Mapping multivalued onto Boolean dynamics

Didier, Gilles; Rémy, Élisabeth; Chaouiya, Claudine

doi:10.1016/j.jtbi.2010.09.017

Cited by 38 publications

(48 citation statements)

References 28 publications

(25 reference statements)

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“…If, for example, experimental evidence suggests the existence of more than two gene activity states, nodes taking additional activity states could be used (e.g., Espinosa-Soto et al, 2004; Benítez et al, 2008; Didier et al, 2011). If non-determinism is not important for the system under study, probabilistic networks can be used (e.g., Shmulevich and Kauffman, 2004; Bornholdt, 2008).…”

Section: Epistasis and (Boolean) Networkmentioning

confidence: 99%

“…Importantly, Boolean networks can be modified for more detailed analysis when noise (Bornholdt, 2008) or multivalued genes (genes that can perform different activities depending on their level of expression; Didier et al, 2011) are considered. Furthermore, once a Boolean network is validated it can be transformed into an equivalent continuous system (Wittmann et al, 2009), which can be amenable to further formal analyses.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic network-based epistasis analysis: Boolean examples

et al. 2011

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In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and single-path assumption, but also by demonstrating the importance of considering temporal dynamics, and specifically introducing the usefulness of Boolean network models and also reviewing some key properties of network approaches.

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Section: Epistasis and (Boolean) Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic network-based epistasis analysis: Boolean examples

et al. 2011

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“…Although multistate systems can be converted into Boolean networks [10], the conversion adds new artificial nodes, and thus models might lose their intuitiveness or become more computationally expensive to analyze. In this manuscript, we have presented a method for extending mixed-state networks into a multistate system in a natural way that preserves steady states.…”

Section: Discussionmentioning

confidence: 99%

Control of Intracellular Molecular Networks Using Algebraic Methods

Vieira

Laubenbacher²,

Murrugarra

2019

Preprint

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Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, such as happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structurebased approaches have been developed, as well as stable motif techniques.

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“…This model Booleanization step is based on the Boolean mapping discussed by Didier, Remy, and Chaouiya [9].…”

Section: Boolean Mapping Of Multi-valued Modelsmentioning

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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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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Mapping multivalued onto Boolean dynamics

Cited by 38 publications

References 28 publications

Dynamic network-based epistasis analysis: Boolean examples

Dynamic network-based epistasis analysis: Boolean examples

Control of Intracellular Molecular Networks Using Algebraic Methods

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

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