In order to optimally control such functions as cellular growth and proliferation, cells must be able to efficiently respond to a dynamically changing environment. For instance, the metabolic response of a cell to changes in nutrient levels requires transcriptional control of metabolic enzymes. This project will quantitatively explore the response of a well-characterized yeast metabolic network to a dynamically changing environment, and will provide general insights into the response of a gene regulatory network to external perturbations. Since galactose utilization networks are common to many higher eukaryotes, and altered expression of essential metabolic enzymes in humans can lead to disease, these studies will also yield insights into the mechanisms of metabolic pathologies. Using experimental synthetic biology techniques, in combination with computational modeling, we will design and construct increasingly complex gene regulatory networks that control cellular metabolism. These networks will be examined at the single-cell level using customized microfluidic devices, in conjunction with fluorescence microsopy. At each stage we will examine the response of different feedback mechanisms to environmental perturbations. The general hypothesis underlying the specific aims is that gene-regulatory feedback loops are responsible for precisely tuning the metabolic response to nutritional fluctuations.
The first aim will involve studying an isolated metabolic gene without feedback to yield a quantitative understanding of how unregulated networks respond to a changing environment. In the second aim, a negative feedback regulatory module will be constructed and studied to determine how negative feedback can dampen the effects of undesirable nutritional fluctuations. In the third aim, a positive feedback regulatory module will be constructed and studied to determine how positive feedback can increase the sensitivity to environmental nutrient shifts. In the final aim, both the positive and negative feedback modules will be combined to study how the metabolic network responds to environmental fluctuations as a whole. These studies will provide crucial insights into the elaborate mechanisms by which a regulatory network responds to dynamically changing environmental conditions.
|Hossain, Munshi Azad; Claggett, Julia M; Edwards, Samantha R et al. (2016) Posttranscriptional Regulation of Gcr1 Expression and Activity Is Crucial for Metabolic Adjustment in Response to Glucose Availability. Mol Cell 62:346-358|
|Mather, William H; Hasty, Jeff; Tsimring, Lev S et al. (2013) Translational cross talk in gene networks. Biophys J 104:2564-72|
|Selimkhanov, Jangir; Hasty, Jeff; Tsimring, Lev S (2012) Recent advances in single-cell studies of gene regulation. Curr Opin Biotechnol 23:34-40|
|Mather, William H; Hasty, Jeff; Tsimring, Lev S (2012) Fast stochastic algorithm for simulating evolutionary population dynamics. Bioinformatics 28:1230-8|
|Jin, Meng; Errede, Beverly; Behar, Marcelo et al. (2011) Yeast dynamically modify their environment to achieve better mating efficiency. Sci Signal 4:ra54|
|Mather, W H; Hasty, J; Tsimring, L S et al. (2011) Factorized time-dependent distributions for certain multiclass queueing networks and an application to enzymatic processing networks. Queueing Syst 69:313-328|
|Cookson, Natalie A; Mather, William H; Danino, Tal et al. (2011) Queueing up for enzymatic processing: correlated signaling through coupled degradation. Mol Syst Biol 7:561|
|Baumgartner, Bridget L; Bennett, Matthew R; Ferry, Michael et al. (2011) Antagonistic gene transcripts regulate adaptation to new growth environments. Proc Natl Acad Sci U S A 108:21087-92|
|Castro-Longoria, Ernestina; Ferry, Michael; Bartnicki-Garcia, Salomon et al. (2010) Circadian rhythms in Neurospora crassa: dynamics of the clock component frequency visualized using a fluorescent reporter. Fungal Genet Biol 47:332-41|
|Prindle, Arthur; Hasty, Jeff (2010) Biochemistry. Stochastic emergence of groupthink. Science 328:987-8|
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