Rodriguez-Fernandez Maria, Mendes Pedro, Banga Julio R
Process Engineering Group, IIM-CSIC, Spanish Council for Scientific Research, C/Eduardo Cabello 6, 36208 Vigo, Spain.
Biosystems. 2006 Feb-Mar;83(2-3):248-65. doi: 10.1016/j.biosystems.2005.06.016. Epub 2005 Oct 19.
Developing suitable dynamic models of biochemical pathways is a key issue in Systems Biology. Predictive models for cells or whole organisms could ultimately lead to model-based predictive and/or preventive medicine. Parameter estimation (i.e. model calibration) in these dynamic models is therefore a critical problem. In a recent contribution [Moles, C.G., Mendes, P., Banga, J.R., 2003b. Parameter estimation in biochemical pathways: a comparison of global optimisation methods. Genome Res. 13, 2467-2474], the challenging nature of such inverse problems was highlighted considering a benchmark problem, and concluding that only a certain type of stochastic global optimisation method, Evolution Strategies (ES), was able to solve it successfully, although at a rather large computational cost. In this new contribution, we present a new integrated optimisation methodology with a number of very significant improvements: (i) computation time is reduced by one order of magnitude by means of a hybrid method which increases efficiency while guaranteeing robustness, (ii) measurement noise (errors) and partial observations are handled adequately, (iii) automatic testing of identifiability of the model (both local and practical) is included and (iv) the information content of the experiments is evaluated via the Fisher information matrix, with subsequent application to design of new optimal experiments through dynamic optimisation.
开发合适的生化途径动态模型是系统生物学中的一个关键问题。细胞或整个生物体的预测模型最终可能会导致基于模型的预测性和/或预防性医学的发展。因此，这些动态模型中的参数估计（即模型校准）是一个关键问题。在最近的一篇论文[莫尔斯，C.G.，门德斯，P.，班加，J.R.，2003b。生化途径中的参数估计：全局优化方法的比较。基因组研究。13，2467 - 2474]中，通过考虑一个基准问题突出了此类反问题的挑战性，并得出结论，只有某种类型的随机全局优化方法，即进化策略（ES），能够成功解决它，尽管计算成本相当高。在这项新的研究中，我们提出了一种新的综合优化方法，有许多非常显著的改进：（i）通过一种混合方法将计算时间减少了一个数量级，该方法提高了效率同时保证了稳健性，（ii）能够充分处理测量噪声（误差）和部分观测值，（iii）包括对模型可识别性（局部和实际）的自动测试，以及（iv）通过费希尔信息矩阵评估实验的信息含量，并随后通过动态优化应用于设计新的最优实验。
