Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development

BMC Systems Biology

Nicolas P Smith¹  Sander Land¹  Steven A Niederer¹  Kristin Tøndel² 

[1] Department of Biomedical Engineering, King’s College London, St. Thomas’ Hospital, Westminster Bridge Road, London SE1 7EH, UK;Simula Research Laboratory, Martin Linges v. 17/25, Rolfsbukta 4B, Fornebu 1364, Norway

关键词: Cardiac contraction;    Computational Biology;    Model reduction;    Experimental design;    Zooming into feasible parameter space regions;    Parameter space exploration;    Multivariate metamodelling;    Parameter estimation;   

Others  :  866365
DOI  :  10.1186/1752-0509-8-59

 received in 2013-12-18, accepted in 2014-04-29,  发布年份 2014

【 摘 要 】

Background

Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms.

Results

The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input–output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on average 15% of the mean values over the succeeding parameter sets.

Conclusions

Our results indicate that the presented approach is effective for comparing model alternatives and reducing models to the minimum complexity replicating measured data. We therefore believe that this approach has significant potential for reparameterising existing frameworks, for identification of redundant model components of large biophysical models and to increase their predictive capacity.

【 授权许可】

   
2014 Tøndel et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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