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This article is part of the supplement: 22nd International Conference on Genome Informatics: Systems Biology

Open Access Proceedings

Novel Markov model of induced pluripotency predicts gene expression changes in reprogramming

Zhirui Hu2, Minping Qian3 and Michael Q Zhang12*

Author Affiliations

1 Department of Molecular and Cell Biology, Center for Systems Biology, the University of Texas at Dallas, 800 West Campbell Road, RL11 Richardson, TX 75080-3021, USA

2 MOE Key Laboratory of Bioinformatics and Bioinformatics Div, TNLIST /Department of Automation, Tsinghua University, Beijing 100084, China

3 School of Mathematics, Peking University, Beijing 100871, China

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BMC Systems Biology 2011, 5(Suppl 2):S8  doi:10.1186/1752-0509-5-S2-S8

Published: 14 December 2011

Abstract

Background

Somatic cells can be reprogrammed to induced-pluripotent stem cells (iPSCs) by introducing few reprogramming factors, which challenges the long held view that cell differentiation is irreversible. However, the mechanism of induced pluripotency is still unknown.

Methods

Inspired by the phenomenological reprogramming model of Artyomov et al (2010), we proposed a novel Markov model, stepwise reprogramming Markov (SRM) model, with simpler gene regulation rules and explored various properties of the model with Monte Carlo simulation. We calculated the reprogramming rate and showed that it would increase in the condition of knockdown of somatic transcription factors or inhibition of DNA methylation globally, consistent with the real reprogramming experiments. Furthermore, we demonstrated the utility of our model by testing it with the real dynamic gene expression data spanning across different intermediate stages in the iPS reprogramming process.

Results

The gene expression data at several stages in reprogramming and the reprogramming rate under several typically experiment conditions coincided with our simulation results. The function of reprogramming factors and gene expression change during reprogramming could be partly explained by our model reasonably well.

Conclusions

This lands further support on our general rules of gene regulation network in iPSC reprogramming. This model may help uncover the basic mechanism of reprogramming and improve the efficiency of converting somatic cells to iPSCs.