We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence.
COBISS.SI-ID: 25147943
Many real-world optimisation problems are of dynamic nature, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. To achieve this, we propose two population-based algorithms for solving dynamic optimisation problems (DOPs) with continuous variables: the self-adaptive differential evolution algorithm (jDE) and the differential ant-stigmergy algorithm (DASA). The performances of the jDE and the DASA are evaluated on the set of well-known benchmark problems provided for the special session on Evolutionary Computation in Dynamic and Uncertain Environments. We analyse the results for five algorithms presented by using the non-parametric statistical test procedure. The two proposed algorithms show a consistently superior performance over other recently proposed methods. The results show that both algorithms are appropriate candidates for DOPs.
COBISS.SI-ID: 15354390
In this paper a novel parameter optimization approach for cell detection tool and counting cells procedure in phase contrast images are presented. Manual counting of the attached cells in phase contrast images is time-consuming and subjective. For evaluation of electroporation efficiency of attached cells, we often perform manual counting of the cells which is needed to determine the percentage of electroporated cells under different experimental conditions. Here we present an automated cell counting procedure based on novel artificial neural network optimization of Image-based Tool for Counting Nuclei algorithm parameters to fit the training image set based on counts from an expert. Comparing the results of automated cell counting to user manual counting a 90,31% average agreement was achieved which is reasonably good especially taking into account inter-person error which can be upto10%. Even more, our procedure can also be used for fluorescent cell images with similar counting accuracy ()90%) enabling us to determine electroporation efficiency. In our experiments, the electroporation efficiency determined by manual cell counting was virtually the same as the one obtained by the automated procedure.
COBISS.SI-ID: 8213332