Systems biology Advance Access publication April 29, 2013 Intervention in gene regulatory networks with maximal phenotype alteration

Motivation: A basic issue for translational genomics is to model gene interaction via gene regulatory networks (GRNs) and thereby provide an informatics environment to study the effects of intervention (say, via drugs) and to derive effective intervention strategies. Taking the view that the phenotype is characterized by the long-run behavior (steady-state distribution) of the network, we desire interventions to optimally move the probability mass from undesirable to desirable states Heretofore, two external control approaches have been taken to shift the steady-state mass of a GRN: (i) use a user-defined cost func-tion for which desirable shift of the steady-state mass is a by-product and (ii) use heuristics to design a greedy algorithm. Neither approach provides an optimal control policy relative to long-run behavior. Results:We use a linear programming approach to optimally shift the steady-state mass from undesirable to desirable states, i.e. optimiza-tion is directly based on the amount of shift and therefore must out-perform previously proposed methods. Moreover, the same basic linear programming structure is used for both unconstrained and con-strained optimization, where in the latter case, constraints on the op-timization limit the amount of mass that may be shifted to ‘ambiguous’ states, these being states that are not directly undesirable relative to the pathology of interest but which bear some perceived risk. We apply the method to probabilistic Boolean networks, but the theory applies to any Markovian GRN. Availability: Supplementary materials, including the simulation results, MATLAB source code and description of suboptimal methods are available a