Computing Symbolic Steady States of Boolean Networks

Klarner, Hannes; Bockmayr, Alexander; Siebert, Heike

doi:10.1007/978-3-319-11520-7_59

Cited by 27 publications

(39 citation statements)

References 9 publications

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“…The efficient identification of cyclical attractors and complex attractors remain a challenging problem, especially as these attractors can depend on the updating mode. Stable patterns have recently been proposed as an approximation of complex attractors, which can be identified efficiently and does not depend on the updating mode [17,40]. Here, a pattern is a partially-defined state where some components have a fixed activity level, while others are undefined.…”

Section: Identification Of Attractorsmentioning

confidence: 99%

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bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

Naldi

2018

Preprint

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Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks, aiming to foster the development of novel complementary tools by providing core modelling operations. Based on the definition of multi-valued logical models, it implements import and export facilities, notably for the recent SBML-qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case. Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements the usual updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.

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Section: Identification Of Attractorsmentioning

confidence: 99%

“…BioLQM proposes an adapted version of the method implemented in PyBoolNet [17,18] using the clingo ASP solver [13], and introduces a new alternative implementation based on decision diagrams.…”

Section: Identification Of Attractorsmentioning

confidence: 99%

bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

Naldi

2018

Preprint

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“…Different update strategies yield different dynamics, with subtleties: steady-state attractors are the same both for synchronous and asynchronous updates of a Boolean Network [19,3]. Stable motifs [100] and trap spaces [46,47], that is, trap sets with particularly simple dynamics, are also independent of the particular updating schedule strategy.…”

Section: The Basics Of Boolean Networkmentioning

confidence: 99%

“…This expansion method is closely related to the translation from [17], although the latter heavily relies on either read-arcs or inhibitory arcs to preserve the dynamics. Similarly, the unique prime implicant graph of a Boolean Network is a B-hypergraph [46,47], and can easily be translated into a Petri Net structure or into a graph with composite nodes. It should be further investigated how this translation preserves the dynamics.…”

Section: Comparing Different Formalisms: a Perspectivementioning

confidence: 99%

Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

Vieira¹,

Vera-Licona²

2018

Preprint

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Abstract. Background Different objects exist to describe how a signal transduces in a given intracellular signaling network, such as elementary signaling modes, T-invariants, extreme pathway analysis, elementary modes and simple paths. For modeling frameworks such as Boolean networks, Petri nets and hypergraphs, these signal transduction objects are broadly used in their respective frameworks but few studies have been done emphasizing how these signal transduction objects compare or relate to each other. Results We provide an overview of the different methodologies for capturing signal transduction in a given model of an intracellular signaling network. We show how minimal functional routes proposed for signaling networks modeled as Boolean networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of an acyclic B-hypergraph, the definitions are equivalent. Furthermore, we show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes, whereas in directed graphs, it has been shown that these computations of elementary modes correspond to computations of simple paths. Conclusions The different objects introduced in the literature to capture signal transduction are deeply related to each other, although they are based on different biological assumptions. Furthermore, methodology in metabolic networks and signaling networks are deeply related to each other.

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“…The notion of stability has been generalised from single states to sub-spaces of states Siebert (2011) with applications in model reduction and attractor detection. Like stable states, stable subspaces are independent of the updating policy and can be computed by constraint-solving methods Klarner et al (2014).…”

Section: Logical Modellingmentioning

confidence: 99%

Cooperative development of logical modelling standards and tools with CoLoMoTo

Naldi

Monteiro

Müssel

et al. 2014

Preprint

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The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour, and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Here, we outline the logical modelling framework and present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. This open community welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

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Computing Symbolic Steady States of Boolean Networks

Cited by 27 publications

References 9 publications

bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

bioLQM: a java library for the manipulation and conversion of Logical Qualitative Models of biological networks

Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

Cooperative development of logical modelling standards and tools with CoLoMoTo

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