Phenotype Control techniques for Boolean gene regulatory networks

Plaugher, Daniel; Murrugarra, David

doi:10.1007/s11538-023-01197-6

Cited by 3 publications

(5 citation statements)

References 49 publications

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“…For an overview of some of the most prominent control techniques (see ref. 21 ). Using apoptosis as the desired attractor, one could control the entire system by regulating a total of three nodes within the top two modules.…”

Section: Resultsmentioning

confidence: 99%

“…While a multi-scale model would likely provide more realistic simulations, state-of-the-art control techniques require a network model specification such as Boolean networks (or their generalizations) to find optimal therapeutic interventions 21 . Implementation of Boolean networks (BNs) provides a coarse-grained description of signaling cascades without the need for tedious parameter fitting and can be simulated through stochastic discrete dynamical systems (SDDS 22 ) to streamline the modeling process and increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations

Plaugher,

Murrugarra

2024

npj Syst Biol Appl

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There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations

Plaugher,

Murrugarra

2024

npj Syst Biol Appl

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“…Figure 4a shows how phenotypes typically lie at the end (i.e. the bottom of a basin) because they are the dynamical endpoint: Apoptosis s (24), Proliferation s (26), Migration s (28), Activation s (27), Autophagy c (21), Apoptosis c (20), and Proliferation c (23). Thus, the condensation ordering begins with central nodes and expands distally to phenotypes.…”

Section: Mutations Perturb Modular Structurementioning

confidence: 99%

“…In [1], target analysis was performed on the wild-type PC model in Figure 3. For an overview of some of the most prominent control techniques, see [21]. Using apoptosis as the desired attractor, one could control the entire system by regulating a total of three nodes within the top two modules.…”

Section: Mutations Impact Target Efficacymentioning

confidence: 99%

“…The high complexity of these models generates challenges for model validation such as the need to estimate too many model parameters. Moreover, a multi-scale model would likely provide more realistic simulations, but their complexity prevents the application of state of the art control techniques to find optimal therapeutic alterations [21]. Implementation of Boolean networks (BNs) provides a course grained description of signaling cascades without the need for tedious parameter fitting and can be simulated through stochastic discrete dynamical systems (SDDS) to streamline the modeling process and increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations

Plaugher,

Murrugarra

2024

Preprint

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Across cancer types, the prognosis for pancreatic cancer (PC) is among the worst and options for treatment are limited. There is increasing evidence that biological systems, including PC, are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time. In this work, we use an established Boolean multicellular signaling network of PC to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.

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Phenotype control and elimination of variables in Boolean networks

Tonello,

Paulevé

2024

Peer Community Journal

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Phenotype Control techniques for Boolean gene regulatory networks

Cited by 3 publications

References 49 publications

Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations

Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations

Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations

Phenotype control and elimination of variables in Boolean networks

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