Volume : 2, Issue : 3, MAR 2016
FUZZY PETRINET FOR MODELING IN BIOLOGICAL EXPERIMENTATIONS
Deepthi KB, Arun Prakash HS, Manoj MB
The regulatory mechanism of gene expression can be modeled using deterministic techniques only when all the physical laws of the mechanism is well known. This requires time series data. Since the feasible experiment does not produce time series data, only a static predictive model can be developed. This model produces the effect of the input on the gene regulation mechanism and the products of the gene regulation. The measurement error and the uncertainties of the deducted model make the modeling of the phenomenon challenging. The uncertainties can be due to the effective variables which are uncontrollable during the experiments. Hence the model is essentially stochastic with deterministic and uncertainty components. Hence a statistical model can be used as a mathematical tool for the modeling and simulation of the observed phenomenon of GRN. It can be observed that the domain expert can heuristically predict the behavior of the GRN with reasonable accuracy.
Fuzzy Logic, Petrinet, Clustering, Defuzzification, Modeling, Inference Mechanism
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