Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.
Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.
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DOI：
10.1186/1752-0509-8-59
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发表时间：
2014-05-20
影响因子：
--
通讯作者：
Smith NP
中科院分区：
文献类型：
--
作者：
Tøndel K;Niederer SA;Land S;Smith NP
在模型复杂程度和参数可识别性之间取得平衡，同时仍然使用建模产生生物学上可行的模拟是计算生物学的主要挑战。虽然模型开发的这两个要素紧密结合，但测量数据的参数拟合和模型机制的分析传统上是分别按顺序进行的。这个过程会产生模型和数据复杂性之间潜在的不匹配，这可能会损害计算框架揭示机械见解或预测新行为的能力。在本研究中，我们通过提出一个用于组合模型参数化、模型替代方案比较和模型机制分析的通用框架来解决这个问题。所提出的方法基于多元元建模（确定性模型的输入输出关系的统计近似）和通过迭代生成新的实验设计和在测量数据附近查找模拟来系统地放大参数空间的生物学可行区域的组合。参数拟合管道包括隐式敏感性分析和参数可识别性分析，使其适合测试模型简化的假设。使用这种方法，可以识别欠约束的模型参数以及模型内参数之间的耦合。该方法通过使用同一系统的替代模型的测量数据和合成数据的组合重新拟合已发布的心脏细胞力学模型的参数来演示。使用这种方法，通过识别模型组件发现了具有原肌球蛋白/桥动力学简化表达式的简化模型，这些模型组件可以被省略而不影响参数化数据的拟合。我们的分析表明，模型参数的标准差可能被限制为后续参数集平均值的平均 15%。我们的结果表明，所提出的方法对于比较模型替代方案和将模型降低到复制测量数据的最小复杂性是有效的。因此，我们相信这种方法在重新参数化现有框架、识别大型生物物理模型的冗余模型组件以及提高其预测能力方面具有巨大潜力。
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. 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. 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.
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DOI：
10.1016/j.chemolab.2011.04.010
发表时间：
2012-08-01
影响因子：
3.9
作者：
Isaeva, Julia;Saebo, Solve;Martens, Harald
通讯作者：
Martens, Harald
影响因子：
2.7
作者：
Niederer, S. A.;Fink, M.;Smith, N. P.
通讯作者：
Smith, N. P.
影响因子：
2.4
作者：
NAES, T;ISAKSSON, T
通讯作者：
ISAKSSON, T
影响因子：
5.5
作者：
Land, Sander;Niederer, Steven A.;Smith, Nicolas P.
通讯作者：
Smith, Nicolas P.
DOI：
10.1016/j.chemolab.2011.04.009
发表时间：
2012-08-01
影响因子：
3.9
作者：
Isaeva, Julia;Saebo, Solve;Martens, Harald
通讯作者：
Martens, Harald