Sufficient conditions for reachability in automata networks with priorities

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

doi:10.1016/j.tcs.2015.08.040

Cited by 16 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reachability verification is a PSPACE-complete problem and its resolution often explodes on large networks. Pint implements over-and under-approximation of reachability [21,9] which allow tackling large models, although being potentially inconclusive when the over-approximation is satisfied but not the under-approximation. In such cases, one should fall back to classical model-checking.…”

Section: Main Features and Benchmarksmentioning

confidence: 99%

Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

Paulevé

2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. The software Pint is devoted to the scalable analysis of the traces of automata networks, which encompass Boolean and discrete networks. Pint implements formal approximations of transient reachabilityrelated properties, including mutation prediction and model reduction. Pint is distributed with command line tools, as well as a Python module pypint. The latter provides a seamless integration with the Jupyter IPython notebook web interface, which allows to easily save, reuse, reproduce, and share workflows of model analysis. Pint can address networks with hundreds to thousands interacting components, which are typically intractable with standard approaches.

show abstract

Section: Main Features and Benchmarksmentioning

confidence: 99%

Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

Paulevé

2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Definition 1 introduces the formalism of automata networks (AN) [ 6 ] (see Fig. 1 ) which allows to model a finite number of discrete levels, called local states , into several automata .…”

Section: Preliminary Definitionsmentioning

confidence: 99%

“…These models include neural networks, differential equations, Petri nets, Boolean networks (BN) as proposed by Kauffman [ 5 ], probabilistic Boolean networks, and other multi-valued models such synchronous/asynchronous automata networks (AN). In this paper, we use the AN formalism [ 6 , 7 ] to model BRNs. ANs especially encompass the framework of René Thomas [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks

Abdallah

Folschette

Roux

et al. 2017

Algorithms Mol Biol

Self Cite

View full text Add to dashboard Cite

BackgroundThis paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors.ResultsWe present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature.ConclusionThe originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

show abstract

“…Signatures can be computed according to gene set enrichment analysis and gathered in databases such as SigDB [18]. They can also be computed to focus on causality effects if logical networks are available [27,17,11]. However, most of the time, signatures are refined manually by clinicians or biological experts in order to be more accurate and discriminate cell-types according to a few biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Formalizing and enriching phenotype signatures using Boolean networks

Wery

Dameron

Nicolas

et al. 2019

Journal of Theoretical Biology

View full text Add to dashboard Cite

In order to predict the behavior of a biological system, one common approach is to perform a simulation on a dynamic model. Boolean networks allow to analyze the qualitative aspects of the model by identifying its steady states and attractors. Each of them, when possible, is associated with a phenotype which conveys a biological interpretation. Phenotypes are characterized by their signatures, provided by domain experts. The number of steady states tends to increase with the network size and the number of simulation conditions, which makes the biological interpretation difficult. As a first step, we explore the use of Formal Concept Analysis as a symbolic bi-clustering technics to classify and sort the steady states of a Boolean network according to biological signatures based on the hierarchy of the roles the network components play in the phenotypes. FCA generates a lattice structure describing the dependencies between proteins in the signature and steady-states of the Boolean network. We use this lattice (i) to enrich the biological signatures according to the dependencies carried by the network dynamics, (ii) to identify variants to the phenotypes and (iii) to characterize hybrid phenotypes. We applied our approach on a T helper lymphocyte (Th) differentiation network with a set of signatures corresponding to the sub-types of Th. Our method generated the same classification as a manual analysis performed by experts in the field, and was also able to work under extended simulation conditions. This led to the identification and prediction of a new hybrid sub-type later confirmed by the literature.

show abstract

Sufficient conditions for reachability in automata networks with priorities

Cited by 16 publications

References 22 publications

Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks

Formalizing and enriching phenotype signatures using Boolean networks

Contact Info

Product

Resources

About