Research article
Evolution of metabolic networks: a computational frame-work
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* Corresponding author: Peter F Stadler studla@bioinf.uni-leipzig.de
1 Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090 Wien, Austria
2 Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany
3 FH Campus Wien, Diplom-Studiengang Bioengineering (Diploma Degree Course), Muthgasse 18, 1190 Wien, Austria
4 Bioinformatics Group, Institute for Computer Science, Albert-Ludwigs-University of Freiburg, Georges-Köhler-Alle 106, 79110 Freiburg, Germany
5 Image and Signal Processing Group, Department of Computer Science, University of Leipzig, Johannisgasse 26, D-04109 Leipzig, Germany
6 Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103 Leipzig, Germany
7 Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany
8 Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
Journal of Systems Chemistry 2010, 1:4 doi:10.1186/1759-2208-1-4
Published: 18 August 2010Abstract
Background
The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reactions upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, a genetic system that encodes these enzymes, and a fitness function that can be selected for.
Results
We describe here a simulation environment that implements all these components in a simplified way so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results.
Conclusions
The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy universe.