Positive and negative cycles in Boolean networks

Richard, Adrien

doi:10.1016/j.jtbi.2018.11.028

Cited by 22 publications

(23 citation statements)

References 65 publications

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“…For example, negative influence manifests as interlayer hyperedges. Thus, it follows from theorem 19 of ( 46 ) that if a Boolean system’s interaction graph lacks negative feedback loops and has no paths of opposite sign between any two nodes, then there is a change of variables that disconnects the parity layers from one another.…”

Section: Methodsmentioning

confidence: 99%

“…Genome-scale models can have thousands of variables, resulting in many more states (~10 300 to ~10 9000 ) than Planck volumes in the observable universe (~10 185 ). This challenge has motivated decades of research analyzing discrete dynamics without exhaustive state-space searches ( 44 ), for example, by analyzing how feedback loops in the interaction network constrain dynamics ( 45 , 46 ). While the body of research regarding how network structure constrains dynamics has proven invaluable, note that multiple Boolean systems are compatible with each interaction network.…”

Section: Introductionmentioning

confidence: 99%

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Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

et al. 2021

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

et al. 2021

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“…A feedback vertex set (FVS) is a collection of nodes in a network whose removal results in a network with no cycles (no feedback loops). On a network with no feedback loops, dynamical processes described by Boolean or differential equation models have a single attractor [24,26]. FVS control thus predicts that by fixing all nodes in a given FVS, as well as all source nodes, to match a particular attractor, one can force the system from any state into that attractor [27].…”

Section: Feedback Vertex Set Controlmentioning

confidence: 99%

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

et al. 2021

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Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 17 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

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“…A feedback vertex set (FVS) is a collection of nodes in a network whose removal results in a network with no cycles (no feedback loops). On a network with no feedback loops, dynamical processes described by Boolean or differential equation models have a single attractor [22,24] . FVS control thus predicts that by fixing all nodes in a given FVS, as well as all source nodes, to match a particular attractor, one can force the system from any state into that attractor [25] .…”

Section: Feedback Vertex Set Controlmentioning

confidence: 99%

Mathematical modeling of theCandida albicansyeast to hyphal transition reveals novel control strategies

Wooten

Zañudo

Murrugarra

et al. 2021

Preprint

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Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition, and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 18 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

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Positive and negative cycles in Boolean networks

Cited by 22 publications

References 65 publications

Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

Mathematical modeling of theCandida albicansyeast to hyphal transition reveals novel control strategies

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