A Mathematical Model for the Branched Chain Amino Acid Biosynthetic Pathways of Escherichia coli K12

Yang, Chin‐Rang; Shapiro, Bruce E.; Hung, She‐pin; Mjolsness, Eric; Hatfield, G. Wesley

doi:10.1074/jbc.m411471200

Cited by 37 publications

(33 citation statements)

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“…The emergence of local attractors belonging to different metabolic subsystems has been investigated in extensive studies mainly carried out by means of systems of differential equations, e.g., in Krebs cycle [96], amino acid biosynthetic pathways [97], oxidative phosphorylation subsystem [98], glycolytic subsystem [99], transduction in G-protein enzyme cascade [100], gene expression [101], cell cycle [102], etc.…”

Section: Discussionmentioning

confidence: 99%

Global Self-Regulation of the Cellular Metabolic Structure

et al. 2010

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BackgroundDifferent studies have shown that cellular enzymatic activities are able to self-organize spontaneously, forming a metabolic core of reactive processes that remain active under different growth conditions while the rest of the molecular catalytic reactions exhibit structural plasticity. This global cellular metabolic structure appears to be an intrinsic characteristic common to all cellular organisms. Recent work performed with dissipative metabolic networks has shown that the fundamental element for the spontaneous emergence of this global self-organized enzymatic structure could be the number of catalytic elements in the metabolic networks.Methodology/Principal FindingsIn order to investigate the factors that may affect the catalytic dynamics under a global metabolic structure characterized by the presence of metabolic cores we have studied different transitions in catalytic patterns belonging to a dissipative metabolic network. The data were analyzed using non-linear dynamics tools: power spectra, reconstructed attractors, long-term correlations, maximum Lyapunov exponent and Approximate Entropy; and we have found the emergence of self-regulation phenomena during the transitions in the metabolic activities.Conclusions/SignificanceThe analysis has also shown that the chaotic numerical series analyzed correspond to the fractional Brownian motion and they exhibit long-term correlations and low Approximate Entropy indicating a high level of predictability and information during the self-regulation of the metabolic transitions. The results illustrate some aspects of the mechanisms behind the emergence of the metabolic self-regulation processes, which may constitute an important property of the global structure of the cellular metabolism.

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Section: Discussionmentioning

confidence: 99%

Global Self-Regulation of the Cellular Metabolic Structure

et al. 2010

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“…To date, only a few amino acid biosyntheses in E. coli have been modeled successfully using a kinetic approach supported by experimental measurements, threonine (43) and branched chain amino acids (44). The molecular kinetic model for arginine biosynthesis in E. coli presented here takes into account the complex network of genetic and metabolic regulations at work in the system as well as its interaction with the de novo pyrimidine biosynthesis through the sharing of the common metabolite CP.…”

Section: Discussionmentioning

confidence: 99%

Arginine Biosynthesis in Escherichia coli

Caldara

Dupont

Leroy

et al. 2008

Journal of Biological Chemistry

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A basic challenge in cell biology is to understand how interconnected metabolic pathways are regulated to provide the adequate cellular outcome when changing levels of metabolites and enzyme expression. In Escherichia coli, the arginine and pyrimidine biosynthetic pathways are connected through a common metabolite provided by a single enzyme. The different elements of the arginine biosynthetic system of Escherichia coli, including the connection with pyrimidine biosynthesis, and the principal regulatory mechanisms operating at genetic and enzymatic levels were integrated in a mathematical model using a molecular kinetic approach combined with a modular description of the system. The model was then used to simulate a set of perturbed conditions as follows: genetic derepression, feedback resistance of the first enzymatic step, and low constitutive synthesis of the intermediate carbamyl phosphate. In all cases, an excellent quantitative agreement between simulations and experimental results was found. The model was used to gain further insight into the function of the system, including the synergy between the different regulations. The outcome of combinations of perturbations on cellular arginine concentration was predicted accurately, establishing the model as a powerful tool for the design of arginine-overproducing strains.One of the major aims of systems biology is to achieve a global understanding of metabolism by developing models able to describe and predict molecular and cellular functions at the level of whole metabolic pathways (1). Ultimately, the objective is to obtain comprehensive computer models of an entire living cell and all its functions. To attain such a goal, the International Escherichia coli Alliance was launched in 2002 to consolidate global E. coli modeling efforts. As a part of this global project, we developed a reliable mathematical model for arginine biosynthesis, based on the existing detailed knowledge of the principal regulatory mechanisms operating at both the genetic and the enzymatic levels of arginine metabolism (for reviews see Refs. 2, 3) as well as on new experimental determinations. A molecular kinetic model will be used that is appropriate for the description of highly regulated systems, in particular to establish a hierarchy of the different regulatory mechanisms at work in the system (4). This model can then be used to predict arginine production in strains containing different modifications of the regulatory circuits.Arginine constitutes about 5% of the total proteins of E. coli.

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“…Nonetheless, the analysis using the ordinary differential equations is still the most well-known method to study the bio-systems. A number of studies have been performed with this technique [164,165].…”

Section: Systems Biologymentioning

confidence: 99%

CE at the omics level: Towards systems biology – An update

Song

Babar

et al. 2007

Electrophoresis

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This review provides an updated overview of recent developments and applications of CE based on previously published reports in the field of omic research. The increased number of published articles on omics shows that the field is growing and attracting the attention of many life science researchers. Due to developments in the omics sciences, many researchers have been studying systems biology, in which biological events in organisms are systematically interpreted through the combination of complex measurements from various methods resulting in high-throughput data. Given the challenges of such complex forms of analysis, CE is a strong candidate for generating omics data useful for acquiring the qualitative and quantitative knowledge necessary for systems-level investigation. By emphasizing CE for systems biology, this review will discuss and focus on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels from 2005 to the present.

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A Mathematical Model for the Branched Chain Amino Acid Biosynthetic Pathways of Escherichia coli K12

Cited by 37 publications

References 34 publications

Global Self-Regulation of the Cellular Metabolic Structure

Global Self-Regulation of the Cellular Metabolic Structure

Arginine Biosynthesis in Escherichia coli

CE at the omics level: Towards systems biology – An update

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