Boolean Networks: A Primer

Abstract

Autism Spectrum Disorders (ASDs) stand out as a relevant example where omics-data approaches have been extensively and successfully employed. For instance, an outstanding outcome of the Autism Genome Project relies in the identification of biomarkers and the mapping of biological processes potentially implicated in ASDs’ pathogenesis. Several of these mapped processes are related to molecular and cellular events (e.g., synaptogenesis and synapse function, axon growth and guidance, etc.) that are required for the development of a correct neuronal connectivity. Interestingly, these data are consistent with results of brain imaging studies of some patients. Despite these remarkable progresses it is still quite a challenge to reconstruct the causal chain of events linking mutations, epimutations and other molecular defects to the spectrum of phenotypes and endophenotypes present in autistic patients. In this article we review the state-of-art of omics-data research in ASDs and present how the use of in silico dynamics modeling of biochemical networks becomes a powerful approach to investigate ASDs omics-data from a different perspective to extract more information. To this end, we generated—for the first time—a protein network regulating the dynamics of the growth cone and we then transformed it into the equivalent Boolean model. Using in silico mutagenesis we show that simulations of this model lead to a more comprehensive and systemic understanding of the cellular and molecular effects of gene mutations in ASDs compared to conventional analyses of static pathways. Such powerful tool might constitute a significant twist in the analysis of in silico genotype-phenotype relationship for ASDs and other multi-genic neurodevelopmental disorders, especially when combined with the latest sequencing technologies applied to single patients.