An efficient algorithm for finding attractors in synchronous Boolean networks with biochemical applications

Zheng, Dongqi; Yang, Gang; Li, X.; Wang, Z.; Hung, William N. N.

doi:10.4238/2013.october.18.4

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…In the first computational experiment, a comparison of based on these approaches computations were carried out for the problem of searching for equilibrium states in autonomous synchronous Boolean networks (models of gene regulatory networks -the GRN, most often used in testing [4,7]) shown in table 1.…”

Section: Experimental Studymentioning

confidence: 99%

“…Synchronous update (a free version provided on https://people.kth.se/~dubrova/bns.html). This well-known solver is still used for comparison with new analogous software tools developed in recent years [6,7]. The input format of BDS dynamics description (1) for this solver is CNET format.…”

Section: Network Withmentioning

confidence: 99%

“…Existing software for qualitative analysis of autonomous synchronous BDS (also called Boolean network) [4][5][6][7] is mainly intended only for searching attractors. It is noted in [8] that these tools have several drawbacks, in particular, they are limited by the complexity of the Boolean model and the format of its presentation, require programming skills from subject specialists, since they are often used only as command line tools depending on the platform.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automation of microservices creation for qualitative analysis of binary dynamic systems

Oparin¹,

Bogdanova²,

Pashinin³

2019

The International Workshop on Information, Computation, and Control Systems for Distributed Environments 2019

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The main objective of qualitative research is to analyze the behavior of the trajectories of a dynamic system to verify whether it corresponds to the set of constraints characterizing the property. We use an approach to study binary dynamic systems on a finite time interval based on the author's method of Boolean constraints. Based on this method, the Boolean model of the properties of a binary dynamic system is written in the language of Boolean equations or Boolean formulas with quantifiers. Thus, the verification of various dynamical properties is reduced to solving the problems of Boolean constraints satisfiability or the validity of a quantified Boolean formula using efficient SAT or TQBF solvers. The high computational complexity of these problems requires the development of software and tools for their parallel and distributed solving and ensuring transparent end-user access to highperformance computing environments based on a service-oriented approach. This paper represents the architecture and functionality of a new instrumental system that automates the creation of a distributed application for solving the considered class of problems based on the microservice approach and multi-agent technology.

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Section: Experimental Studymentioning

confidence: 99%

Section: Network Withmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automation of microservices creation for qualitative analysis of binary dynamic systems

Oparin¹,

Bogdanova²,

Pashinin³

2019

The International Workshop on Information, Computation, and Control Systems for Distributed Environments 2019

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“…In order to identify all attractors of the genetic regulatory network, so far several methods have been proposed, including the methods relying on binary decision diagrams [26, 27], constraint programming [28], feedback vertex sets [29, 30], linear mapping [31]. Moreover, many of these methods have been developed to be more general and effective [32–34]. …”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Finding the Singleton Attractors and Pre-Images in Strong-Inhibition Boolean Networks

Zhan

Shuai

et al. 2016

PLoS ONE

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The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.

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“…Helikar and Rogers [9 ] developed the simulation platform ChemicalChains and Müssel et al [10 ] developed BoolNet. Zheng et al [11, 12 ] combined iterative methods with reduced order BDDs to identify attractors in synchronous BNs. In addition, the attractors of synchronous BNs are used in asynchronous Boolean translation functions to derive attractors of asynchronous scenarios.…”

Section: Introductionmentioning

confidence: 99%

Detecting small attractors of large Boolean networks by function‐reduction‐based strategy

et al. 2016

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Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.

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An efficient algorithm for finding attractors in synchronous Boolean networks with biochemical applications

Cited by 4 publications

References 20 publications

Automation of microservices creation for qualitative analysis of binary dynamic systems

Automation of microservices creation for qualitative analysis of binary dynamic systems

An Algorithm for Finding the Singleton Attractors and Pre-Images in Strong-Inhibition Boolean Networks

Detecting small attractors of large Boolean networks by function‐reduction‐based strategy

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