"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

Arellano, Gustavo; Argil, Julián; Azpeitia, Eugenio; Benítez, Mariana; Carrillo, Miguel; Góngora, Pedro Arturo; Rosenblueth, David A.; Álvarez-Buylla, Elena

doi:10.1186/1471-2105-12-490

Cited by 35 publications

(22 citation statements)

References 70 publications

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“…Optimizing with Binary Decision Diagrams (BDDs) or more efficient algorithms could also allow for testing of more than one interaction. A way to simplify the use of our procedures is to incorporate them into an existing dynamic network analyzer (e.g., Arellano et al, 2011). Procedures that use information of the GRN topology or about the effect of how genes in the networks indirectly affect other genes should further reduce the number of BFs generated when we add putative missing interactions.…”

Section: Discussionmentioning

confidence: 99%

“…As observed, modeling the number of possibilities caused by additional components or links quickly becomes computationally intractable, even for such small networks using a simple Boolean formalism. Nevertheless, the dynamics of BNs with tens of nodes can be exhaustively analyzed in a relatively short amount of time, compared with other types of networks (e.g., Arellano et al, 2011). Thus, a methodology that allows for systematic integration and prediction of missing interactions in BNs would provide an instrumental tool in the proposal of a more complete RSCN GRN model and likely any other GRN.…”

Section: Introductionmentioning

confidence: 99%

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Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

Azpeitia

Weinstein²,

Benítez

et al. 2013

Front. Plant Sci.

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Over the last few decades, the Arabidopsis thaliana root stem cell niche (RSCN) has become a model system for the study of plant development and stem cell niche dynamics. Currently, many of the molecular mechanisms involved in RSCN maintenance and development have been described. A few years ago, we published a gene regulatory network (GRN) model integrating this information. This model suggested that there were missing components or interactions. Upon updating the model, the observed stable gene configurations of the RSCN could not be recovered, indicating that there are additional missing components or interactions in the model. In fact, due to the lack of experimental data, GRNs inferred from published data are usually incomplete. However, predicting the location and nature of the missing data is a not trivial task. Here, we propose a set of procedures for detecting and predicting missing interactions in Boolean networks. We used these procedures to predict putative missing interactions in the A. thaliana RSCN network model. Using our approach, we identified three necessary interactions to recover the reported gene activation configurations that have been experimentally uncovered for the different cell types within the RSCN: (1) a regulation of PHABULOSA to restrict its expression domain to the vascular cells, (2) a self-regulation of WOX5, possibly by an indirect mechanism through the auxin signaling pathway, and (3) a positive regulation of JACKDAW by MAGPIE. The procedures proposed here greatly reduce the number of possible Boolean functions that are biologically meaningful and experimentally testable and that do not contradict previous data. We believe that these procedures can be used on any Boolean network. However, because the procedures were designed for the specific case of the RSCN, formal demonstrations of the procedures should be shown in future efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

Azpeitia

Weinstein²,

Benítez

et al. 2013

Front. Plant Sci.

Self Cite

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“…There are several free software packages for the dynamic analysis of Boolean GRNs, including: ANTELOPE [ 64 ], GINSIM [ 65 ], BoolNet [ 60 ], GNbox [ 66 ], GNA [ 67 ], and BioCham [ 68 ].…”

Section: Notesmentioning

confidence: 99%

Descriptive vs. Mechanistic Network Models in Plant Development in the Post-Genomic Era

Dávila-Velderrain

Martínez-García

Álvarez-Buylla

2015

Methods in Molecular Biology

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Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

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“…Antelope (Analysis of Networks through TEmporal-LOgic sPEcifications) (Arellano et al, 2011) is a system developed by our team, aimed at Boolean networks. Antelope also uses model checking in a different way from other computational-biology tools.…”

Section: Specialized Toolsmentioning

confidence: 99%

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Carrillo¹,

Góngora²,

Rosenblueth³

2012

Front. Plant Sci.

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Model checking is a well-established technique for automatically verifying complex systems. Recently, model checkers have appeared in computer tools for the analysis of biochemical (and gene regulatory) networks. We survey several such tools to assess the potential of model checking in computational biology. Next, our overview focuses on direct applications of existing model checkers, as well as on algorithms for biochemical network analysis influenced by model checking, such as those using binary decision diagrams (BDDs) or Boolean-satisfiability solvers. We conclude with advantages and drawbacks of model checking for the analysis of biochemical networks.

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"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

Cited by 35 publications

References 70 publications

Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

Descriptive vs. Mechanistic Network Models in Plant Development in the Post-Genomic Era

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

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