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

doi:10.3969/j.issn.1000-5641.2018.01.007

Abstract

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

CLC Number:

TP3-05

LYU Yue, ZHANG Min, QIN Xu-dong, YAN Jia. The transition and properties from Boolean networks to discrete-time Markov chains: A case study of mice stem cell gene regulatory networks[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(1): 59-75,90.

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