Biological control networks suggest the use of biomimetic sets for combinatorial therapies

TL;DR

This study analyzes biological control networks across different systems, revealing universal properties and suggesting that combinatorial therapies could benefit from biomimetic, many-to-many control strategies similar to natural biological networks.

Contribution

The paper uncovers universal network properties in cellular control systems and proposes biomimetic, combinatorial therapeutic strategies based on these insights.

Findings

Controllers comprise about 8% of targets

Link density is approximately 2.5%

Links per node follow an exponential distribution

Abstract

Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, but these "many-to-many" combinatorial control systems are poorly understood. Here we analyze distinct cellular networks (transcription factors, microRNAs, and protein kinases) and a drug-target network. Certain network properties seem universal across systems and species, suggesting the existence of common control strategies in biology. The number of controllers is ~8% of targets and the density of links is 2.5% \pm 1.2%. Links per node are predominantly exponentially distributed, implying conservation of the average, which we explain using a mathematical model of robustness in control networks. These findings suggest that optimal pharmacological strategies may benefit from a similar, many-to-many combinatorial structure, and molecular tools are available to test this approach.