Construction of a Boolean model of gene and protein regulatory network with memory.
A Boolean model of gene and protein regulatory network with memory (GPBN) has recently attracted interest as a generalization of original random Boolean networks (BNs) for genetic and cellular networks. It is better suited to describe experimental data from the time-course microarray. Addressing construction problems in GPBNs may lead to a better understanding of the intrinsic dynamics in biological systems. Using the technique of the semi-tensor product (STP) of matrices, the dynamics of a GPBN can be expressed in an algebraic form and the attractors can be calculated. This paper investigates the issue of construction of GPBNs from prescribed attractors. Based on a rigorous theoretical analysis, some algebraic formulae and a computationally feasible algorithm are obtained to construct the least in-degree model with prescribed attractors. Illustrative examples are presented to show the validity of the theoretical results and the proposed algorithm.
China Ship Development and Design Center, Wuhan 430064, China; State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China. Electronic address: ym_jeff@hotmail.com.
Meng
M
Yang
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China. Electronic address: rui.li@pku.edu.cn.
Rui
R
Li
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China. Electronic address: chutg@pku.edu.cn.
Tianguang
T
Chu
D000465:Algorithms; D005240:Feasibility Studies; D053263:Gene Regulatory Networks; D008954:Models, Biological; D008957:Models, Genetic; D011506:Proteins / Q000378:metabolism*
D011506:Proteins
Algebraic form; Construction from attractors; Decay time; Least in-degree model; Semi-tensor product