Computational analysis of biochemical networks for drug target identification and therapeutic intervention design

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 96-104).Identification of effective drug targets to intervene, either as single agent therapy or in combination, is a critical question in drug development. As complexity of disease like cancer is revealed, it has become clear that a holistic network approach is needed to identify drug targets that are specially positioned to provide desired leverage on disease phenotypes. In this thesis we develop a computational framework to exhaustively evaluate target behaviors in biochemical network, either as single agent or combination therapies. We present our single target therapy work as a problem of identifying good places to intervene in a network. We quantify a relationship between how interventions at different places in network affect an output of interest. We use this quantitative relationship between target inhibited and output of interest as a metric to compare targets. In network analyzed here, most targets show a sub-linear behavior where a large percentage of targeted molecule needs to be inhibited to see a small change on output. The other key observation is that targets at the top of the network exerted relatively small control compared to the targets at the bottom of the network. In the combination therapy work we study how combination of drug concentrations affect network output of interest compared to when one of the drugs was given alone at equivalent concentrations. By adapting the definitions of additive, synergistic, and antagonistic combination behaviors developed by Ting Chao-Chou (Chou TC, Talalay P (1984), Advances in enzyme regulation 22: 27-55) for our system and systematically perturbing biochemical pathway, we explore the range of combination behaviors for all plausible combination targets. This holistic approach reveals that most target combinations show additive behaviors. Synergistic, and antagonistic behaviors are rare. Even when combinations are classified as synergistic or antagonistic, they show this behavior only in a small range of the inhibitor concentrations. This work is developed in a particular variant of the epidermal growth factor (EGF) receptor pathway for which a detailed mathematical model was first proposed by Schoeberl et al. Computational framework developed in this work is applicable to any biochemical network.by Nirmala Paudel.Ph. D