Detailed models are aimed 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. Comparison of the glucose transport system in S. cerevisiae to that in Candida albicans is expected to yield insights into the adaptive value of cerevisiae's complex regulatory system. 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 inferring regulatory networks in organisms whose gene regulation has not been mapped in detail, such as the fungal pathogen Cryptococcus neoformans. We are inferring Dynamic Bayes Net models from data such as genome-wide expression profiles obtained in a time course after changing growth conditions or other external signals. Such experiments can be carried out in combination with high-throughput interventions in the regulatory network by methods such as RNA interference. Data on the binding of transcription factors (e.g. ChIP-Seq) and sequence analysis can also inform these models.

While we are ramping up efforts in systems biology, we continue to carry out the sequence analysis work for which we are known, especially computational prediction and experimental verification of the exon-intron structures of genes.

Selected Publications:
Brent, M.R. 2007. How does eukaryotic gene prediction work? Nat Biotechnol 25: 883-885.

Brent, M.R. 2008. Steady progress and recent breakthroughs in the accuracy of automated genome annotation. Nat Rev Genet 9: 62-73.

Siepel, A., M. Diekhans, B. Brejova, L. Langton, M. Stevens, C.L. Comstock, C. Davis, B. Ewing, S. Oommen, C. Lau,…M. Brent. 2007. Targeted discovery of novel human exons by comparative genomics. Genome Res 17: 1763-1773.

Keibler, E., M. Arumugam, and M. Brent. 2007. The Treeterbi and Parallel Treeterbi algorithms: Efficient, optimal decoding for ordinary, generalized, and Pair HMMs. Bioinformatics 23: 545-554.

Gross, S.S. and M.R. Brent. 2006. Using Multiple Alignments To Improve Gene Prediction. Journal of Computational Biology 13: 379-393.

Last Updated: 06/12/2008