Neural Network Simulation of Energy Transfer Processes in a Membrane Protein System
Part of the
Advances in Soft Computing
book series (AINSC, volume 19)
The method of the direct application of an artificial neural network for modelling of a complex system is developed with the purpose of speeding up the optimisation procedure for determination of system parameters. The method provides a significant decrease in simulation time. Moreover the artificial neural network produces a smooth approximation of stochastic simulation results and consequently it reduces the level of stochastic errors. The developed algorithm is applied to model the fluorescence resonance energy transfer within a system of M13 major coat protein mutants embedded in a membrane.
KeywordsArtificial Neural Network Fluorescence Resonance Energy Transfer Energy Transfer Process Artificial Neural Network Method Stochastic Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Barron AR (1993) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans on Inform Theory, 39: 3: 930–945MathSciNetzbMATHCrossRefGoogle Scholar
Bishop M (1997) Neural Networks for Pattern Recognition. Clarendon Press, OxfordGoogle Scholar
Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Math Contr Signals Syst, 2: 304–314MathSciNetCrossRefGoogle Scholar
Föster T (1948) Intermolecular energy migration and fluorescence. Ann Phys 2: 55–75CrossRefGoogle Scholar
Hornik K, Stinchcombe M, and White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks, 2: 359–366CrossRefGoogle Scholar
Koppel D, et al. (1979) Intramembrane Position of Membrane-Bound Chromophores Determined by Excitation Energy Transfer. Biochemistry 18: 5450–5457CrossRefGoogle Scholar
Spruijt R, et al. (2000) Localization and rearrangement modulation of the N-terminal arm of the membrane-bound major coat protein of bacteriophage M13. Biochim Biophys Acta 1509: 311–323CrossRefGoogle Scholar
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