Reduction of chemical reaction networks through delay distributions
Reduction of chemical reaction networks through delay distributions
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DOI：
10.1063/1.4793982
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发表时间：
2013-03-14
影响因子：
4.4
通讯作者：
Marquez-Lago, Tatiana T.
中科院分区：
文献类型：
--
作者：
Barrio, Manuel;Leier, Andre;Marquez-Lago, Tatiana T.
动态细胞事件的精确建模和模拟需要两个主要成分：关键化学反应的充分描述和在合理的时间跨度内模拟这些化学事件。从逻辑上讲，提出正确的模型是计算生物学中任何努力的关键一步。然而，通常情况下，相关的计算成本实际上限制了我们表示复杂细胞行为的能力。在本文中，我们提出了一种方法，旨在表示链的化学反应更简单，减少模型。通过生成特定于模型的延迟分布函数，连续馈送到延迟随机模拟算法来实现删节。我们展示了如何这样的延迟分布可以解析地描述时，系统是完全由连续的一阶反应，有或没有额外的“向后”旁路反应，产生一个确切的减少。对于模型，包括其他类型的单分子反应（组成合成，降解，或“向前”旁路反应），我们讨论了为什么必须采用数值方法，其准确的随机表示，并提出了两种替代方案。在这些情况下，准确度取决于相应的数字样本量。我们的模型简化方法在保持准确性的同时显著降低了计算成本。很自然地，计算成本随着网络规模和时间尺度的分离而增加。因此，我们期望我们的模型简化方法在这些情况下显着降低计算成本。我们预计，在模型简化中使用延迟将大大缓解目前在模拟大型化学反应方面的一些限制，这些限制主要适用于制药和生物研究。(C)2013年美国物理学会。[http：//dx.doi.org/10.1063/1.4793982]
Accurate modelling and simulation of dynamic cellular events require two main ingredients: an adequate description of key chemical reactions and simulation of such chemical events in reasonable time spans. Quite logically, posing the right model is a crucial step for any endeavour in Computational Biology. However, more often than not, it is the associated computational costs which actually limit our capabilities of representing complex cellular behaviour. In this paper, we propose a methodology aimed at representing chains of chemical reactions by much simpler, reduced models. The abridgement is achieved by generation of model-specific delay distribution functions, consecutively fed to a delay stochastic simulation algorithm. We show how such delay distributions can be analytically described whenever the system is solely composed of consecutive first-order reactions, with or without additional "backward" bypass reactions, yielding an exact reduction. For models including other types of monomolecular reactions (constitutive synthesis, degradation, or "forward" bypass reactions), we discuss why one must adopt a numerical approach for its accurate stochastic representation, and propose two alternatives for this. In these cases, the accuracy depends on the respective numerical sample size. Our model reduction methodology yields significantly lower computational costs while retaining accuracy. Quite naturally, computational costs increase alongside network size and separation of time scales. Thus, we expect our model reduction methodologies to significantly decrease computational costs in these instances. We anticipate the use of delays in model reduction will greatly alleviate some of the current restrictions in simulating large sets of chemical reactions, largely applicable in pharmaceutical and biological research. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4793982]