Inference of Boolean Networks from Gene Interaction Graphs Using a SAT Solver

Rosenblueth, David A.; Muñoz, Stalin; Carrillo, Miguel; Azpeitia, Eugenio

doi:10.1007/978-3-319-07953-0_19

Cited by 18 publications

(18 citation statements)

References 19 publications

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“…The update rules of our model of the cell cycle module (Equations 7-14) are not ambiguous. Without allowing ambiguity in the update rules, we found with Griffin a [ 73 ] 120 functional network topologies of the cell cycle module that allow the existence of the cyclic attractor. All such topologies include the following 14 interactions: a ) the inhibition of EFL-1 by LIN-35, b ) the activation of APC by CDK-1/CYB-3, c ) the inhibition of LIN-35 by CDK-4/CYD-1, d ) the activation of SCF by CDK-2/CYE-1, e ) the inhibition of CDK-2/CYE-1 by SCF, f ) the inhibition of CDK-4/CYD-1 by SCF, g ) the inhibition of CDK-2/CYE-1 by LIN-35, h ) the activation of CDK-1/CYB-3 by EFL-1, i ) the inhibition of CDK-4/CYD-1 by CKI-1, j ) the activation of CKI-1 by APC, k ) the activation of CKI-1 by CDK-1/CYB-3, l ) the inhibition of CDK-1/CYB-3 by CDK-4/CYD-1, m ) the inhibition of CDK-4/CYD-1 by CDK-1/CYB-3, and n ) the activation of CDK-2/CYE-1 by EFL-1.…”

Section: Resultsmentioning

confidence: 99%

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

et al. 2015

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BackgroundThere are recent experimental reports on the cross-regulation between molecules involved in the control of the cell cycle and the differentiation of the vulval precursor cells (VPCs) of Caenorhabditis elegans. Such discoveries provide novel clues on how the molecular mechanisms involved in the cell cycle and cell differentiation processes are coordinated during vulval development. Dynamic computational models are helpful to understand the integrated regulatory mechanisms affecting these cellular processes.ResultsHere we propose a simplified model of the regulatory network that includes sufficient molecules involved in the control of both the cell cycle and cell differentiation in the C. elegans vulva to recover their dynamic behavior. We first infer both the topology and the update rules of the cell cycle module from an expected time series. Next, we use a symbolic algorithmic approach to find which interactions must be included in the regulatory network. Finally, we use a continuous-time version of the update rules for the cell cycle module to validate the cyclic behavior of the network, as well as to rule out the presence of potential artifacts due to the synchronous updating of the discrete model. We analyze the dynamical behavior of the model for the wild type and several mutants, finding that most of the results are consistent with published experimental results.ConclusionsOur model shows that the regulation of Notch signaling by the cell cycle preserves the potential of the VPCs and the three vulval fates to differentiate and de-differentiate, allowing them to remain completely responsive to the concentration of LIN-3 and lateral signal in the extracellular microenvironment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0498-z) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

et al. 2015

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“…Boolean networks [8,9] are a widely used qualitative modelling approach for biological control systems (see for example [1,13,11,10,3]). In this section we introduce the basic denitions for Boolean networks needed in the sequel and provide illustrative examples.…”

Section: Boolean Networkmentioning

confidence: 99%

“…Despite their simplicity, Boolean networks have been shown to allow a range of interesting biological analysis to be performed and have been widely considered in the literature (for example, see [1,13,11,10,3]). Indeed, it can be seen that they have an important role to play in advancing our understanding and engineering capability of complex biological systems.…”

Section: Introductionmentioning

confidence: 99%

A Formal Framework for Composing Qualitative Models of Biological Systems

Alkhudhayr

Steggles

2017

Theory and Practice of Natural Computing

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“…Necessary conditions for multi-stability (cell differentiation) and oscillations have been given in terms of positive or negative circuits in the influence graph [7], [8]. Several tools such as GINsim [9], [10], GNA [11] or Griffin [12], use these properties and powerful graph-theoretic and model-checking techniques to automate reasoning about the Boolean state transition graph, compute attractors and verify various reachability and path properties. The representation of Boolean influence networks by Petri nets was described in [13] but leads to complicated encodings.…”

Section: Introductionmentioning

confidence: 99%

Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors

Fages

Martinez

Rosenblueth

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Biochemical reaction networks are one of the most widely used formalisms in systems biology to describe the molecular mechanisms of high-level cell processes. However, modellers also reason with influence diagrams to represent the positive and negative influences between molecular species and may find an influence network useful in the process of building a reaction network. In this paper, we introduce a formalism of influence networks with forces, and equip it with a hierarchy of Boolean, Petri net, stochastic and differential semantics, similarly to reaction networks with rates. We show that the expressive power of influence networks is the same as that of reaction networks under the differential semantics, but weaker under the discrete semantics. Furthermore, the hierarchy of semantics leads us to consider a (positive) Boolean semantics that cannot test the absence of a species, that we compare with the (negative) Boolean semantics with test for absence of a species in gene regulatory networks à la Thomas. We study the monotonicity properties of the positive semantics and derive from them an algorithm to compute attractors in both the positive and negative Boolean semantics. We illustrate our results on models of the literature about the p53/Mdm2 DNA damage repair system, the circadian clock, and the influence of MAPK signaling on cell-fate decision in urinary bladder cancer.

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Inference of Boolean Networks from Gene Interaction Graphs Using a SAT Solver

Cited by 18 publications

References 19 publications

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

A Formal Framework for Composing Qualitative Models of Biological Systems

Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors

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