2020/10/31 by Enrico Borriello, Bryan C. Daniels · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · #Algorithm #Applied mathematics #Attractor #Basis (linear algebra) #Biological network #Boolean function #Boolean network #Complex network #Computer science #Controllability #Discrete mathematics #Gene Regulatory Network Analysis #Mathematics #Network dynamics #Neural dynamics and brain function #Physics #Receptor Mechanisms and Signaling #Scaling #State (computer science) #Statistical physics #Theoretical computer science #Topology (electrical circuits) #q-bio.MN
paper · pdf · doi:10.1038/s41467-021-25533-3
published in Nature Communications 12(1), 5227 (Nature Portfolio) · 44 pages, 17 figures, 2 table
openalex publication_date 2021/09/01 · arxiv created 2021/09/09 · arxiv updated 2021/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Effective control of biological systems can often be achieved through the control of a surprisingly small number of distinct variables. We bring clarity to such results using the formalism of Boolean dynamical networks, analyzing the effectiveness of external control in selecting a desired final state when that state is among the original attractors of the dynamics. Analyzing 49 existing biological network models, we find strong numerical evidence that the average number of nodes that must be forced scales logarithmically with the number of original attractors. This suggests that biological networks may be typically easy to control even when the number of interacting components is large. We provide a theoretical explanation of the scaling by separating controlling nodes into three types: those that act as inputs, those that distinguish among attractors, and any remaining nodes. We further identify characteristics of dynamics that can invalidate this scaling, and speculate about how this relates more broadly to non-biological systems.