Growth Dependent Computation of Chokepoints in Metabolic Networks

Abstract

Bacterial infections are among the major causes of mortality in the world. Despite the social and economical burden produced by bacteria, the number of new drugs to combat them increases very slowly due to the cost and time to develop them. Thus, innovative approaches to identify efficiently drug targets are required. In the absence of genetic information, chokepoint reactions represent appealing drug targets since their inhibition might involve an important metabolic damage. In contrast to the standard definition of chokepoints, which is purely structural, this paper makes use of the dynamical information of the model to compute chokepoints. This novel approach can provide a more realistic set of chokepoints. The dependence of the number of chokepoints on the growth rate is assessed on a number of metabolic networks. A software tool has been implemented to facilitate the computation of growth dependent chokepoints by the practitioners.