Literature DB >> 19669494

A strategy to calculate the patterns of nutrient consumption by microorganisms applying a two-level optimisation principle to reconstructed metabolic networks.

Miguel Ponce de León1, Héctor Cancela, Luis Acerenza.   

Abstract

Bacterial responses to environmental changes rely on a complex network of biochemical reactions. The properties of the metabolic network determining these responses can be divided into two groups: the stoichiometric properties, given by the stoichiometry matrix, and the kinetic/thermodynamic properties, given by the rate equations of the reaction steps. The stoichiometry matrix represents the maximal metabolic capabilities of the organism, and the regulatory mechanisms based on the rate laws could be considered as being responsible for the administration of these capabilities. Post-genomic reconstruction of metabolic networks provides us with the stoichiometry matrix of particular strains of microorganisms, but the kinetic aspects of in vivo rate laws are still largely unknown. Therefore, the validity of predictions of cellular responses requiring detailed knowledge of the rate equations is difficult to assert. In this paper, we show that by applying optimisation criteria to the core stoichiometric network of the metabolism of Escherichia coli, and including information about reversibility/irreversibility only of the reaction steps, it is possible to calculate bacterial responses to growth media with different amounts of glucose and galactose. The target was the minimisation of the number of active reactions (subject to attaining a growth rate higher than a lower limit) and subsequent maximisation of the growth rate (subject to the number of active reactions being equal to the minimum previously calculated). Using this two-level target, we were able to obtain by calculation four fundamental behaviours found experimentally: inhibition of respiration at high glucose concentrations in aerobic conditions, turning on of respiration when glucose decreases, induction of galactose utilisation when the system is depleted of glucose and simultaneous use of glucose and galactose as carbon sources when both sugars are present in low concentrations. Preliminary results of the coarse pattern of sugar utilisation were also obtained with a genome-scale E. coli reconstructed network, yielding similar qualitative results.

Entities:  

Year:  2008        PMID: 19669494      PMCID: PMC2577741          DOI: 10.1007/s10867-008-9067-2

Source DB:  PubMed          Journal:  J Biol Phys        ISSN: 0092-0606            Impact factor:   1.365


  23 in total

1.  Kinetic models for the growth of Escherichia coli with mixtures of sugars under carbon-limited conditions.

Authors:  U Lendenmann; T Egli
Journal:  Biotechnol Bioeng       Date:  1998-07-05       Impact factor: 4.530

Review 2.  The acetate switch.

Authors:  Alan J Wolfe
Journal:  Microbiol Mol Biol Rev       Date:  2005-03       Impact factor: 11.056

3.  Observations on the carbohydrate metabolism of tumours.

Authors:  H G Crabtree
Journal:  Biochem J       Date:  1929       Impact factor: 3.857

4.  On the origins of a crowded cytoplasm.

Authors:  Luis Acerenza; Martin Graña
Journal:  J Mol Evol       Date:  2006-09-26       Impact factor: 2.395

5.  Recovering metabolic pathways via optimization.

Authors:  John E Beasley; Francisco J Planes
Journal:  Bioinformatics       Date:  2006-10-26       Impact factor: 6.937

6.  Stochastic fluctuations in metabolic pathways.

Authors:  Erel Levine; Terence Hwa
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-18       Impact factor: 11.205

Review 7.  Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics.

Authors:  K Kovárová-Kovar; T Egli
Journal:  Microbiol Mol Biol Rev       Date:  1998-09       Impact factor: 11.056

8.  Optimal metabolic control design.

Authors:  F Ortega; L Acerenza
Journal:  J Theor Biol       Date:  1998-04-21       Impact factor: 2.691

9.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

Authors:  Adam M Feist; Christopher S Henry; Jennifer L Reed; Markus Krummenacker; Andrew R Joyce; Peter D Karp; Linda J Broadbelt; Vassily Hatzimanikatis; Bernhard Ø Palsson
Journal:  Mol Syst Biol       Date:  2007-06-26       Impact factor: 11.429

10.  Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli.

Authors:  Robert Schuetz; Lars Kuepfer; Uwe Sauer
Journal:  Mol Syst Biol       Date:  2007-07-10       Impact factor: 11.429

View more
  4 in total

Review 1.  Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

Authors:  Nathan E Lewis; Harish Nagarajan; Bernhard O Palsson
Journal:  Nat Rev Microbiol       Date:  2012-02-27       Impact factor: 60.633

2.  Predicting internal cell fluxes at sub-optimal growth.

Authors:  André Schultz; Amina A Qutub
Journal:  BMC Syst Biol       Date:  2015-04-03

3.  Metabolic Complementation in Bacterial Communities: Necessary Conditions and Optimality.

Authors:  Matteo Mori; Miguel Ponce-de-León; Juli Peretó; Francisco Montero
Journal:  Front Microbiol       Date:  2016-10-07       Impact factor: 5.640

4.  A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype.

Authors:  Jon Pey; Luis Tobalina; Joaquín Prada J de Cisneros; Francisco J Planes
Journal:  BMC Syst Biol       Date:  2013-07-19
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.