Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

Open Access Highly Accessed Research article

A stochastic automaton shows how enzyme assemblies may contribute to metabolic efficiency

Patrick Amar12, Guillaume Legent3, Michel Thellier13, Camille Ripoll13, Gilles Bernot1, Thomas Nystrom4, Milton H Saier5 and Vic Norris13*

Author Affiliations

1 Epigenomics Programme, genopole®, 91000 Evry, France

2 Laboratoire de Recherches en Informatique, Université Paris Sud & CNRS UMR 8623, 15 avenue George Clémenceau, 91405 Orsay, Cedex, France

3 Laboratoire d'Assemblages moléculaires: modélisation et imagerie SIMS, Faculté des Sciences de l'Université de Rouen, 76821 Mont Saint Aignan Cedex, France

4 Department of Cell and Molecular Biology, Goteborg University, Medicinaregatan 9C, 413 90 Goteborg, Sweden

5 Division of Biological Sciences, University of California at San Diego, La Jolla, CA 92093-0116 USA

For all author emails, please log on.

BMC Systems Biology 2008, 2:27  doi:10.1186/1752-0509-2-27

Published: 25 March 2008

Abstract

Background

The advantages of grouping enzymes into metabolons and into higher order structures have long been debated. To quantify these advantages, we have developed a stochastic automaton that allows experiments to be performed in a virtual bacterium with both a membrane and a cytoplasm. We have investigated the general case of transport and metabolism as inspired by the phosphoenolpyruvate:sugar phosphotransferase system (PTS) for glucose importation and by glycolysis.

Results

We show that PTS and glycolytic metabolons can increase production of pyruvate eightfold at low concentrations of phosphoenolpyruvate. A fourfold increase in the numbers of enzyme EI led to a 40% increase in pyruvate production, similar to that observed in vivo in the presence of glucose. Although little improvement resulted from the assembly of metabolons into a hyperstructure, such assembly can generate gradients of metabolites and signaling molecules.

Conclusion

in silico experiments may be performed successfully using stochastic automata such as HSIM (Hyperstructure Simulator) to help answer fundamental questions in metabolism about the properties of molecular assemblies and to devise strategies to modify such assemblies for biotechnological ends.