PMID:11536134
| Citation |
Srivastava, R, Peterson, MS and Bentley, WE (2001) Stochastic kinetic analysis of the Escherichia coli stress circuit using sigma(32)-targeted antisense. Biotechnol. Bioeng. 75:120-9 |
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| Abstract |
A stochastic Petri net model was developed for simulating the sigma(32) stress circuit in E. coli. Transcription factor sigma(32) is the principal regulator of the response of E. coli to heat shock. Stochastic Petri net (SPN) models are well suited for kinetics characterization of fluxes in biochemical pathways. Notably, there exists a one-to-one mapping of model tokens and places to molecules of particular species. Our model was validated against experiments in which ethanol (inducer of heat shock response) and sigma(32)-targeted antisense (downward regulator) were used to perturb the sigma(32) regulatory pathway. The model was also extended to simulate the effects of recombinant protein production. Results show that the stress response depends heavily on the partitioning of sigma(32) within the cell; that is, sigma(32) becomes immediately available to mediate a stress response because it exists primarily in a sequestered, inactive form, complexed with chaperones DnaK, DnaJ, and GrpE. Recombinant proteins, however, also compete for chaperone proteins, particularly when folded improperly. Our simulations indicate that when the expression of recombinant protein has a low requirement for DnaK, DnaJ, and GrpE, the overall sigma(32) levels may drop, but the level of heat shock proteins will increase. Conversely, when the overexpressed recombinant protein has a strong requirement for the chaperones, a severe response is predicted. Interestingly, both cases were observed experimentally. |
| Links | |
| Keywords |
Antisense Elements (Genetics); Computer Simulation; Escherichia coli/genetics; Escherichia coli/metabolism; Ethanol; Gene Expression Regulation, Bacterial; Heat-Shock Proteins/genetics; Heat-Shock Proteins/metabolism; Models, Biological; Recombinant Proteins/genetics; Sigma Factor/genetics; Sigma Factor/metabolism; Solvents; Stochastic Processes |
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