Structure-based approach to identifying small sets of driver nodes in biological networks

Abstract

In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories -- centrality measures, propagation measures, and cycle-based measures. Using each measure every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system towards or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without knowledge of the network's dynamics.

arXiv comment22 pages for main text 4 pages for supplementary materials, 10 figures. Supplementary tables S5 and S6 are ancillary files

References in corpus (12)

• The structure and function of complex networks
• Hierarchical organization of modularity in metabolic networks
• Control Principles of Complex Networks
• Realistic Control of Network Dynamics
• Structure-based control of complex networks with nonlinear dynamics
• Cell fate reprogramming by control of intracellular network dynamics
• An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks
• Energy Scaling of Targeted Optimal Control of Complex Networks
• Two-dimensional ranking of Wikipedia articles
• Identification of control targets in Boolean molecular network models via computational algebra
• Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition
• Locally Optimal Control of Complex Networks

Cited by in corpus (2)

• Cell reprogramming design by transfer learning of functional transcriptional networks
• Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks