Abstract

Systems Engineering Perspective of Human Metabolism through a Multiscale Model for Disease Analysis : A Cell to Human Framework

K V Venkatesh, Professor, Indian Institute of Technology Bombay

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The characterization of the network involves interactions from genes to proteins to metabolites. In this hierarchical link from genotype to phenotype, metabolomics directly links to the phenotypic state. The analysis of metabolites present in a particular phenotype forms the basis of characterization of the system state of the cell/tissue. Using steady state and dynamical models along with metabolomics data, one can try to obtain insights into the system level properties. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The research work highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of diabetes and hypercholesterolemia. We put forth a concept of cell-to- human framework comprising of five modules (data mining, networking, modeling, and experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The work emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.