Nature uses combinatorial control strategies that are different from those used by present pharmacological approaches for controlling disease. Function and differentiation of cells URLanisms are naturally regulated by control networks that are bipartite, i.e. contain two different types of nodes: controllers and targets. Each controller acts on many targets, and target nodes are controlled by many controllers in a strongly overlapping many-to-many network structure. We present a quantitative analysis of the network properties of three key biological systems for which sufficiently comprehensive datasets are now available: transcription factors, microRNAs  and protein kinases . We find that some parameters defining general network properties vary only within limited ranges , suggesting the existence of common control strategies in biology. The \pm  many-to-many structure might permit higher robustness to variation. A mathematical model also provides insight into the factors determining the values of the biological control network parameters and the network structure. The model showed an increased probability of finding solutions that achieve control within the naturally occurring range. This analysis of biological control networkssuggests a new paradigm for pharmacological interventions. Combinatorial therapies could be found by searching within biomimetic pharmacological sets with the same many-to-many structure as the biological systems and parameters within the ranges we observed in biology. The molecular tools for testing this new approach have recently become available . The necessary experiments would be greatly accelerated by a concerted effort of several pharmaceutical and biotech companies willing to pool compounds. This effort has potential benefits for the therapies of complex diseases, for the pharmaceutical industry and for our basic understanding of biological control .
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 .
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Nature uses combinatorial control strategies that are different from those used by present pharmacological approaches for controlling disease. Function and differentiation of cells URLanisms are naturally regulated by control networks that are bipartite, i.e. contain two different types of nodes: controllers and targets. Each controller acts on
Cells are regulated by networks of controllers having
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target nodes are controlled
targets affected
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in a strongly overlapping
, but these "
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network structure. We present a quantitative analysis of the network properties of three key biological systems for which sufficiently comprehensive datasets are now available:
" combinatorial control systems are poorly understood. Here we analyze distinct cellular networks (
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,
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. We find that some parameters defining general network properties vary only within limited ranges
) and a drug-target network. Certain network properties seem universal across systems and species
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number of controllers is ~8\% of targets and the density of links is 2.5\%
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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,
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structure might permit higher robustness to variation. A mathematical model also provides insight into the factors determining the values of the biological control network parameters and the network structure. The model showed an increased probability of finding solutions that achieve control within the naturally occurring range. This analysis of biological control networkssuggests a new paradigm for pharmacological interventions. Combinatorial therapies could be found by searching within biomimetic pharmacological sets with the same many-to-many structure as the biological systems and parameters within the ranges we observed in biology. The molecular tools for testing this new approach have recently become available . The necessary experiments would be greatly accelerated by a concerted effort of several pharmaceutical and biotech companies willing to pool compounds. This effort has potential benefits for the therapies of complex diseases, for the pharmaceutical industry and for our basic understanding of biological control
combinatorial structure, and molecular tools are available to test this approach