Photosynthetic pigment-protein complexes optical response modeling optimized by Differential evolution: algorithm convergence study

• Published in: Journal of physics. Conference series Vol. 2090; no. 1; pp. 12028 - 12032
• Main Authors: Chesalin, Denis D, Pishchalnikov, Roman Y
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.11.2021
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2090/1/012028

Photosynthetic pigment-protein complexes are the essential parts of thylakoid membranes of higher plants and cyanobacteria. Besides many organic and inorganic molecules they contain pigments like chlorophyll, bacteriochlorophyll, and carotenoids, which absorb the incident light and transform it into the energy of the excited electronic states. The semiclassical theories such as molecular exciton theory and the multimode Brownian oscillator model allows us to simulate the linear and nonlinear optical response of any pigment-protein complex, however, the main disadvantage of those approaches is a significant amount of effective parameters needed to be found in order to reproduce the experimental data. To overcome these difficulties we used the Differential evolution method (DE) that belongs to the family of evolutionary optimization algorithms. Based on our preliminary studies of the linear optical properties of monomeric photosynthetic pigments using DE, we proceed to more complex systems like the reaction center of photosystem II isolated from higher plants (PSIIRC). PSIIRC contains only eight chlorophyll pigments, and therefore it is potentially a very promising subject to test DE as a powerful optimization procedure for simulation of the optical response of a system of interacting pigments. Using the theoretically simulated linear spectra of PSIIRC (absorption, circular dichroism, linear dichroism, and fluorescence), we investigated the dependence of the algorithm convergence on DE settings: strategies, crossover, weighting factor; eventually finding the optimal mode of operation of the optimization procedure.