IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1152-1166. doi: 10.1109/TCBB.2018.2804395. Epub 2018 Feb 9.
Chemical organisation theory is a framework developed to simplify the analysis of long-term behaviour of chemical systems. In this work, we build on these ideas to develop novel techniques for formal quantitative analysis of chemical reaction networks, using discrete stochastic models represented as continuous-time Markov chains. We propose methods to identify organisations, and to study quantitative properties regarding movements between these organisations. We then construct and formalise a coarse-grained Markov chain model of hierarchic organisations for a given reaction network, which can be used to approximate the behaviour of the original reaction network. As an application of the coarse-grained model, we predict the behaviour of the reaction network systems over time via the master equation. Experiments show that our predictions can mimic the main pattern of the concrete behaviour in the long run, but the precision varies for different models and reaction rule rates. Finally, we propose an algorithm to selectively refine the coarse-grained models and show experiments demonstrating that the precision of the prediction has been improved.
化学组织理论是一个为简化化学系统长期行为分析而开发的框架。在这项工作中，我们基于这些思想，使用表示为连续时间马尔可夫链的离散随机模型，开发了用于化学反应网络形式定量分析的新方法。我们提出了识别组织的方法，并研究了这些组织之间运动的定量性质。然后，我们为给定的反应网络构建并形式化了层次组织的粗粒度马尔可夫链模型，该模型可用于近似原始反应网络的行为。作为粗粒度模型的应用，我们通过主方程预测反应网络系统随时间的行为。实验表明，我们的预测可以在长期内模拟具体行为的主要模式，但不同模型和反应规则速率的精度不同。最后，我们提出了一种算法来选择性地细化粗粒度模型，并展示了实验结果，表明预测的精度得到了提高。
