An Improved Method for Finding Attractors of Large-Scale Asynchronous Boolean Networks

Giang, Trinh Van; Hiraishi, Kunihiko

doi:10.1109/cibcb49929.2021.9562947

Cited by 4 publications

(27 citation statements)

References 28 publications

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“…Besides performance improvements, it is possible to extend our framework to apply the new GRN decomposition 19,34 and sample strategies to scale for larger values of

n

. Furthermore, the integration of the accelerator with Amazon AWS cloud services offers the possibility to incorporate it in software applications.…”

Section: Discussionmentioning

confidence: 99%

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Gene regulatory accelerators on cloud FPGA

Penha

Bragança

Canesche

et al. 2023

Concurrency and Computation

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SummaryGene regulatory networks (GRN) are dynamic models in time and space. These models are used to predict diseases and in drugs research. GRN models are discrete, and Boolean graphs can efficiently represent them. However, GRN algorithms explore a large solution space with high computational complexity. This work proposes efficient FPGA‐based accelerators to implement two GRN algorithms: attractor computation and Derrida plot. Nevertheless, FPGA accelerator design and deployment are still a challenge. This work presents an accelerator design framework for AWS Amazon FPGA cloud. The framework simplifies the software (SW) and hardware (HW) generation for GRN accelerators. The user provides a high‐level model for the Boolean GRN, and our tool automatically creates AWS‐ready‐to‐deploy software and hardware components. For the attractor and the Derrida plot computation, the proposed FPGA accelerators are on average and faster than a V100 GPU.

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“…Besides performance improvements, it is possible to extend our framework to apply the new GRN decomposition 19,34 and sample strategies to scale for larger values of

n

. Furthermore, the integration of the accelerator with Amazon AWS cloud services offers the possibility to incorporate it in software applications.…”

Section: Discussionmentioning

confidence: 99%

“…First, it requires efficient software and hardware implementations. Second, recent GRN decomposition strategies 19,34 should be applied to handle large networks (greater than 20 nodes). Third, efficient sample approaches reduce the solution space.…”

Section: Derrida Plotmentioning

confidence: 99%

Gene regulatory accelerators on cloud FPGA

Penha

Bragança

Canesche

et al. 2023

Concurrency and Computation

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“…Historically, much of the Boolean network design and analysis has been performed either manually, or through computer assisted approaches requiring extensive involvement of domain experts [2][3][4] . However, recent developments in topics like attractor [5][6][7] and trap space 8 detection, bifurcation analysis 9 , and synthesis from data and prior knowledge 10,11 enable comprehensive automated analysis of large, possibly partially unknown networks comprising hundreds of components. This proliferation of Boolean network tools and methods motivates the need for extensive and reproducible validation using realistic real-world models.…”

Section: Background and Summarymentioning

confidence: 99%

Repository of logically consistent real-world Boolean network models

Pastva

Šafránek

Beneš

et al. 2023

Preprint

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Recent developments in both computational analysis and data-driven synthesis enable a new era of automated reasoning with logical models (Boolean networks in particular) in systems biology. However, these advancements also motivate an increased focus on quality control and performance comparisons between tools. At the moment, to illustrate real-world applicability, authors typically test their approaches on small sets of manually curated models that are inherently limited in scope. This further complicates reuse and comparisons, because benchmark models often contain ad hoc modifications or are outright not available. In this paper, we describe a new, comprehensive, open source dataset of 210+ Boolean network models compiled from available databases and a literature survey. The models are available in a wide range of formats. Furthermore, the dataset is accompanied by a validation pipeline that ensures the integrity and logical consistency of each model. Using this pipeline, we identified and repaired 400+ potential problems in a number of widely used models.

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“…Recently, an efficient method called iFVS-ABN [17] has been proposed for exactly computing all the attractors of an ABN. iFVS-ABN first computes a Negative Feedback Vertex Set (NFVS) of the interaction graph of the ABN that is a signed directed graph that expresses the effects (positive or negative) among the nodes.…”

mentioning

confidence: 99%

“…The approach of iFVS-ABN seems to be very promising and its prototype implementation significantly outperforms the previous methods including genYsis [12], CABEAN [26], and FVS-ABN [46] (the predecessor of iFVS-ABN). However, the crucial issue of iFVS-ABN is that it still must perform the reachability analysis in most cases [17]. Since the reachability in ABNs is PSPACE-complete [9] and there is no reachability analysis method that is robustly efficient for large models [17], the issue may drastically reduce the efficiency of iFVS-ABN.…”

mentioning

confidence: 99%

Computing attractors of large-scale asynchronous boolean networks using minimal trap spaces

Trinh

Hiraishi

Benhamou

2022

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Boolean Networks (BNs) play a crucial role in modeling, analyzing, and controlling biological systems. One of the most important problems on BNs is to compute all the possible attractors of a BN. There are two popular types of BNs, Synchronous BNs (SBNs) and Asynchronous BNs (ABNs). Although ABNs are considered more suitable than SBNs in modeling real-world biological systems, their attractor computation is more challenging than that of SBNs. Several methods have been proposed for computing attractors of ABNs. However, none of them can robustly handle large and complex models. In this paper, we propose a novel method called mtsNFVS for exactly computing all the attractors of an ABN based on its minimal trap spaces, where a trap space is a subspace of state space that no path can leave. The main advantage of mtsNFVS lies in opening the chance to reach easy cases for the attractor computation. We then evaluate mtsNFVS on a set of large and complex real-world models with crucial biologically motivations as well as a set of randomly generated models. The experimental results show that mtsNFVS can easily handle large-scale models and it completely outperforms the state-of-the-art method CABEAN as well as other recently notable methods.

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An Improved Method for Finding Attractors of Large-Scale Asynchronous Boolean Networks

Cited by 4 publications

References 28 publications

Gene regulatory accelerators on cloud FPGA

Gene regulatory accelerators on cloud FPGA

Repository of logically consistent real-world Boolean network models

Computing attractors of large-scale asynchronous boolean networks using minimal trap spaces

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