华东师范大学学报（自然科学版） >

2018 , Vol. 2018 >Issue 1: 59 - 75,90

DOI: https://doi.org/10.3969/j.issn.1000-5641.2018.01.007

计算机科学

布尔网络到离散时间马尔科夫模型的转换及性质研究——以大鼠干细胞基因调控网络为例

  • 吕悦 ,
  • 张敏 ,
  • 秦旭东 ,
  • 严佳
展开
  • 1. 华东师范大学 上海市高可信计算重点实验室, 上海 200062;
    2. 华东师范大学 计算机科学与软件工程学院, 上海 200062
吕悦,女,硕士研究生,研究方向为形式化验证.E-mail:lvyue2013@foxmail.com.

收稿日期: 2016-12-09

  网络出版日期: 2018-01-11

基金资助

国家自然科学基金（6167202）

收起

The transition and properties from Boolean networks to discrete-time Markov chains: A case study of mice stem cell gene regulatory networks

  • LYU Yue ,
  • ZHANG Min ,
  • QIN Xu-dong ,
  • YAN Jia
Expand
  • 1. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China;
    2. School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received date: 2016-12-09

  Online published: 2018-01-11

Fold

摘要

提出了一种基于概率模型检测技术的新方法用于解决生物工程中基因调控网络探查吸引子这一关键问题.以大鼠干细胞基因调控网络的吸引子找寻这样一个具体问题为例，将布尔网络表示的基因调控网络的更新函数通过对应的真值表，转换为离散时间马尔科夫链，写入模型检测工具PRISM中；之后通过验证模型的系统性质的技术去验证每个基因在很长一段时间之后的激活概率，以此找到基因调控网络中的吸引子.同时，通过添加基因扰动的方式，改变每个基因的激活/抑制概率，可以找到每个基因对其他基因的促进/抑制关系.实验表明，大鼠干细胞基因中有7个基因在一段时间后状态不变，剩余基因的变化共同构成了一个吸引环.整个检测流程简洁易用，可以直接找出吸引子.进一步地，实验准确地找出了大鼠干细胞中Gata1基因的抑制/促进对象，此实验结果对解决大鼠的白血球减少症有着治疗方面的意义.

关键词： 布尔网络; 离散时间马尔科夫链; 基因调控网络

本文引用格式

吕悦 , 张敏 , 秦旭东 , 严佳 . 布尔网络到离散时间马尔科夫模型的转换及性质研究——以大鼠干细胞基因调控网络为例[J]. 华东师范大学学报（自然科学版）, 2018 , 2018(1) : 59 -75,90 . DOI: 10.3969/j.issn.1000-5641.2018.01.007

Abstract

This paper proposes a new method based on probabilistic model checking technique to solve the problem of detecting attractors in gene regulatory networks which is vital in bio-engineering. We transform a gene regulatory network into a discrete-time Markov chains (DTMC for short) by using truth table. We verify the possibility of gene activation in some "long term" through a model checker named PRISM, which help us to find attractors of the gene regulatory network. In this paper, we show the whole procedure using the example of mice stem cell gene regulatory networks. Meanwhile, we make a new technique of detecting the promotion/inhibition relations between genes by adding gene disturbance and modifying gene activation/suppression probability. We show that in the mice regulatory network, seven genes will remain their invariable states in some "long term", then the rest of "changing" genes forms a cyclic attractor. The experiment shows that our method can find the attractors easily and directly. Moreover, our experiment successfully finds those genes affected by gene Gata1, which would be helpful for studying the mice leucopenia.

Key words： Boolean networks; discrete-time Markov chains; gene regulatory network

参考文献

[1] KWIATKOWSKA M, NORMAN G, PARKER D. PRISM:Probabilistic symbolic model checker[C]//International Conference on Computer Performance Evaluation. Berlin:Springer, 2002:200-204.
[2] KAUFFMAN S. Homeostasis and differentiation in random genetic control networks[J]. Nature, 1969, 5215(224):177-178.
[3] TRAIRATPHISAN P, MIZERA A, PANG J, et al. Recent development and biomedical applications of probabilistic Boolean networks[J]. Cell Communication and Signaling, 2013, 11:46. DOI:10.1186/1478-811X-11-46.
[4] AKUTSU T, MELKMAN A A, TAMURA T, et al. Determining a singleton attractor of a Boolean network with nested canalyzing functions[J]. Journal of Computational Biology, 2011, 18(10):1275-1290.
[5] HUANG S. Gene expression profiling, genetic networks, and cellular states:An integrating concept for tumorigenesis and drug discovery[J]. Journal of Molecular Medicine, 1999, 77(6):469-480.
[6] HUANG S. Genomics, complexity and drug discovery:Insights from Boolean network models of cellular regulation[J]. Pharmacogenomics, 2001, 2(3):203-222.
[7] DROSSEL B, MIHALJEV T, GREIL F. Number and length of attractors in a critical Kauffman model with connectivity one[J]. Physical Review Letters, 2005, 94(8):088701. DOI:10.1103/PhysRevLett.94.088701.
[8] KAUFFMAN S A. The origins of order:Self-organization and selection in evolution[J]. Journal of Evolutionary Biology, 1992, 13(1):133-144.
[9] SAMUELSSON B, TROEIN C. Superpolynomial growth in the number of attractors in Kauffman networks[J]. Physical Review Letters, 2003, 90(9):098701. DOI:10.1103/PhysRevLett.90.098701.
[10] TAMURA T, AKUTSU T. An O(1.787n)-time algorithm for detecting a singleton attractor in a Boolean network consisting of AND/OR nodes[C]//Fundamentals of Computation Theory, 16th International Symposium, FCT 2007, Proceedings. 2007:494-505.
[11] ZHENG Q B, SHEN L Z, SHANG X Q, et al. Detecting small attractors of large Boolean networks by functionreduction-based strategy[J]. IET Systems Biology, 2016, 10(2):49-56.
[12] HE Q B, XIA Z L, LIN B. An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks[J]. Journal of Theoretical Biology, 2016, 408:137-144.
[13] MEYN S P, TWEEDIE R L. Markov Chains and Stochastic Stability[M]. Berlin:Springer Science & Business Media, 2012.
[14] HOSOYA K, TOMI M, OHTSUKI S, et al. Enhancement of L-cystine transport activity and its relation to xCT gene induction at the blood-brain barrier by diethyl maleate treatment[J]. The Journal of Pharmacology and Experimental Therapeutics, 2002, 302(1):225-231.
[15] LI G, ROSS K E, ARIGHI C N, et al. MiRTex:A text mining system for miRNA-gene relation extraction[J]. PLOS Computational Biology, 2015, 11(9):e1004391. DOI:10.1371/journal.pcbi.1004391.
[16] 朱祥, 张云秋, 冯佳. 基于生物医学文本挖掘工具的白血病和基因关系研究[J]. 中华医学图书情报杂志, 2015, 24(10):28-32.
[17] HOWARD R A. Dynamic Probabilistic Systems[M]. New York:Dover Publicatinos Inc, 2007.
[18] KWIATKOWSKA M, NORMAN G, PARKER D. Advances and challenges of probabilistic model checking[C]//Communication, Control, and Computing. IEEE, 2010:1691-1698.
[19] LIANG S D, FUHRMAN S, SOMOGYI R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures[J]. Pacific Symposium on Biocomputing, 1998(3):18-29.
[20] 郑启奔. 布尔网络吸引子确定算法研究[D]. 浙江温州:温州大学, 2015.
[21] TAMURA T, AKUTSU T. Detecting a singleton attractor in a Boolean network utilizing SAT algorithms[J]. Ieice Trans Fundamentals, 2009, 92(2):493-501.
[22] KRUMSIEK J, MARR C, SCHROEDER T, et al. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network[J/OL]. PlOS One, (2011-08-10)[2016-08-21]. https://doi.org/10.1371/journal.pone.0022649.
[23] ZHANG S Q, HAYASHIDA M, AKUTSU T, et al. Algorithms for finding small attractors in Boolean networks[J]. Eurasip Journal on Bioinformatics and Systems Biology, 2007:20180. DOI:10.1155/2007/20180.
[24] LEE W P, HSIAO Y T. An adaptive GA-PSO approach with gene clustering to infer S-system models of gene regulatory networks[J]. Computer Journal, 2011, 54(9):1449-1464.
[25] SHLYKOVA I, PONOSOV A. Singular perturbation analysis and gene regulatory networks with delay[J]. Nonlinear Analysis:Theory Methods & Applications, 2010, 72(9/10):3786-3812.
[26] NICHOLSON C, GOODWIN L, CLARK C. Variable neighborhood search for reverse engineering of gene regulatory networks[J]. Journal of Biomedical Informatics, 2017, 65:120-131.
[27] FISHER J, KÖKSAL A S, PITERMAN N, et al. Synthesising executable gene regulatory networks from singlecell gene expression data[C]//International Conference on Computer Aided Verification. Springer International Publishing, 2015:544-560.
[28] ORKIN S H, ZON L I. Hematopoiesis:an evolving paradigm for stem cell biology[J]. Cell, 2008, 132(4):631-644.
[29] KARSUNKY H, ZENG H, SCHMIDT T, et al. Inflammatory reactions and severe neutropenia in mice lacking the transcriptional repressor Gfi1[J]. Nature Genetics, 2002, 30(3):295-300.
Options
文章导航

/

〈 〉