An efficient method of exploring simulation models by assimilating literature and biological observational data
An efficient method of exploring simulation models by assimilating literature and biological observational data
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DOI：
10.1016/j.biosystems.2014.06.001
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发表时间：
2014-07-01
期刊：
影响因子：
1.6
通讯作者：
Miyano, Satoru
中科院分区：
文献类型：
--
作者：
Hasegawa, Takanori;Nagasaki, Masao;Miyano, Satoru
最近，一些生物模拟模型，例如，基因调控网络和代谢途径，已经基于生物分子反应的现有知识构建，例如，DNA-蛋白质和蛋白质-蛋白质相互作用然而，由于这些并不总是包含所有必要的分子和反应，它们的模拟结果可能与观测数据不一致。因此，迫切需要改进这种模拟模型。先前报道的方法通过部分修改现有模型来创建多个候选仿真模型。然而，这种方法计算成本高，无法处理大量的候选人，需要找到模型，其模拟结果与数据高度一致。为了克服这个问题，我们专注于这样一个事实，即模拟模型的定性动态是高度相似的，如果它们共享一定量的监管结构。这表明更好的拟合候选者倾向于共享最佳拟合候选者的基本调控结构，这可以最好地预测候选者之间的数据。因此，而不是评估所有的候选人，我们提出了一个有效的探索方法，可以选择性地和顺序评估候选人的基础上，他们的监管结构的相似性。此外，在估计候选者的参数值时，例如，对于mRNA的合成和降解速率，可以利用先前评估的候选物的数据。该方法在这里适用于大鼠皮质类固醇的药物基因组学途径，使用时间序列微阵列表达数据。在性能测试中，我们成功地获得了超过80%的一致性解决方案在15%的计算时间相比，综合评价。然后，我们将这种方法应用于142个文献记录的皮质类固醇诱导基因的模拟模型，从而选择了134个新构建的更好的模型。这里描述的方法被发现能够有效地探索候选仿真模型，并在短时间内获得更好的模型。此外，研究结果表明，文献记录的途径可能有改进的余地，可以使用生物学观察数据系统地更新。(C)2014爱思唯尔爱尔兰有限公司版权所有。
Recently, several biological simulation models of, e.g., gene regulatory networks and metabolic pathways, have been constructed based on existing knowledge of biomolecular reactions, e.g., DNA-protein and protein-protein interactions. However, since these do not always contain all necessary molecules and reactions, their simulation results can be inconsistent with observational data. Therefore, improvements in such simulation models are urgently required. A previously reported method created multiple candidate simulation models by partially modifying existing models. However, this approach was computationally costly and could not handle a large number of candidates that are required to find models whose simulation results are highly consistent with the data. In order to overcome the problem, we focused on the fact that the qualitative dynamics of simulation models are highly similar if they share a certain amount of regulatory structures. This indicates that better fitting candidates tend to share the basic regulatory structure of the best fitting candidate, which can best predict the data among candidates. Thus, instead of evaluating all candidates, we propose an efficient explorative method that can selectively and sequentially evaluate candidates based on the similarity of their regulatory structures. Furthermore, in estimating the parameter values of a candidate, e.g., synthesis and degradation rates of mRNA, for the data, those of the previously evaluated candidates can be utilized. The method is applied here to the pharmacogenomic pathways for corticosteroids in rats, using time-series microarray expression data. In the performance test, we succeeded in obtaining more than 80% of consistent solutions within 15% of the computational time as compared to the comprehensive evaluation. Then, we applied this approach to 142 literature-recorded simulation models of corticosteroid-induced genes, and consequently selected 134 newly constructed better models. The method described here was found to be capable of efficiently exploring candidate simulation models and obtaining better models within a short span of time. Furthermore, the results suggest that there may be room for improvement in literature recorded pathways and that they can be systematically updated using biological observational data. (C) 2014 Elsevier Ireland Ltd. All rights reserved.