Polynomial models of gene dynamics
Neurocomputing
A mixed-integer optimization framework for the synthesis and analysis of regulatory networks
Journal of Global Optimization
International Journal of Bioinformatics Research and Applications
Developing Itô stochastic differential equation models for neuronal signal transduction pathways
Computational Biology and Chemistry
Inference of gene regulatory network using modified genetic algorithm
ISB '10 Proceedings of the International Symposium on Biocomputing
Learning gene regulatory networks via globally regularized risk minimization
RECOMB-CG'07 Proceedings of the 2007 international conference on Comparative genomics
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Partial correlation with copula modeling
Computational Statistics & Data Analysis
Inferring parameters of gene regulatory networks via particle filtering
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Regulatory network reconstruction using stochastic logical networks
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
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Motivation: The explosion of microarray studies has promised to shed light on the temporal expression patterns of thousands of genes simultaneously. However, available methods are far from adequate in efficiently extracting useful information to aid in a greater understanding of transcriptional regulatory network. Biological systems have been modeled as dynamic systems for a long history, such as genetic networks and cell regulatory network. This study evaluated if the stochastic differential equation (SDE), which is prominent for modeling dynamic diffusion process originating from the irregular Brownian motion, can be applied in modeling the transcriptional regulatory network in Saccharomyces cerevisiae. Results: To model the time-continuous gene-expression datasets, a model of SDE is applied to depict irregular patterns. Our goal is to fit a generalized linear model by combining putative regulators to estimate the transcriptional pattern of a target gene. Goodness-of-fit is evaluated by log-likelihood and Akaike Information Criterion. Moreover, estimations of the contribution of regulators and inference of transcriptional pattern are implemented by statistical approaches. Our SDE model is basic but the test results agree well with the observed dynamic expression patterns. It implies that advanced SDE model might be perfectly suited to portray transcriptional regulatory networks. Availability: The R code is available on request. Contact: cykao@csie.ntu.edu.tw Supplementary information: http://www.csie.ntu.edu.tw/~b89x035/yeast/