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Algorithms 2017, 10(1), 31; doi:10.3390/a10010031

Optimization-Based Approaches to Control of Probabilistic Boolean Networks

1
Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan
2
School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Tatsuya Akutsu and Henning Fernau
Received: 30 September 2016 / Revised: 17 February 2017 / Accepted: 20 February 2017 / Published: 22 February 2017
(This article belongs to the Special Issue Biological Networks)
View Full-Text   |   Download PDF [219 KB, uploaded 22 February 2017]   |  

Abstract

Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained. View Full-Text
Keywords: integer programming; gene regulatory network; polynomial optimization; probabilistic Boolean network integer programming; gene regulatory network; polynomial optimization; probabilistic Boolean network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kobayashi, K.; Hiraishi, K. Optimization-Based Approaches to Control of Probabilistic Boolean Networks. Algorithms 2017, 10, 31.

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