2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) 2020
DOI: 10.1109/bibe50027.2020.00020
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Global fitting and parameter identifiability for amyloid-β aggregation with competing pathways

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Cited by 2 publications
(6 citation statements)
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“…Our Aβ competing pathways model using the ensemble kinetic simulation-based method also needs a gradient-free parameter optimization algorithm as it consists of several biochemical reactions from the competing pathways that makes the gradient-based models less effective. In fact, we reported the performance of different optimization methods for the competing pathways simulation in ref and validated that derivative-free methods work best in this context.…”
Section: Experimental Proceduresmentioning
confidence: 67%
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“…Our Aβ competing pathways model using the ensemble kinetic simulation-based method also needs a gradient-free parameter optimization algorithm as it consists of several biochemical reactions from the competing pathways that makes the gradient-based models less effective. In fact, we reported the performance of different optimization methods for the competing pathways simulation in ref and validated that derivative-free methods work best in this context.…”
Section: Experimental Proceduresmentioning
confidence: 67%
“…Optimization methods can be gradient-independent or gradient-based; the former method is theoretically less susceptible to stochastic noise than the latter method. Hence, in this case of stochastic optimization, we have used gradient-free metaheuristics , as our algorithm for parameter estimation. The following modeling abstraction was used in this study.…”
Section: Experimental Proceduresmentioning
confidence: 99%
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“…We used Scatter Search optimization algorithm to fit the experimental data as it has been earlier shown that metaheuristic algorithms like scatter performs better than other algorithms to fit the Aβ aggregation 45 . Sum of squared errors (SSE) was used as a metric to evaluate the models, and COmplex PAthway SImulator (COPASI) 46 to solve the mathematical models. Briefly, oligomerization was considered up to the formation of 12mers, beyond which all aggregates were considered ‘fibrils’ for modeling simplicity.…”
Section: Resultsmentioning
confidence: 99%
“…Optimization methods can be gradient-independent or gradient-based; the former method is theoretically less susceptible to the stochastic noise than the latter method. Hence, in this case of stochastic optimization, we have used gradient-free metaheuristics 45,46 as our algorithm for parameter estimation. The following modelling abstraction was used in this study.…”
Section: Model Simulationsmentioning
confidence: 99%