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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/7783

Title: The pattern memory of gene-protein networks
Authors: WESTRA, Ronald
HOLLANDERS, Goele
BEX, Geert Jan
GYSSENS, Marc
TUYLS, Karl
Issue Date: 2007
Publisher: IOS PRESS
Citation: AI COMMUNICATIONS, 20(4). p. 297-311
Abstract: In this paper we study the potential of gene-protein interaction networks to store input-output patterns. The central question in this study concerns the memory capacity of a network of a given number of genes and proteins, which interact according to a linear state space model with external inputs. Here it is assumed that to a certain combination of inputs there exists an optimal state of the system, i.e., values of the gene expressions and protein levels, that has been attained externally, e.g., through evolutionary learning. Given such a set of learned optimal input-output patterns, the design question here is to find a sparse and hierarchical network structure for the gene-protein interactions and the gene-input couplings. This problem is formulated as an optimization problem in a linear programming setting. Numerical analysis shows that there are clear scale-invariant continuous second-order phase transitions for the network sparsity as the number of patterns increases. These phase transitions divide the system in three regions with different memory characteristics. It is possible to formulate simple scaling rules for the behavior of the network sparsity. Finally, numerical experiments show that these patterns are stable within a certain finite range around the patterns.
Notes: Maastricht Univ, Dept Math & Comp Sci, Maastricht, Netherlands. Transnatl Univ Limburg, Maastricht, Netherlands. Hasselt Univ, Dept Math Phys & Comp Sci, Hasselt, Belgium. Transnatl Univ Limburg, Hasselt, Belgium.Westra, RL, Maastricht Univ, Dept Math & Comp Sci, Maastricht, Netherlands.
URI: http://hdl.handle.net/1942/7783
Link to publication: http://iospress.metapress.com/openurl.asp?genre=article&issn=0921-7126&volume=20&issue=4&spage=297
ISI #: 000251720200007
ISSN: 0921-7126
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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