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When modeling a genetic regulatory network, many candidate model equations are often considered.
While linear mixed models implicitly assume that all SNPs have an effect on the phenotype (an infinitesimal genetic architecture), it is widely believed that disease phenotypes do not follow an infinitesimal model and that modeling a genetic architecture where most SNPs have negligible effect and some have modest effect (a noninfinitesimal genetic architecture) would increase power.
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We model a genetic toggle switch [ 30], which consists of two genes, expressing proteins P1 and P2, respectively.
We modelled a genetic knockout by forcing the respective component to remain in the off-state for the whole course of the simulation.
Moreover, to optimize the corresponding parameters of these models, a Genetic Algorithm (GA) was applied.
In order to solve the proposed models, a genetic algorithm integrating fuzzy simulation is designed.
Concerning the properties of a nonlinear model, a genetic algorithm is taken into account to find the optimal solutions for the selling price, lot size, and reorder point.
Based on the VMT model, a genetic algorithm is used to optimize the machining condition to reduce the energy consumption.
Because of computational complexity of the developed mathematical model, a genetic algorithm is developed upon which numerical experiments are reported in order to show validity and applicability of the proposed model.
Similar to A-FABP deficiency in experimental models, a genetic variant at A-FABP locus with reduction in A-FABP expression reduced the risk for cardiovascular disease and type 2 diabetes in a population genetic study [10].
In previous studies, we demonstrated the chemopreventive effect of metformin in two rodent models (a genetic model and a chemically-induced cancer model) and one human study of colorectal carcinogenesis.
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