TY - JOUR
T1 - Criticality Distinguishes the Ensemble of Biological Regulatory Networks
AU - DANIELS, BRYAN
AU - Kim, Hyunju
AU - Moore, Douglas
AU - Zhou, Siyu
AU - Smith, Harrison B.
AU - Karas, Bradley
AU - Kauffman, Stuart A.
AU - Walker, Sara
N1 - Publisher Copyright:
© 2018 American Physical Society.
PY - 2018/9/28
Y1 - 2018/9/28
N2 - The hypothesis that many living systems should exhibit near-critical behavior is well motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near critical. By comparing to ensembles of random networks with similar topological and logical properties, we show that criticality in biological networks is not predictable solely from macroscale properties such as mean degree K and mean bias in the logic functions p, as previously emphasized in theories of random Boolean networks. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.
AB - The hypothesis that many living systems should exhibit near-critical behavior is well motivated theoretically, and an increasing number of cases have been demonstrated empirically. However, a systematic analysis across biological networks, which would enable identification of the network properties that drive criticality, has not yet been realized. Here, we provide a first comprehensive survey of criticality across a diverse sample of biological networks, leveraging a publicly available database of 67 Boolean models of regulatory circuits. We find all 67 networks to be near critical. By comparing to ensembles of random networks with similar topological and logical properties, we show that criticality in biological networks is not predictable solely from macroscale properties such as mean degree K and mean bias in the logic functions p, as previously emphasized in theories of random Boolean networks. Instead, the ensemble of real biological circuits is jointly constrained by the local causal structure and logic of each node. In this way, biological regulatory networks are more distinguished from random networks by their criticality than by other macroscale network properties such as degree distribution, edge density, or fraction of activating conditions.
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U2 - 10.1103/PhysRevLett.121.138102
DO - 10.1103/PhysRevLett.121.138102
M3 - Article
C2 - 30312104
AN - SCOPUS:85054182691
SN - 0031-9007
VL - 121
JO - Physical Review Letters
JF - Physical Review Letters
IS - 13
M1 - 138102
ER -