Reconciling qualitative, abstract, and scalable modeling of biological networks.

ABSTRACT: Predicting biological systems' behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks

SUBMITTER: Pauleve L 

PROVIDER: S-EPMC7450094 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature