Bisimilar Booleanization of multivalued networks

Delaplace, Franck; Ivanov, Sergiu

doi:10.1016/j.biosystems.2020.104205

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Multivalued BNs, i.e. whose variables can take more than two activation values, are currently supported only via a booleanization technique [18,14] that might hamper the interpretability of the reduced model. In future work we plan to generalize BBE to support directly multivalued networks.…”

Section: Discussionmentioning

confidence: 99%

“…{0, 1, 2}. These models are automatically booleanized [18,14] by GinSim when exporting in the input formats of the other tools in the tool-chain.…”

Section: Large Scale Validation Of Bbe On Bnsmentioning

confidence: 99%

“…BNs where variables are multivalued, i.e. can take more than two values to express different levels of activation [46], are supported via the use of booleanization techniques [18], at the cost, however, of increasing the number of variables. BNs suffer from the state space explosion problem: there are exponentially many STG states with respect to the number of BN variables.…”

Section: Introductionmentioning

confidence: 99%

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Reducing Boolean Networks with Backward Boolean Equivalence

Argyris¹,

Lafuente²,

Tribastone³

et al. 2021

Preprint

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Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called Backward Boolean Equivalence (BBE), which preserves some properties of interest of BNs. In particular, reduced BNs provide a compact representation by grouping variables that, if initialized equally, are always updated equally. The resulting reduced state space is a subset of the original one, restricted to identical initialization of grouped variables. The corresponding trajectories of the original BN can be exactly restored. We show the effectiveness of BBE by performing a large-scale validation on the whole GINsim BN repository. In selected cases, we show how our method enables analyses that would be otherwise intractable. Our method complements, and can be combined with, other reduction methods found in the literature.

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Section: Discussionmentioning

confidence: 99%

“…{0, 1, 2}. These models are automatically booleanized [18,14] by GinSim when exporting in the input formats of the other tools in the tool-chain.…”

Section: Large Scale Validation Of Bbe On Bnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reducing Boolean Networks with Backward Boolean Equivalence

Argyris¹,

Lafuente²,

Tribastone³

et al. 2021

Preprint

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“…Nevertheless, it is easy to replace Boolean states by a multi-valued ones, or to simulate a multi-valued state by a set of Boolean nodes, in order to represent biological amounts more realistically (e.g. [5]). Moreover weighted edges reflect the relative strength of interactions that biological elements exert onto each other.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Evaluating the Extensibility of Boolean Networks’ Structure and Function

Segretain

Ivanov

Trilling

et al. 2021

Studies in Computational Intelligence

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Formal interaction networks are well suited for representing complex biological systems and have been used to model signalling pathways, gene regulatory networks, interaction within ecosystems, etc. In this paper, we introduce Sign Boolean Networks (SBNs), which are a uniform variant of Threshold Boolean Networks (TBFs). We continue the study of the complexity of SBNs and build a new framework for evaluating their ability to extend, i.e. the potential to gain new functions by addition of nodes, while also maintaining the original functions. We describe our software implementation of this framework and show some first results. These results seem to confirm the conjecture that networks of moderate complexity are the most able to grow, because they are not too simple, but also not too constrained, like the highly complex ones.

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