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Automated Large-Scale Control of Gene Regulatory Networks
Date
2010-04-01
Author
Tan, Mehmet
Alhajj, Reda
Polat, Faruk
Metadata
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Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem ( FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.
Subject Keywords
Control and Systems Engineering
,
Human-Computer Interaction
,
Electrical and Electronic Engineering
,
Software
,
Information Systems
,
General Medicine
,
Computer Science Applications
URI
https://hdl.handle.net/11511/34946
Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
DOI
https://doi.org/10.1109/tsmcb.2009.2014736
Collections
Department of Computer Engineering, Article
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M. Tan, R. Alhajj, and F. Polat, “Automated Large-Scale Control of Gene Regulatory Networks,”
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
, pp. 286–297, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34946.