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Model checking optimal finite-horizon control for probabilistic gene regulatory networks

Overview of attention for article published in BMC Systems Biology, December 2017
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Title
Model checking optimal finite-horizon control for probabilistic gene regulatory networks
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0481-6
Pubmed ID
Authors

Ou Wei, Zonghao Guo, Yun Niu, Wenyuan Liao

Abstract

Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 38%
Professor 1 13%
Other 1 13%
Researcher 1 13%
Professor > Associate Professor 1 13%
Other 0 0%
Unknown 1 13%
Readers by discipline Count As %
Engineering 2 25%
Biochemistry, Genetics and Molecular Biology 1 13%
Business, Management and Accounting 1 13%
Mathematics 1 13%
Computer Science 1 13%
Other 1 13%
Unknown 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 January 2018.
All research outputs
#10,971,885
of 12,381,422 outputs
Outputs from BMC Systems Biology
#901
of 1,040 outputs
Outputs of similar age
#292,890
of 351,959 outputs
Outputs of similar age from BMC Systems Biology
#40
of 53 outputs
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