Metabolic network dynamics: Properties and principles

Abstract

The growth in size and complexity of kinetic models has stagnated since the late 1980s. To date this has been because the approaches used for building models required detailed measurements of kinetic parameters that is resource and energy intensive. Furthermore these measurements have been carried out in vitro, whose conditions generally do not reflect the in vivo conditions which are of interest. Genome sequencing in conjunction with bibliomic data enabled the construction of genome-scale stoichiometric models. With the fields of proteomics and more recently metabolomics rapidly advancing at an accelerated rate, there are opportunities to construct genome-scale kinetic models. In this chapter we discuss the key matrices: the stoichiometric and gradient matrices, that are required to construct genome-scale kinetic models and we expound upon their key properties and important considerations in biological dynamics. Analysis of biological dynamics and time scale separation can lead to modularization of a network through identification of groups of pooled metabolites. This modularization can help simplify what is otherwise a complex set of interactions. We illustrate some of these properties through consideration of a model of one-carbon metabolism in humans. © 2011 Springer Science+Business Media, LLC.