The Microsoft Research-University of Trento, Centre for Computational and Systems Biology, Piazza Manci 17, Povo, Trento, Italy.
Eur Biophys J. 2010 May;39(6):1019-39. doi: 10.1007/s00249-009-0520-3. Epub 2009 Aug 11.
Methods for parameter estimation that are robust to experimental uncertainties and to stochastic and biological noise and that require a minimum of a priori input knowledge are of key importance in computational systems biology. The new method presented in this paper aims to ensure an inference model that deduces the rate constants of a system of biochemical reactions from experimentally measured time courses of reactants. This new method was applied to some challenging parameter estimation problems of nonlinear dynamic biological systems and was tested both on synthetic and real data. The synthetic case studies are the 12-state model of the SERCA pump and a model of a genetic network containing feedback loops of interaction between regulator and effector genes. The real case studies consist of a model of the reaction between the inhibitor kappaB kinase enzyme and its substrate in the signal transduction pathway of NF-kappaB, and a stiff model of the fermentation pathway of Lactococcus lactis.
在计算系统生物学中，对于能够抵抗实验不确定性、随机和生物噪声且需要最少先验输入知识的参数估计方法至关重要。本文提出的新方法旨在确保一种推理模型，该模型可以从实验测量的反应物时间历程中推导出系统的生化反应速率常数。这种新方法被应用于一些具有挑战性的非线性动态生物系统的参数估计问题，并在合成数据和真实数据上进行了测试。合成案例研究包括 SERCA 泵的 12 状态模型和一个包含调节基因和效应基因之间相互作用反馈环的遗传网络模型。真实案例研究包括 NF-κB 信号转导通路中抑制剂 κB 激酶酶与其底物之间反应的模型，以及乳球菌发酵途径的刚性模型。
