ArticleNPJ systems biology and applications2017
Performance of objective functions and optimisation procedures for parameter estimation in system biology models.
Article in NPJ systems biology and applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms.Scientific reports · 2024Article
- Benchmarking methods for computing local sensitivities in ordinary differential equation models at dynamic and steady states.PloS one · 2024Article
- In Silico Clinical Trials: Is It Possible?Methods in molecular biology (Clifton, N.J.) · 2024Article
- Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.iScience · 2023Article
- The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction.IET systems biology · 2023Article
- Meta-Dynamic Network Modelling for Biochemical Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Multidimensional hyperspin machine.Nature communications · 2022Article
- Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models.PLoS computational biology · 2022Article
- GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems.ACS synthetic biology · 2022Article
- Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?Frontiers in systems biology · 2022Article
- Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI.Methods in molecular biology (Clifton, N.J.) · 2022Article
- MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY.Current opinion in systems biology · 2021Article
- Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data.Journal of the Royal Society, Interface · 2021Article
- Modeling the Nonlinear Dynamics of Intracellular Signaling Networks.Bio-protocol · 2021Article
- Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response.Journal of personalized medicine · 2021Review
- Dynamic parameter estimation and prediction over consecutive scales, based on moving horizon estimation: applied to an industrial cell culture seed train.Bioprocess and biosystems engineering · 2021Article
- Calibration of ionic and cellular cardiac electrophysiology models.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
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 multiply the simulations to convert them to the scale of the data. Here we show that the scaling factor approach increases, compared to data-driven normalisation of the simulations, the degree of practical non-identifiability, defined as the number of directions in the parameter space, along which parameters are not identifiable. Further, the results indicate that data-driven normalisation of the simulations greatly improve the speed of convergence of all tested algorithms when the overall number of unknown parameters is relatively large (74 parameters in our test problems). Data-driven normalisation of the simulations also markedly improve the performance of the non-gradient-based algorithm tested even when the number of unknown parameters is relatively small (10 parameters in our test problems). As the models and the unknown parameters increase in size, the data-driven normalisation of the simulation approach can be the preferred option, because it does not aggravate non-identifiability and allows for obtaining parameter estimates in a reasonable amount of time.
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