Simulation-based fitting has been applied to data analysis and parameter determination of complex experimental systems in many areas of chemistry and biophysics. However, this method is limited because of the time costs of the calculations. In this paper it is proposed to approximate and substitute a simulation model by an artificial neural network during the fitting procedure. Such a substitution significantly speeds up the parameter determination. This approach is tested on a model of fluorescence resonance energy transfer (FRET) within a system of site-directed fluorescence labeled M13 major coat protein mutants incorporated into a lipid bilayer. It is demonstrated that in our case the application of a trained artificial neural network for the substitution of the simulation model results in a significant gain in computing time by a factor of 5 x 10(4). Moreover, an artificial neural network produces a smooth approximation of the noisy results of a stochastic simulation.

Computers in chemistryComputers in chemistry V 0380 Artificial Neural Network Modification of Simulation-Based Fitting: Application to a Protein-Lipid System. -(NAZAROV*, P. V.; APANASOVICH, V. V.; LUTKOVSKI, V. M.; YATSKOU, M. M.; KOEHORST, R. B. M.; HEMMINGA, M. A.; J. Chem. Inf. Comput. Sci. 44 (2004) 2, 568-574; Lab. Biophys., Wageningen Univ., NL-6703 HA Wageningen, Neth.; Eng.) -Lindner 25-230

Current fluctuation processes in silicon avalanche photodiodes, noise generating and voltage-reference diodes are considered in this work. Plausible reasons of microplasma current fluctuations in the reverse-biased n-p-junctions are analysed. Both simulation experiments and real experimental data showed that the current fluctuations in all tested diodes strongly depend on the device internal structure.

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