期刊
SCIENCE ADVANCES
卷 7, 期 29, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abf8124
关键词
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资金
- NSF [PHY 1545832, MCB 1715826, IIS 1814405]
- Stand Up to Cancer Foundation/The V Foundation Convergence Scholar Award [D2015-039]
This study introduces new applications of parity inversion and time reversal to reveal stable and unstable manifolds in stochastic discrete models, demonstrating their predictive power in decision-making in systems biology and statistical physics models. Furthermore, it presents a novel attractor identification algorithm for Boolean networks under stochastic dynamics, which helped resolve a long-standing open question regarding attractor count in critical random Boolean networks and its scaling with network size.
We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 +/- 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system's relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
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