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Conf Proc IEEE Eng Med Biol Soc. 2013;2013:579-83. doi: 10.1109/EMBC.2013.6609566.

G-networks towards synthetic biology: A brief review.


G-networks and the Random Neural Network are a class of stochastic models that have a broad range applications ranging from modeling neuronal ensembles, gene regulatory networks, and the performance of computer systems and networks and modeling of energy flows in systems with renewable energy sources. Eaarlier applications include learning, bio-medical image processing, and network routing. Gene regulatory networks (GRNs) consist of thousands of genes and proteins which dynamically interact with each other. Once these regulatory structures are revealed, one must understand their dynamical behaviors through pathway activities. GRN dynamics are often investigated via stochastic models since molecular interactions are discrete and stochastic. However, this stochastic nature requires substantial computation to find the steady-state solution of the GRNs where thousands of genes are involved. This review focuses on a stochastic GRN modeling techniques based on G-networks which provide the analytical steady-state solution for efficient GRN dynamics. Three applications of G-networks to GRNs show that this novel approach serves to detect abnormalities from gene expression data, and that they help to explicit the behavior of complicated GRN models by dividing the gene regulatory processes into DNA and protein layers. Appropriate reverse engineering methods similar to neural network learning allows the G-network to provide important insight into the manner in which GRNs respond to external conditions, offering biologically meaningful and clinically useful information, and as an exploratory design tool for synthetic biology.

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