Parallel Parameter Identification in Industrial Biotechnology

Real-valued black-box optimization of badly behaved and not well understood functions is a wide topic in many scientific areas. Possible applications range from maximizing portfolio profits in financial mathematics over efficient training of neuronal networks in computational linguistics to parameter identification of metabolism models in industrial biotechnology. This paper presents a comparison of several global as well as local optimization strategies applied to the task of efficiently identifying free parameters of a metabolic network model. A focus is being set on the ease of adopting these strategies to modern, highly parallel architectures. Finally an outlook on the possible parallel performance is being presented.