TY - JOUR
T1 - An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles
AU - Ho, Shinn-Ying
AU - Hsieh, Chin Hung
AU - Yu, Fu Chieh
AU - Huang, Hui Ling
PY - 2007/10/1
Y1 - 2007/10/1
N2 - From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. However, the inference of an S-system model of N-gene genetic networks has 2N(N + 1) parameters in a set of nonlinear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with the curse of dimensionality, the proposed algorithm consists of two stages, where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N + 1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on an orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC It is shown that 1) IGA is efficient enough to solve subproblems, 2) IGA is significantly superior to the existing method GA with simplex crossover (SPXGA), and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.
AB - From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. However, the inference of an S-system model of N-gene genetic networks has 2N(N + 1) parameters in a set of nonlinear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with the curse of dimensionality, the proposed algorithm consists of two stages, where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N + 1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on an orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC It is shown that 1) IGA is efficient enough to solve subproblems, 2) IGA is significantly superior to the existing method GA with simplex crossover (SPXGA), and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.
KW - Divide and conquer
KW - Evolutionary algorithm
KW - Genetic network
KW - Orthogonal experimental design
KW - Pathway identification
KW - S-system model
UR - http://www.scopus.com/inward/record.url?scp=36249014245&partnerID=8YFLogxK
U2 - 10.1109/tcbb.2007.1051
DO - 10.1109/tcbb.2007.1051
M3 - Article
C2 - 17975275
AN - SCOPUS:36249014245
VL - 4
SP - 648
EP - 660
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
SN - 1545-5963
IS - 4
ER -