COMPUTATIONAL ANALYSIS OF METABOL(OM)IC NETWORKS

Computational methods have been widely used in omics to handle 'big data' generated through high throughput technologies. However, metabolomics analysis is relatively naive and aims to understand the highly interconnected 'networks' rather than treating them as 'isolated pathways'. To deal with metabolic networks, it is essential to integrate the metabolic pathways so as to understand the stoichiometry and to represent them in the form of a matrix. This enables to compute elemental, mass, heat, energy and flux balances. This helps in analysing metabolic flux and control. The main focus of this chapter is to introduce the concept of reconstructing metabolic networks. This includes 'information fluxes' as in signal transduction and other metabolic networks. This chapter also introduces graph theory to deal with metabolic networks and represent mathematical forms of reconstructed networks, and provide an overview of adjacency and stoichiometric matrices, elemental matrix, open and closed networks, etc. In brief, this chapter aims to intersect disciplines such as metabolomics, bioprocess engineering, metabolic engineering, network biology and systems biology.