Refining Dynamics of Gene Regulatory Networks in a Stochastic π-Calculus Framework

Paulevé, Loı̈c; Magnin, Morgan; Roux, Olivier H.

doi:10.1007/978-3-642-19748-2_8

Cited by 20 publications

(43 citation statements)

References 22 publications

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“…As described in [10], the cooperation between processes to make another bounce can be expressed in PH by building a cooperative sort. Fig.…”

Section: Represents a Ph (σ L H) Withmentioning

confidence: 99%

“…While the construction of cooperation in PH allows to encode any boolean functions between cooperating processes [10], it is worth noticing they introduce a temporal shift in their application. This allows the existence of interleaving of actions leading to a cooperative sort representing a past sub-state of the presence of the cooperative processes.…”

Section: P Denotes the Set Of All Processes (Pmentioning

confidence: 99%

“…Finally, it must be noticed that we are not interested in this paper in the derivation of one PH from a BRN (which was previously described in [10]) but, on the contrary, to finding out a set of BRNs from one PH.…”

Section: Introductionmentioning

confidence: 99%

“…In order to address the formal checking of dynamical properties within very large BRNs, we recently introduced in [10] a new formalism, named the "Process Hitting" (PH), to model concurrent systems having components with a few qualitative levels. A PH describes, in an atomic manner, the possible evolutions of a process (representing one component at one level) triggered by the hit of at most one other process in the system.…”

Section: Introductionmentioning

confidence: 99%

“…The first benefit is that such an approach makes it possible to refine the construction of BRNs with a partial and progressively brought knowledge in PH, while being able to export such models in the Thomas' framework. The second feature of our method is that it can be applied on very large BRNs.Finally, it must be noticed that we are not interested in this paper in the derivation of one PH from a BRN (which was previously described in [10]) but, on the contrary, to finding out a set of BRNs from one PH.Our work is related to the approach of [13] which relies on temporal logic, and [14,15] which also uses constraint programming. Both aim at determining a class of models which are consistent with available partial data on the regulatory structure and dynamical properties.…”

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confidence: 99%

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Concretizing the Process Hitting into Biological Regulatory Networks

Folschette

Paulevé

Inoue

et al. 2012

Computational Methods in Systems Biology

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Abstract. The Process Hitting (PH) is a recently introduced framework to model concurrent processes. Its major originality lies in a specific restriction on the causality of actions, which makes the formal analysis of very large systems tractable. PH is suitable to model Biological Regulatory Networks (BRNs) with complete or partial knowledge of cooperations between regulators by defining the most permissive dynamics with respect to these constraints.On the other hand, the qualitative modeling of BRNs has been widely addressed using René Thomas' formalism, leading to numerous theoretical work and practical tools to understand emerging behaviors.Given a PH model of a BRN, we first tackle the inference of the underlying Interaction Graph between components. Then the inference of corresponding Thomas' models is provided using Answer Set Programming, which allows notably an efficient enumeration of (possibly numerous) compatible parametrizations.In addition to giving a formal link between different approaches for qualitative BRNs modeling, this work emphasizes the ability of PH to deal with large BRNs with incomplete knowledge on cooperations, where Thomas' approach fails because of the combinatorics of parameters. IntroductionAs regulatory phenomena play a crucial role in biological systems, they need to be studied accurately. Biological Regulatory Networks (BRNs) consist in sets of either positive or negative mutual effects between the components. With the purpose of analyzing these systems, they are often modeled as graphs which make it possible to determine the possible evolutions of all the interacting components of the system. Indeed, besides continuous models of physicists, often designed through systems of ordinary differential equations, a discrete modeling approach was initiated by René Thomas in 1973 [1].In this approach, the different levels of a component, such as concentration or expression levels, are abstractly represented by (positive) integer values and transitions between these levels may be considered as instantaneous. Hence, qualitative state graphs may be derived from which we are able to formally find out all the possible behaviors expressed as sequences of transitions between these states. Nevertheless, these dynamics can be precisely established only with regard to some discrete parameters which stand for a kind of "focal points", i.e. the evolutionary tendency from each state and depending of the set of resources in this very state, that is, the set of the other currently interacting components. Hereafter, we refer to these discrete parameters as Thomas' parameters.Thomas' modeling has motivated numerous works around the link between the Interaction Graph (IG) (summarizing the global influences between components) and the possible dynamics (e.g., [2,3]), model reduction (e.g., [4]), formal checking of dynamics (e.g., [5,6]), and the incorporation of time (e.g., [7,8]) and probability (e.g., [9]) dimensions, to name but a few. While the formal checking of dynamical properties is often limite...

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“…As described in [10], the cooperation between processes to make another bounce can be expressed in PH by building a cooperative sort. Fig.…”

Section: Represents a Ph (σ L H) Withmentioning

confidence: 99%

Section: P Denotes the Set Of All Processes (Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Concretizing the Process Hitting into Biological Regulatory Networks

Folschette

Paulevé

Inoue

et al. 2012

Computational Methods in Systems Biology

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Analyzing Large Network Dynamics with Process Hitting

Paulevé¹,

Chancellor²,

Folschette³

et al. 2014

Logical Modeling of Biological Systems

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The modeling challenge Regulation is a key aspect of biological systems, all the way from the molecular scale to the ecological one. Gaining a precise understanding of regulation is one of the main goals of systems biology. This discipline has emerged from the synergy between cell biology and cybernetics [WIE 48], from the collaboration of biologists, physicists and computer scientists [IDE 01]. The modeling approach presented in this chapter derives from this heritage by studying the interactions of components of a biological system and analyzing how these interactions impact the function and behavior of the system as a whole. Here, we will focus on applications to genetics, but the potential field of the approach is broader: the Process Hitting framework is relevant to any interactive system, whether it is a biological regulatory network, a logistic scheme or an embedded system. The recent progress in molecular biology has made it possible to obtain a comprehensive map of the genomes of many living organisms. Simultaneously, the development of DNA micro array technology has given access to time series data of the expression of several thousands of genes. One of the main challenges now is to Chapter written by Loïc PAULEVÉ and Courtney CHANCELLOR and Maxime FOLSCHETTE and Morgan MAGNIN and Olivier ROUX.

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Analyzing Long‐Term Dynamics of Biological Networks With Answer Set Programming

Abdallah

Folschette

Magnin

2022

Systems Biology Modelling and Analysis

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Merits of ASP for biological systems analysis: The formal study of the dynamics of biological systems raises many problems, e.g., identification of attractors, bifurcations, reachability, that are combinatorial by essence. Making these issues tractable requires to design efficient methods that rely on solid and efficient programming frameworks. During the last decades, Answer Set Programming (ASP) [Baral, 2003] has proven to be a strong logic programming paradigm to deal with the inherent complexity of the biological models, allowing to quickly investigate a wide range of configurations. ASP can efficiently enumerate a large number of answer sets, as well as easily filter the results thanks to constraints based on certain properties. This chapter begins by a motivation of the merits of ASP in biological studies based on a 1 state of the art. The basic concepts about ASP and its use in systems biology are then introduced. After having given an overview of the different issues that can be tackled using ASP, the focus is turned onto one problem that is of critical importance: model-checking with ASP, and more specifically the identification of attractors. The merits of such an approach are then exhibited using case studies. This chapter ends on a summary of the promising perspectives that ASP holds in future works about biological dynamics.

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Refining Dynamics of Gene Regulatory Networks in a Stochastic π-Calculus Framework

Cited by 20 publications

References 22 publications

Concretizing the Process Hitting into Biological Regulatory Networks

Concretizing the Process Hitting into Biological Regulatory Networks

Analyzing Large Network Dynamics with Process Hitting

Analyzing Long‐Term Dynamics of Biological Networks With Answer Set Programming

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