作为复杂反应网络中看似可变常数来源的缺失环节
Missing links as a source of seemingly variable constants in complex reaction networks
摘要：
网络科学的一个主要挑战是从实验观测和理论模型中确定控制复杂网络动力学的参数。例如，在复杂的化学反应网络中，如描述内燃机和发电机中的过程的那些网络，尽管进行了大量的实验努力，但速率常数估计在研究中差异很大。在这里，我们研究的可能性，测量常数的变化可以在很大程度上归因于缺失的网络信息对参数估计的影响。通过对不完全化学反应网络中的测量进行数值模拟，我们发现，假定网络链接不重要（局部灵敏度小于测量链接的2%）的不可解释性可以产生高达一个数量级的明显速率常数变化，即使数据中没有实验误差。此外，在所有情况下，速率常数估计值的对数偏差与被忽略反应的累积相对灵敏度之间的相关系数均小于$0.5$。因此，对于复杂网络上的动态过程，通过从特定条件下收集的数据中确定新参数来迭代扩展模型不太可能产生可靠的结果。
英文摘要：
A major challenge in network science is to determine parameters governing complex network dynamics from experimental observations and theoretical models. In complex chemical reaction networks, for example, such as those describing processes in internal combustion engines and power generators, rate constant estimates vary significantly across studies despite substantial experimental efforts. Here, we examine the possibility that variability in measured constants can be largely attributed to the impact of missing network information on parameter estimation. Through the numerical simulation of measurements in incomplete chemical reaction networks, we show that unaccountability of network links presumed unimportant (with local sensitivity amounting to less than two percent of that of a measured link) can create apparent rate constant variations as large as one order of magnitude even if no experimental errors are present in the data. Furthermore, the correlation coefficient between the logarithmic deviation of the rate constant estimate and the cumulative relative sensitivity of the neglected reactions was less than $0.5$ in all cases. Thus, for dynamical processes on complex networks, iteratively expanding a model by determining new parameters from data collected under specific conditions is unlikely to produce reliable results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
