Evolution of metabolic network organization

Abstract

BackgroundComparison 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.ResultsWe 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.ConclusionsCombining 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.

Cellular Automata Modeling of Biomolecular Networks

“…The answer of this question is a subject of our extensive almost completed study, in which the abundance of motifs in metabolic networks, and their higher level of organization termed network of interacting pathways (NIP) (Mazurie et al, 2008(Mazurie et al, , 2010, were analyzed in over 1000 species. Evidence for high statistical support was recovered for the over-representation of certain feedforward and bi-parallel motifs (subgraphs) with 3 and 4 nodes.…”

Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches

“…Reconstructed networks highlight the differences and similarities between intracellular mechanisms of various species and are hence important for comparative studies (Mazurie et al 2010). Identification of potential drug targets and the causative reactions of a disease is also possible due to availability of metabolic network models (Folger et al 2011).…”

Analysis of Metabolic Evolution in Bacteria Using Whole-Genome Metabolic Models

“…In recent years, a number of attempts have been made to study the evolution of metabolic networks, and these have provided insights into the mechanisms of evolution of various extant bacteria (Mithani et al, 2010;Mazurie et al, 2010;Pfeiffer et al, 2005). Understanding the evolution of bacterial metabolism is of great importance for a number of reasons.…”