, 2005
  13. Begin at the beginning: Predicting genes with 5' UTRs
    Genome Research, vol. 15, no. 5, pp. 742–747, 2005
  14. Genome annotation past, present, and future: How to define an ORF at each locus
    Genome Research, vol. 15, no. 12, pp. 1777–1786, 2005
  15. BMC Bioinformatics, vol. 6, no. 1, p. 131, 2005
  16. The Genome of the Basidiomycetous Yeast and Human Pathogen Cryptococcus neoformans
    Science, vol. 307, no. 5713, pp. 1321–1324, 2005
  17. The ENCODE (ENCyclopedia Of DNA Elements) Project
    Science, vol. 306, no. 5696, pp. 636–640, 2004
  18. Gene prediction and verification in a compact genome with numerous small introns
    Genome Research, vol. 14, no. 11, pp. 2330–2335, 2004
  19. Reexamining the Vocabulary Spurt.
    Developmental Psychology, vol. 40, no. 4, Article ID 2004-15557-013, 11 pages, 2004
  20. Recent advances in gene structure prediction
    Current Opinion in Structural Biology, vol. 14, no. 3, pp. 264–272, 2004
  21. Comparison of mouse and human genomes followed by experimental verification yields an estimated 1,019 additional genes
    Proceedings of the National Academy of Sciences, vol. 100, no. 3, pp. 1140–1145, 2003
  22. The DNA sequence of human chromosome 7
    Nature, vol. 424, no. 6945, Article ID nature01782, 7 pages, 2003
  23. BMC Bioinformatics, vol. 4, no. 1, p. 50, 2003
  24. Leveraging the Mouse Genome for Gene Prediction in Human: From Whole-Genome Shotgun Reads to a Global Synteny Map
    Genome Research, vol. 13, no. 1, pp. 46–54, 2003
  25. The Genome Sequence of Caenorhabditis briggsae: A Platform for Comparative Genomics
    PLoS Biology, vol. 1, no. 2, p. e5, 2003
  26. Initial sequencing and comparative analysis of the mouse genome
    Nature, vol. 420, no. 6915, Article ID nature01262, 42 pages, 2002
  27. Predicting full-length transcripts
    Trends in Biotechnology, vol. 20, no. 7, pp. 273–275, 2002
  28. The role of exposure to isolated words in early vocabulary development
    Cognition, vol. 81, no. 2, pp. B33–B44, 2001
  29. Speech segmentation and word discovery: a computational perspective
    Trends in Cognitive Sciences, vol. 3, no. 8, pp. 294–301, 1999
  30. Machine Learning, vol. 34, no. 1/3, pp. 71–105, 1999
  31. On the discovery of novel wordlike units from utterances: An artificial-language study with implications for native-language acquisition.
    Journal of Experimental Psychology: General, vol. 128, no. 2, Article ID 1999-05245-003, 20 pages, 1999
  32. Syntactic categorization in early language acquisition: formalizing the role of distributional analysis
    Cognition, vol. 63, no. 2, pp. 121–170, 1997
  33. Journal of Psycholinguistic Research, vol. 26, no. 3, pp. 363–375, 1997
  34. Distributional regularity and phonotactic constraints are useful for segmentation
    Cognition, vol. 61, no. 1-2, pp. 93–125, 1996
  35. Advances in the computational study of language acquisition
    Cognition, vol. 61, no. 1-2, pp. 1–38, 1996