Predicting complex genetic phenotypes using error propagation in weighted networks

Abstract

Network-biology view of biological systems is a ubiquitous abstraction that emerged in the last two decades to allow a high-level understanding of principles governing them. However, the principles according to which biological systems are organized are still unclear. Here, we investigate if biological networks could be approximated as overlapping, feed-forward networks where the nodes have non-linear activation functions. Such networks have been shown to be universal approximators and their stability has been explored in the context of artificial neural networks. Mathematical formalization of this model followed by numerical simulations based on genomic data allowed us to accurately predict the statistics of gene essentiality in yeast and hence indicate that biological networks might be better understood as a distributed system, comprising potentially unreliable components.