Bringing metabolomics data into the forefront of systems biology

Abstract

Mol Syst Biol. 2: 2006.0035 Systems Biology has been defined in various ways since the term was first used less than two decades ago, and the boundary between what does and does not constitute systems biology is not likely to be defined anytime soon. Two aspects that appear to be irrefutable are that systems biology involves the use of mathematical models and high‐throughput ‘omics’ data. A model should use the experimental results to understand the complex relationships and interactions among the various parts of the system, not merely organize and catalog the data into arbitrary classifications. Ideally, one would develop a model starting from the kinetic equations governing each molecular step in all aspects of the cell's existence: signaling, metabolism, growth, etc. However, owing to a lack of comprehensive knowledge, data, and computational power, it is not possible to formulate or solve this level of description on a genome scale. One mathematical framework that has gained wide acceptance in the systems biology community, particularly for the study of metabolism, is the general approach of constraint‐based modeling (Price et al , 2004). Instead of attempting to calculate an exact phenotypic ‘solution’, physico‐chemical constraints are imposed on a metabolic network to determine a feasible solution space in which the cell must operate. In this way, models …