Research Abstract:
The Brent lab is focused on 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 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.
Selected Publications:
Haynes BC andBrent MR. Benchmarking regulatory network reconstruction with GRENDEL. Bioinformatics 2009 25: 801-807.
Brent MR. How does eukaryotic gene prediction work? Nat Biotechnol 2007 25: 883-885.
Brent MR. 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.
Gross SS and Brent MR. Using Multiple Alignments To Improve Gene Prediction. Journal of Computational Biology 2006 13: 379-393.
Last Updated: 07/31/2009 |