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

Abdallah, Emna Ben; Folschette, Maxime; Roux, Olivier; Magnin, Morgan

doi:10.1186/s13015-017-0111-2

Cited by 11 publications

(12 citation statements)

References 44 publications

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“…Therefore, it would be worthy to study these directions. Although we have considered synchronous BNs in this work, various models of asynchronous BNs have been proposed and studied [ 19 , 20 ]. Unfortunately, our approach cannot be applied to such BNs because trajectories are not uniquely determined.…”

Section: Discussionmentioning

confidence: 99%

“…It is worthy to mention that many practically efficient methods have been developed for detection and/or enumeration of attractors using such techniques as logic programming [ 18 ], SAT solvers [ 12 ], binary decision diagrams [ 19 ], and answer set programming [ 20 ], as well as representation/approximation of complex attractors through stable motifs [ 21 ] (equivalently, symbolic steady states [ 22 ] and trap spaces [ 13 ]). Although these methods are practically very useful, there is no theoretical guarantee better than O (2 n ) on either worst-case or expected time complexity.…”

Section: Introductionmentioning

confidence: 99%

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Identification of periodic attractors in Boolean networks using a priori information

et al. 2022

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Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of periodic attractors in Boolean networks using a priori information

et al. 2022

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“…The second main challenge -which is explored in details in this chapter -consists in the identification of attractors in biological regulator networks. In [Ben Abdallah et al, 2017], the authors proposed a first version of the approach and algorithms that will be explained in more details below. Meanwhile, other authors got interest in such a problem.…”

Section: Biologymentioning

confidence: 99%

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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“…For other logic-based approaches, Devloo et al developed an algorithm applying constraint programming to the singleton attractor detection and enumeration [65] . Inoue provided an algorithm that directly encodes a BN into a logic program and computes a singleton attractor based on that logic program [66] , and Abdallah et al proposed an algorithm based on answer set programming (ASP) that enumerates all attractors without creating an entire state transition diagram [67] . In addition to SAT, Binary decision diagrams (BDDs) have also been utilized to solve large scale logic-based problems in various fields.…”

Section: Practical Algorithmsmentioning

confidence: 99%