Tuning EPR spectral parameters with a genetic algorithm
We present an evolutionary computation approach to parameter tuning in electron paramagnetic resonance (EPR) spectroscopy which is a nondestructive technique suitable for inspection of complex biological systems. Characterization of such a system is much more reliable when spectral features are extracted from a biophysical model of the system. This involves optimization of the model parameters so that the spectrum generated by the model matches the experimental EPR spectrum. Various optimization methods have been applied to this task in the past, but nowadays stochastic algorithms are used more and more often. As many single-point algorithms require time-consuming preparation of promising starting points to produce reasonable results, we have addressed the problem with population-based search strategy. We have implemented a genetic algorithm for EPR spectral parameter optimization and tested it on synthetic spectra obtained in cell membrane inspection. Preliminary numerical experiments show the new approach is beneficial in that it produces satisfactory results and reduces the time a spectroscopist spends for navigating the optimization process.
epr spectral parameters genetic algorithm evolutionary computation electron paramagnetic resonance (epr) cell membranes biological system characterization stochastic optimization