Literature DB >> 11536134

Stochastic kinetic analysis of the Escherichia coli stress circuit using sigma(32)-targeted antisense.

R Srivastava1, M S Peterson, W E Bentley.   

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. Copyright 2001 John Wiley & Sons, Inc.

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Year:  2001        PMID: 11536134     DOI: 10.1002/bit.1171

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  11 in total

1.  The effect of coupled stochastic processes in a two-state biochemical switch.

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2.  Global transcriptome response of recombinant Escherichia coli to heat-shock and dual heat-shock recombinant protein induction.

Authors:  Sarah W Harcum; Fu'ad T Haddadin
Journal:  J Ind Microbiol Biotechnol       Date:  2006-05-06       Impact factor: 3.346

3.  A quantitative model of the switch cycle of an archaeal flagellar motor and its sensory control.

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Journal:  Biophys J       Date:  2005-10       Impact factor: 4.033

4.  Mathematical modeling of the eukaryotic heat-shock response: dynamics of the hsp70 promoter.

Authors:  Theodore R Rieger; Richard I Morimoto; Vassily Hatzimanikatis
Journal:  Biophys J       Date:  2004-12-30       Impact factor: 4.033

Review 5.  Convergence of molecular, modeling, and systems approaches for an understanding of the Escherichia coli heat shock response.

Authors:  Eric Guisbert; Takashi Yura; Virgil A Rhodius; Carol A Gross
Journal:  Microbiol Mol Biol Rev       Date:  2008-09       Impact factor: 11.056

6.  A stochastic model of Escherichia coli AI-2 quorum signal circuit reveals alternative synthesis pathways.

Authors:  Jun Li; Liang Wang; Yoshifumi Hashimoto; Chen-Yu Tsao; Thomas K Wood; James J Valdes; Evanghelos Zafiriou; William E Bentley
Journal:  Mol Syst Biol       Date:  2006-12-12       Impact factor: 11.429

7.  Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.

Authors:  Fei Liu; Monika Heiner; Ming Yang
Journal:  PLoS One       Date:  2016-02-24       Impact factor: 3.240

8.  A multiscale approximation in a heat shock response model of E. coli.

Authors:  Hye-Won Kang
Journal:  BMC Syst Biol       Date:  2012-11-21

9.  Modularization of biochemical networks based on classification of Petri net t-invariants.

Authors:  Eva Grafahrend-Belau; Falk Schreiber; Monika Heiner; Andrea Sackmann; Björn H Junker; Stefanie Grunwald; Astrid Speer; Katja Winder; Ina Koch
Journal:  BMC Bioinformatics       Date:  2008-02-08       Impact factor: 3.169

10.  Data-driven dynamical model indicates that the heat shock response in Chlamydomonas reinhardtii is tailored to handle natural temperature variation.

Authors:  Stefano Magni; Antonella Succurro; Alexander Skupin; Oliver Ebenhöh
Journal:  J R Soc Interface       Date:  2018-05       Impact factor: 4.118

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