Application of neural network and genetic algorithm based approaches to bioprocess

Neural network and genetic algorithm with altered genetic operators have been implemented into bioprocess field to improve the modelling approach. Enzyme streptokinase is secreted by β-hemolytic streptococcus sp. often used to treat acute myocardial infarction, being a vital drug it is required to be produced in recombinant form for thrombolytic therapy. The unstructured model factors are found to apparently influence the existence of active cells in bioreactor environment. The structural features of a cell itself can be assumed to be an entire functional structured system with various sub-cellular entities. Our endeavour is to make out obligatory constraints that deal with plasmid instability. On simulation of process the patterns obtained noticeably depicts the role of few relevant parameters governing such system, including, metabolite concentration and dilution rate, to find out segregational instability and competitive dynamics in growing cell population. An adjustment of genetic operators to deal with two key factors of continuous culture model is taken into account. Neural network based technique has been employed to constitute logical models prior to serve as tool for optimizing process parameters of the dynamical system.