Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case

We present an algorithm that determines the smallest perturbations required for manipulating the dynamics of a network formulated as a Petri net, in order to cause or avoid a specified phenotype. By modifying McMillan's unfolding algorithm, we handle partial knowledge and reduce computation cost. The methodology is demonstrated on a glioma network. Out of the single gene perturbations, activation of glutathione S-transferase P (GSTP1) gene was by far the most effective in blocking the cancer phenotype. Among pairs of perturbations, NFkB and TGF-β had the largest joint effect, in accordance with their role in the EMT process.Our method allows perturbation analysis of regulatory networks and can overcome incomplete information. It can help in identifying drug targets and in prioritizing perturbation experiments.In contrast to the gene-centric approach, systems biology [1] emphasizes the importance of the interactions between different genes in determining the phenotype. Instead of asking "what is the role of gene A", the question becomes "what is the role of gene A in system B". The activity (or inactivity) of a gene is therefore not viewed as an isolated event, but assigned a meaning in the context in which it is active. An analogy from the sphere of computer science equates the genome to a database, and the system's dynamic behavior to the execution of a computer program that uses the database [2-4]. This paradigm shift has two major implications for the biomedical community. First, it complicates understanding cellular processes as each component must be considered with respect to its environment. Second, the fact that alternative phenotypes correspond to alternative dynamic behaviors of the system offers considerable advantages, because it is technically easier to influence the dynamics of a cellular network than to modify the information coded in the genome. Combining computational tools, which can help overcome the complexity of biological networks, with wet lab