Abstract
Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a “many-to-many” control structure. Here we study several of these bipartite (two-layer) networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates) and a drug-target network composed of kinase inhibitors and of their kinase targets. Certain statistical properties of these biological bipartite structures 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%±1.2%. Links per node are predominantly exponentially distributed. We explain the conservation of the mean number of incoming links per target using a mathematical model of control networks, which also indicates that the “many-to-many” structure of biological control has properties of efficient robustness. The drug-target network has many statistical properties similar to the biological networks and we show that drug-target networks with biomimetic features can be obtained. These findings suggest a completely new approach to pharmacological control of biological systems. Molecular tools, such as kinase inhibitors, are now available to test if therapeutic combinations may benefit from being designed with biomimetic properties, such as “many-to-many” targeting, very wide coverage of the target set, and redundancy of incoming links per target.
Highlights
Control of cellular function depends on bipartite networks, in which one class of nodes acts on the other class to regulate its function
The number of controllers is usually significantly lower than the number of targets. This ‘‘many-to-many’’ structure is well recognized in biological systems [1], in intracellular control and in many other types of complex control in biology, including the nervous system
What are the advantages of the ‘‘many-to-many’’ structure in terms of robustness, and why, as we show here, do some parameters of the networks seem to be universal across different control structures and species? In order to explore the link between the network properties and robustness we introduce a simplified Boolean signaling model
Summary
Control of cellular function depends on bipartite (two-layer) networks, in which one class of nodes (the controller) acts on the other class (the target) to regulate its function. Examples of cellular control networks include transcription factors, microRNAs, and protein kinases, which control genes, mRNA transcripts, and protein substrates, respectively. In these networks, the control layer interacts with the target layer in a combinatorial, ‘‘many-to-many’’ fashion (see Figure 1). The number of controllers is usually significantly lower than the number of targets. This ‘‘many-to-many’’ structure is well recognized in biological systems [1], in intracellular control and in many other types of complex control in biology, including the nervous system (see Text S1, section S1.1)
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