A probabilistic approach to identify putative drug targets in biochemical networks

Abstract

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity.

SupraBiology 2014: Promoting UK‐China collaboration on Systems Biology and High Performance Computing

“…The second approach consists of exploring the region of the parameter space that is compliant with the known outcome of the system. In this case Monte-Carlo sampling is commonly used to gain a probabilistic insight of some relevant system properties such as sensitivity coefficients [30][31][32]. Although random sampling can often get away with the combinatorial explosion of a systematic parameter screening without compromising prediction accuracy, its application to genome-wide networks or even multi-scale models is likely to greatly benefit from HPC resources.…”

A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis

“…1). Similar sampling approaches have been developed for classical MCA on simple or approximate kinetics and have been employed to explore control properties of biochemical networks [38,39], as well as for identification of putative drug targets in cancer cells [40]. While these approaches yielded valuable insights into the operation of biochemical networks, they have been limited to the analysis of reactions displaying simple kinetic mechanisms and lack a general framework to mathematically describe more complex enzymatic mechanisms.…”

Erratum to: A coupled thermodynamic and metabolic control analysis methodology and its evaluation on glycerol biosynthesis in Saccharomyces cerevisiae

“…In the present study, the steady-state glycerol-3-phosphate (G3P) concentration and the steady-state fluxes, v, respond to infinitesimal changes in the enzyme concentrations e (Wang et al 2004;Murabito et al 2011). These responses are described by the scaled concentration control coefficients, C G3P e , and the scaled flux control coefficients, C v e , respectively, given by (in matrix notation):…”

“…These approaches are based on randomized sampling of unknown or uncertain parameters within a constrained parameter space (Murabito et al 2011;Murabito 2013). One such approach is the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework developed by Hatzimanikatis and coworkers (Wang et al 2004;Miskovic and Hatzimanikatis 2010;Soh et al 2012;Chakrabarti et al 2013).…”

“…To compensate for the lack of in vivo kinetic enzyme data, researchers use probabilistic modeling approaches to predict the properties of metabolic networks (Klipp et al 2004;Wang et al 2004;Steuer et al 2006;Tran et al 2008;Murabito et al 2011). These approaches are based on randomized sampling of unknown or uncertain parameters within a constrained parameter space (Murabito et al 2011;Murabito 2013).…”