Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data

Motivation: High-throughput technologies have facilitated the acquisition of large genomics and proteomics datasets. However, these data provide snapshots of cellular behavior, rather than help us reveal causal relations. Here, we propose how these technologies can be utilized to infer the topology and strengths of connections among genes, proteins and metabolites by monitoring time-dependent responses of cellular networks to experimental interventions. Results: We demonstrate that all connections leading to a given network node, e.g. to a particular gene, can be deduced from responses to perturbations none of which directly influences that node, e.g. using strains with knock-outs to other genes. To infer all interactions from stationary data, each node should be perturbed separately or in combination with other nodes. Monitoring time series provides richer information and does not require perturbations to all nodes. Overall, the methods we propose are capable of deducing and quantifying functional interactions within and across cellular gene, signaling and metabolic networks.