Fundamental Structure/Behaviour Relationships in Synchronous Boolean Neural Networks

Milligan, D. K.; Wilson, M.

doi:10.1007/978-94-009-0643-3_164

Cited by 2 publications

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“…A synchronous Boolean network constitutes the simplest possible model that mimics or captures the essential features of general regulatory networks, gene expressions, and various biological systems comprising different types of cellular networks, proteins, DNA, and RNA [1][2][3][4][5][6][7]. Hence, this model became a powerful tool for describing, analyzing, and simulating these systems.…”

Section: Introductionmentioning

confidence: 99%

The Eigenvectors of the Transition Matrix as Predictors of the Dynamics of a Synchronous Boolean Network

Rushdi

Alsogati

2020

JAMCS

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The synchronous Boolean network model is a simple and powerful tool in describing, analyzing and simulating cellular biological networks. This paper seeks a complete understanding of the dynamics of such a model by utilizing conventional matrix methods, rather than scalar methods, or matrix methods employing the non-conventional semi-tensor products (STP) of matrices. The paper starts by relating the network transition matrix to its function matrix via a self-inverse (involutary) state matrix, which has a simple recursive expression, provided a recursive ordering is employed for the underlying basis vector. Once the network transition matrix is obtained, it can be used to generate a wealth of information including its powers, characteristic equation, minimal equation, 1-eigenvectors, and 0-eigenvectors. These might be used to correctly predict both the transient behavior and (more importantly) the cyclic behavior of the network. In a short-cut partial variant of the proposed approach, the step of computing the transition matrix might be by-passed. The reason for this is that the transition matrix and the function matrix are similar matrices that share the same characteristic equation and hence the function matrix might suffice when only the partial information supplied by the characteristic equation is all that is needed. We demonstrate the conceptual simplicity and practical utility of our approach via two illustrative examples. The first example illustrates the computation of 1-eigenvectors (that can be used to identify loops or attractors), while the second example deals with the evaluation of 0-eigenvectors (that can be used to explore transient chains). Since attractors are the main concern in the underlying model, then analysis of the Boolean network might be confined to the determination of 1-eigenvectors only.

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