Dataset Information

A comparative kinetic and thermodynamic perspective of the ?-competition model in Escherichia coli.

ABSTRACT: Transcription is the most fundamental step in gene expression in any living organism. Various environmental cues help in the maturation of core RNA polymerase (RNAP; ?(2)??'?) with different ?-factors, leading to the directed recruitment of RNAP to different promoter DNA sequences. Thus it is essential to determine the ?-factors that affect the preferential partitioning of core RNAP among various ?-actors, and the role of ?-switching in transcriptional gene regulation. Further, the macromolecular assembly of holo RNAP takes place in an extremely crowded environment within a cell, and thus far the kinetics and thermodynamics of this molecular recognition process have not been well addressed. In this study we used a site-directed bioaffinity immobilization method to evaluate the relative binding affinities of three different Escherichia coli ?-factors to the same core RNAP with variations in temperature and ionic strength while emulating the crowded cellular milieu. Our data indicate that the interaction of core RNAP-? is susceptible to changes in external stimuli such as osmolytic and thermal stress, and the degree of susceptibility varies among different ?-factors. This allows for a reversible ?-switching from housekeeping factors to alternate ?-factors when the organism senses a change in its physiological conditions.

SUBMITTER: Ganguly A

PROVIDER: S-EPMC3446719 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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A comparative kinetic and thermodynamic perspective of the σ-competition model in Escherichia coli.

Ganguly Abantika A Chatterji Dipankar D

Biophysical journal 20120901 6

Transcription is the most fundamental step in gene expression in any living organism. Various environmental cues help in the maturation of core RNA polymerase (RNAP; α(2)ββ'ω) with different σ-factors, leading to the directed recruitment of RNAP to different promoter DNA sequences. Thus it is essential to determine the σ-factors that affect the preferential partitioning of core RNAP among various σ-actors, and the role of σ-switching in transcriptional gene regulation. Further, the macromolecula ...[more]

PMID: 22995505

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Project description:Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Dataset Information

A comparative kinetic and thermodynamic perspective of the ?-competition model in Escherichia coli.

Publications

A comparative kinetic and thermodynamic perspective of the σ-competition model in Escherichia coli.

Similar Datasets

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