2010/06/30 by Jacob D. Feala, Jacob Feala, Jorge Cortés +5 · 15 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #Biological network #Biology #Bipartite graph #Computational Drug Discovery Methods #Computational biology #Computer science #Gene #Gene Regulatory Network Analysis #Genetics #Redundancy (engineering) #Robustness (evolution) #Systems biology #Theoretical computer science #q-bio.MN
paper · pdf · doi:10.1371/journal.pone.0029374
published in PLoS ONE 7(1), e29374 (Public Library of Science) · 33 pages
arxiv created 2010/12/21 · openalex publication_date 2012/01/03 · arxiv updated 2012/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.