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A Linear Representation of Dynamics of Boolean Networks

Daizhan Cheng, +1 more
- 17 Feb 2010 - 
- Vol. 55, Iss: 10, pp 2251-2258
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TLDR
Under this framework, a Boolean network equation is converted into an equivalent algebraic form as a conventional discrete-time linear system, and a matrix expression of logic is proposed, where a logical variable is expressed as a vector, a logical function is express as a multiple linear mapping.
Abstract
A new matrix product, called semi-tensor product of matrices, is reviewed Using it, a matrix expression of logic is proposed, where a logical variable is expressed as a vector, a logical function is expressed as a multiple linear mapping Under this framework, a Boolean network equation is converted into an equivalent algebraic form as a conventional discrete-time linear system Analyzing the transition matrix of the linear system, formulas are obtained to show a) the number of fixed points; b) the numbers of cycles of different lengths; c) transient period, for all points to enter the set of attractors; and d) basin of each attractor The corresponding algorithms are developed and used to some examples

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Analysis and Control of Boolean Networks: A Semi-tensor Product Approach

TL;DR: A new matrix product, called semi-tensor product of matrices, is used, which can covert the Boolean networks into discrete-time linear dynamic systems and the controllability of Boolean control networks is considered in the paper as an application.
Journal ArticleDOI

Controllability and observability of Boolean control networks

TL;DR: The controllability and observability of Boolean control networks are investigated and the controllable via two kinds of inputs is revealed by providing the corresponding reachable sets precisely.
Journal ArticleDOI

Observability, Reconstructibility and State Observers of Boolean Control Networks

TL;DR: A complete characterization of observability and reconstructibility properties for Boolean networks and Boolean control networks are provided, based both on the Boolean matrices involved in the network description and on the corresponding digraphs.
Journal ArticleDOI

Stability and stabilization of Boolean networks

TL;DR: In this paper, the stability of Boolean networks and the stabilization of Boolean control networks are investigated using semi-tensor product of matrices and the matrix expression of logic, which can be converted to a discrete time linear (bilinear) dynamics, called the algebraic form of the Boolean (control) network.
Journal ArticleDOI

Brief paper: Controllability of Boolean control networks via the Perron-Frobenius theory

TL;DR: Two definitions for controllability of a BCN are introduced, and it is shown that a necessary and sufficient condition for each form of controllable is that a certain nonnegative matrix is irreducible or primitive, respectively.
References
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Journal ArticleDOI

Metabolic stability and epigenesis in randomly constructed genetic nets

TL;DR: The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”.
Journal ArticleDOI

Systems biology: a brief overview.

Hiroaki Kitano
- 01 Mar 2002 - 
TL;DR: To understand biology at the system level, the authors must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism.
Journal ArticleDOI

Control of systems integrating logic, dynamics, and constraints

TL;DR: A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules.
Journal ArticleDOI

A NEW APPROACH TO DECODING LIFE: Systems Biology

TL;DR: The emergence of systems biology is described, as well as several examples of specific systems approaches.
Journal ArticleDOI

Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

TL;DR: Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.
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