Performance of objective functions and optimisation procedures for parameter estimation in system biology models.

ABSTRACT: Mathematical modelling of signalling pathways aids experimental investigation in system and synthetic biology. Ever increasing data availability prompts the development of large dynamic models with numerous parameters. In this paper, we investigate how the number of unknown parameters affects the convergence of three frequently used optimisation algorithms and four objective functions. We compare objective functions that use data-driven normalisation of the simulations with those that use scaling factors. The data-driven normalisation of the simulation approach implies that simulations are normalised in the same way as the data, making both directly comparable. The scaling factor approach, which is commonly used for parameter estimation in dynamic systems, introduces scaling factors that m

SUBMITTER: Degasperi A 

PROVIDER: S-EPMC5548920 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature