Discriminating between rival biochemical network models: three approaches to optimal experiment design.
Discriminating between rival biochemical network models: three approaches to optimal experiment design.
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DOI：
10.1186/1752-0509-4-38
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发表时间：
2010-04-01
影响因子：
--
通讯作者：
El-Samad H
中科院分区：
文献类型：
--
作者：
Mélykúti B;August E;Papachristodoulou A;El-Samad H
分子系统生物学的成功取决于利用计算模型设计预测性实验，并最终揭示潜在生物学机制的能力。在生物网络的计算建模中，常见的一个问题是，具有相似复杂性但结构不同的替代模型对一组实验数据的拟合效果同样好。在这种情况下，不止一种分子机制可以解释现有数据。为了排除错误的机制，需要使错误的模型无效。此时，应该提出并进行能使替代模型测量值之间的差异最大化的新实验。此类实验应经过优化设计，以产生最有可能使错误的模型结构无效的数据。
在本文中，我们开发了用于优化实验设计的方法，目的是区分同一生物系统的不同数学模型。第一种方法确定使竞争模型的输出之间的L2（能量）距离最大化的“最佳”初始条件。在第二种方法中，我们通过设计单位L2 -范数的最佳外部刺激（输入）曲线来使输出的L2 -距离最大化。我们的第三种方法使用优化的结构变化（例如，对应于反映基因敲除的参数值变化）来实现相同的目标。在一个例子——饥饿的盘基网柄菌的信号处理中，考虑了每种方法的数值实现。
基于模型的实验设计提高了生化网络模型区分的可靠性和效率。这为模型无效化开辟了道路，可用于完善我们对生化网络的理解。我们提出的一般问题表述以及三种实验设计方法为从业者提供了用于系统生物学实验设计的新工具。
The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures. In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ. Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to experiment design.
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