ViSiBooL—visualization and simulation of Boolean networks with temporal constraints

Schwab, Julian; Burkovski, Andreas; Siegle, Lea; Müssel, Christoph; Kestler, Hans A.

doi:10.1093/bioinformatics/btw661

Cited by 21 publications

(13 citation statements)

References 27 publications

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“…The dynamics of Boolean networks are still an active research field, e.g. temporal extensions of synchronous Boolean networks that allow to express processes on different time scales [ 45 ], model-checking-based methods for attractor identification [ 46 ] or the identification of stable states and subspaces in Boolean networks [ 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Schwab

Siegle

Kühlwein

et al. 2017

Biology

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Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF-κB signaling is a crucial pathway in the process of aging. The NF-κB system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes—for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF-κB signaling in aged organisms in comparison to young ones.

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Section: Methodsmentioning

confidence: 99%

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Schwab

Siegle

Kühlwein

et al. 2017

Biology

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“…The Boolean network model for the IGF/Wnt crosstalk was derived from collecting literature data. Modeling of the network functions was performed using ViSiBooL [ 57 ]. The exhaustive attractor search simulation to analyze the network was carried out with the R-package BoolNet [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells

et al. 2018

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Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.

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“…From a modeling perspective, Boolean Networks are easier to set up than Petri Nets. Several tools exist to both model and analyze Boolean Networks [60,16,59,95,83,11,44].…”

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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ViSiBooL—visualization and simulation of Boolean networks with temporal constraints

Abstract: hans.kestler@uni-ulm.de.

Cited by 21 publications

References 27 publications

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Stability of Signaling Pathways during Aging—A Boolean Network Approach

A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells

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

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