Graph Representations of Monotonic Boolean Model Pools

Schwieger, Robert; Siebert, Heike

doi:10.1007/978-3-319-67471-1_14

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Information about constraints on the behavior of models in this family is stored in a so called qualitative state transition graph G QDE (Σ) [4]. We showed previously that a similar approach can be used to analyze a model pool of Boolean functions characterized by a common interaction graph [13]. Here we show that the opposite approach is fruitful as well.…”

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confidence: 89%

Representing Model Ensembles as Boolean Functions

Schwieger¹,

Siebert²

2018

Preprint

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Families of ODE models ẋ = f (x) characterized by a common sign structure Σ of their Jacobi matrix J(f ) are investigated within the formalism of qualitative differential equations [4]. In the context of regulatory networks the sign structure of the Jacobi matrix carries the information about which components of the network inhibit or activate each other. Information about constraints on the behavior of models in this family is stored in a so called qualitative state transition graph G QDE (Σ) [4]. We showed previously that a similar approach can be used to analyze a model pool of Boolean functions characterized by a common interaction graph [13]. Here we show that the opposite approach is fruitful as well. We show that the qualitative state transition graph G QDE (Σ) can be reduced to a "skeleton" represented by a Boolean function f Σ conserving the reachability properties. This reduction has the advantage that approaches such as model checking and network inference methods can be applied to G async (f Σ ) within the framework of Boolean networks. Furthermore, our work constitutes an alternative to approaches like [14] and [15] to link Boolean networks and differential equations.

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confidence: 89%

Representing Model Ensembles as Boolean Functions

Schwieger¹,

Siebert²

2018

Preprint

Self Cite

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“…In the literature, ensembles of random BNs have been employed to show emerging properties of families of BNs sharing properties related to their architecture or logic rules [14,16]. In [21], ensembles of BNs sharing a network architecture are used to assess dynamical properties of qualitative differential equations. In contrast, our approach is focused on ensembles of models which satisfy a set of constraints both on their architecture and on their dynamics.…”

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Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision

Chevalier

Noël

Calzone

et al. 2020

Computational Methods in Systems Biology

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The construction of models of biological networks from prior knowledge and experimental data often leads to a multitude of candidate models. Devising a single model from them can require arbitrary choices, which may lead to strong biases in subsequent predictions. We introduce here a methodology for a) synthesizing Boolean model ensembles satisfying a set of biologically relevant constraints and b) reasoning on the dynamics of the ensembles of models. The synthesis is performed using Answer-Set Programming, extending prior work to account for solution diversity and universal constraints on reachable fixed points, enabling an accurate specification of desired dynamics. The sampled models are then simulated and the results are aggregated through averaging or can be analyzed as a multi-dimensional distribution. We illustrate our approach on a previously published Boolean model of a molecular network regulating the cell fate decisions in cancer progression. It appears that the ensemble-based approach to Boolean modelling brings new insights on the variability of synergistic interacting mutations effect concerning propensity of a cancer cell to metastasize.

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Variable Stabilisation in Boolean Monotonic Model Pools

Pastva

2022

Computational Methods in Systems Biology

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Graph Representations of Monotonic Boolean Model Pools

Cited by 4 publications

References 12 publications

Representing Model Ensembles as Boolean Functions

Representing Model Ensembles as Boolean Functions

Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision

Variable Stabilisation in Boolean Monotonic Model Pools

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