Evolution of metabolic network organization
1 Institut Pasteur, Systems Biology Lab, Department of Genomes and Genetics, F-75015 Paris, France
2 CNRS URA 2171, F-75015 Paris, France
3 Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond VA 23284, USA
4 Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond VA 23284, USA
5 Department of Microbiology and Immunology, Virginia Commonwealth University, Richmond VA 23298, USA
BMC Systems Biology 2010, 4:59 doi:10.1186/1752-0509-4-59Published: 11 May 2010
Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints.
We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints.
Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.