COMPARATIVE ANALYSIS OF METABOLIC NETWORKS OF PATHOGENS

Abstract

Metabolism is an important cellular process and its malfunction is a significant reason behind different human diseases. Metabolic Network is the system of connected chemical reactions. It is the interconnection between the different metabolites and physical processes determining physiological and biochemical properties in the particular metabolic pathway. Connections between biochemical responses through substrate and product metabolites produce complex metabolic networks that may be studied with the help of network theory, stoichiometric analysis, and information on protein structure or function and metabolite properties. Metabolic networks are complex and highly interconnected and hence it needs system level computational approach to identify the genotype - phenotype relationship. Many diseases are caused by failures of metabolic enzymes. These enzymes exist in the perspective of networks well characterized by the static topology of enzyme-metabolite interactions and by the reaction fluxes that are possible at steady state. Flux balance analysis (FBA) is based on the linear programming algorithm. It has developed as a powerful method for the in silico analyses of metabolic networks. Here I present the Flux balance analysis of the genomic scale metabolic models of the four microorganisms i.e. Microbacterium tuberculosis, Staphylococcus aureus, Helicobacter pylori, and Salmonella typhimurium. Using this approach Gene Essentiality and Synthetic Lethality of the metabolic pathway of the given metabolic models has been performed with the help of Fast-SL algorithm. Synthetic lethal genes are the pairs of non-essential genes whose simultaneous deletion limits growth. These attributes helps to find out some of the important factors for the comparative analysis like essential genes and reactions. The study of synthetic lethality plays a crucial role in explaining functional links between genes and gene function predictions. Metabolite connectivity provides a major insight into basic structure of the metabolite networks.