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Artificial neural network modification of simulation-based fitting: application to a protein-lipid system
Petr V Nazarov1, Vladimir V Apanasovich, Vladimir M Lutkovski
1Laboratory of Biophysics, Wageningen University, Dreijenlaan 3, 6703 HA, Wageningen, The Netherlands. petr.nazarov@wur.nl
Journal of Chemical Information and Computer Sciences
|March 23, 2004
Summary
This study introduces artificial neural networks (ANNs) to accelerate complex system analysis. ANNs significantly reduce computation time for parameter determination in biophysics and chemistry simulations.
Area of Science:
- Biophysics
- Computational Chemistry
- Biochemistry
Background:
- Simulation-based fitting is crucial for analyzing complex experimental systems in chemistry and biophysics.
- Current simulation methods face limitations due to high computational time costs.
Purpose of the Study:
- To propose and validate the use of artificial neural networks (ANNs) to approximate simulation models.
- To significantly accelerate parameter determination in complex systems.
Main Methods:
- Substitution of computationally expensive simulation models with trained ANNs during the fitting procedure.
- Application of this approach to a fluorescence resonance energy transfer (FRET) model involving M13 major coat protein mutants in a lipid bilayer.
Main Results:
- Achieved a substantial computational time reduction by a factor of 5 x 10^4.
- Demonstrated that ANNs provide a smooth approximation of noisy simulation data.
Conclusions:
- ANNs offer a powerful method to overcome the time limitations of traditional simulation-based fitting.
- This approach enhances the efficiency and accuracy of parameter determination in biophysical and chemical research.