論文の公開元へ

書き出し

Refer/BibIX

RIS

BibTeX

TSV

Identification of periodic attractors in Boolean networks using a priori information

Münzner, Ulrike Mori, Tomoya Krantz, Marcus Klipp, Edda Akutsu, Tatsuya 京都大学 DOI:10.1371/journal.pcbi.1009702

2022.01

概要

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.

論文の公開元へ

この論文で使われている画像

参考文献

Kauffman S. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theoret Biol.

2010; 22:437–467. https://doi.org/10.1016/0022-5193(69)90015-0

Cheng D, Qi H, Zhao Y. Analysis and Control of Boolean Networks: A Semi-tensor Product Approach.

London,UK: Springer-Verlag; 2011.

Akutsu T. Algorithms for Analysis, Inference, and Control of Boolean Networks. Singapore: World Scientific; 2018.

Anthony M. Discrete Mathematics of Neural Networks, Selected Topics. Philadelphia, PA, USA: SIAM;

2001.

Saadatpour A, Wang RS, Liao A, Liu X, Loughran TP, Albert I, et al. Dynamical and Structural Analysis

of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia. PLOS Comput Biol. 2011; 7(11):1–15. https://doi.org/10.1371/journal.pcbi.1002267

Abou-Jaoude´ W, Traynard P, Monteiro PT, Saez-Rodriguez J, Helikar T, Thieffry D, et al. Logical

Modeling and Dynamical Analysis of Cellular Networks. Frontiers in Genetics. 2016; 7:94. https://doi.

org/10.3389/fgene.2016.00094 PMID: 27303434

Liu R, Lu J, Liu Y, Cao J, Wu ZG. Delayed Feedback Control for Stabilization of Boolean Control Networks With State Delay. IEEE Trans Neural Netw Learn Syst. 2018; 29(7):3283–3288. PMID:

28650826

Wang Y, Shen H, Duan D. On Stabilization of Quantized Sampled-Data Neural-Network-Based Control

Systems. IEEE Trans Cybern. 2017; 47(10):3124–3135. https://doi.org/10.1109/TCYB.2016.2581220

PMID: 27362992

Weinstein N, Mendoza L, Gitler I, Klapp J. A Network Model to Explore the Effect of the Micro-environment on Endothelial Cell Behavior during Angiogenesis. Frontiers in Physiology. 2017; 8:960. https://

doi.org/10.3389/fphys.2017.00960 PMID: 29230182

10.

Saadatpour A, Albert R, Reluga T. A Reduction Method for Boolean Network Models Proven to Conserve Attractors. SIAM J Appl Dynamical Systems. 2013; 12(4):1997–2011. https://doi.org/10.1137/

13090537X PMID: 33132767

PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1009702 January 14, 2022

26 / 27

A Self-archived copy in

Kyoto University Research Information Repository

https://repository.kulib.kyoto-u.ac.jp

PLOS COMPUTATIONAL BIOLOGY

Identifying periodic attractors in Boolean networks

11.

Veliz-Cuba A. Reduction of Boolean network models. J Theoret Biol. 2011; 289:167–172. https://doi.

org/10.1016/j.jtbi.2011.08.042 PMID: 21907211

12.

Dubrova E, Teslenko M. A SAT-Based Algorithm for Finding Attractors in Synchronous Boolean Networks. IEEE/ACM Trans Comput Biol Bioinform. 2011; 8(5):1393–1399. https://doi.org/10.1109/TCBB.

2010.20 PMID: 21778527

13.

Klarner H, Siebert H. Approximating Attractors of Boolean Networks by Iterative CTL Model Checking.

Frontiers in Bioeng Biotech. 2015; 3:130. https://doi.org/10.3389/fbioe.2015.00130 PMID: 26442247

14.

Aracena J. On the Number of Fixed Points in Regulatory Boolean Networks. Bull Math Biol. 2008;

70:1398–1409. https://doi.org/10.1007/s11538-008-9304-7 PMID: 18306974

15.

Goles E, Salinas L. Sequential Operator for Filtering Cycles in Boolean Networks. Adv Appl Math. 2010;

45:346–358. https://doi.org/10.1016/j.aam.2010.03.002

16.

Melkman AA, Tamura T, Akutsu T. Determining a singleton attractor of an AND/OR Boolean network in

O(1.587n) time. Inform Proc Lett. 2010; 110(14-15):565–569. https://doi.org/10.1016/j.ipl.2010.05.001

17.

Akutsu T, Kosub S, Melkman AA, Tamura T. Finding a Periodic Attractor of a Boolean Network. IEEE/

ACM Trans Comput Biol Bioinform. 2012; 9(5):1410–1421. https://doi.org/10.1109/TCBB.2012.87

PMID: 22689081

18.

Inoue K. Logic Programming for Boolean Networks. In: Proceedings of the Twenty-Second International

Joint Conference on Artificial Intelligence—Volume Two. IJCAI’11. AAAI Press; 2011. p. 924–930.

19.

Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G. Synchronous versus asynchronous modeling

of gene regulatory networks. Bioinformatics. 2008; 24(17):1917–1925. https://doi.org/10.1093/

bioinformatics/btn336 PMID: 18614585

20.

Abdallah E, Folschette M, Roux O, Magnin M. ASP-based method for the enumeration of attractors in

non-deterministic synchronous and asynchronous multi-valued networks. Algorithms for Molecular Biology. 2017; 12. https://doi.org/10.1186/s13015-017-0111-2 PMID: 28814968

21.

Zañudo JGT, Albert R. An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2013; 23(2):025111.

https://doi.org/10.1063/1.4809777 PMID: 23822509

22.

Klarner H, Bockmayr A, Siebert H. Computing Symbolic Steady States of Boolean Networks. In: Cellular Automata. Cham: Springer International Publishing; 2014. p. 561–570.

23.

Cheng D, Qi H. A Linear Representation of Dynamics of Boolean Networks. IEEE Trans Autom Control.

2010; 55(10):2251–2258. https://doi.org/10.1109/TAC.2010.2043294

24.

Liu Y, Sun L, Lu J, Liang J. Feedback Controller Design for the Synchronization of Boolean Control Networks. IEEE Trans Neural Netw Learn Syst. 2016; 27(9):1991–1996. https://doi.org/10.1109/TNNLS.

2015.2461012 PMID: 26316221

25.

Zhao Y, Ghosh BK, Cheng D. Control of Large-Scale Boolean Networks via Network Aggregation. IEEE

Trans Neural Netw Learn Syst. 2016; 27(7):1527–1536. https://doi.org/10.1109/TNNLS.2015.2442593

PMID: 26259249

26.

Potente M, Gerhardt H, Carmeliet P. Basic and Therapeutic Aspects of Angiogenesis. Cell. 2011; 146

(6):873–887. https://doi.org/10.1016/j.cell.2011.08.039 PMID: 21925313

27.

Mu¨ssel C, Hopfensitz M, Kestler HA. BoolNet–an R package for generation, reconstruction and analysis

of Boolean networks. Bioinformatics. 2010; 26(10):1378–1380. https://doi.org/10.1093/bioinformatics/

btq124 PMID: 20378558

28.

Mu¨nzner U, Klipp E, Krantz M. A comprehensive, mechanistically detailed, and executable model of the

cell division cycle in Saccharomyces cerevisiae. Nat Comm. 2019; 10(1):1308. https://doi.org/10.1038/

s41467-019-08903-w PMID: 30899000

29.

Romers J, Thieme S, Mu¨nzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl. 2020; 6(2).

https://doi.org/10.1038/s41540-019-0120-5 PMID: 31934349

30.

Romers JC, Krantz M. rxncon 2.0: a language for executable molecular systems biology. bioRxiv. 2017.

31.

Cherry JM, Hong EL, Amundsen C, Balakrishnan R, Binkley G, Chan ET, et al. Saccharomyces

Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 2011; 40(D1):D700–

D705. https://doi.org/10.1093/nar/gkr1029 PMID: 22110037

32.

Zecchin A, Kalucka J, Dubois C, Carmeliet P. How Endothelial Cells Adapt Their Metabolism to Form

Vessels in Tumors. Frontiers in Immunology. 2017; 8:1750. https://doi.org/10.3389/fimmu.2017.01750

PMID: 29321777

33.

Li F, Long T, Lu Y, Ouyang Q, Tang C. The yeast cell-cycle network is robustly designed. Proc Natl

Acad Sci. 2004; 101(14):4781–4786. https://doi.org/10.1073/pnas.0305937101 PMID: 15037758

PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1009702 January 14, 2022

27 / 27

...

参考文献をもっと見る

分野

大学

学位論文種類・取得年

言語

Identification of periodic attractors in Boolean networks using a priori information

概要

この論文で使われている画像

関連論文

Weighted minimum feedback vertex sets and implementation in human cancer genes detection

余事象モデルに基づく未学習クラス推定法と生体信号分類への応用

Forecast and control of dynamical systems with data assimilation: Applications to COVID-19 epidemic and to Lorenz models

DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction

Raman spectroscopic studies coupled with MCR-ALS applied on some biomedical systems

参考文献

分野

大学

学位論文種類・取得年

言語

コピーが完了しました

URLをコピーしました

Identification of periodic attractors in Boolean networks using a priori information

概要

この論文で使われている画像

関連論文

Weighted minimum feedback vertex sets and implementation in human cancer genes detection

余事象モデルに基づく未学習クラス推定法と生体信号分類への応用

Forecast and control of dynamical systems with data assimilation: Applications to COVID-19 epidemic and to Lorenz models

DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction

Raman spectroscopic studies coupled with MCR-ALS applied on some biomedical systems

参考文献