Hoffmann Martin, Galle Jörg
1Division of Personalized Tumor Therapy, Fraunhofer ITEM, BioPark I, Am Biopark 9, 93053 Regensburg, Germany.
2Department of Data Science and Knowledge Engineering, Maastricht University, Bouillonstraat 8-10, 6211 LH Maastricht, The Netherlands.
NPJ Syst Biol Appl. 2018 Apr 11;4:15. doi: 10.1038/s41540-018-0049-0. eCollection 2018.
Kinetic models are at the heart of system identification. A priori chosen rate functions may, however, be unfitting or too restrictive for complex or previously unanticipated regulation. We applied general purpose piecewise linear functions for stochastic system identification in one dimension using published flow cytometry data on and report on identification results for equilibrium state and dynamic time series. In metabolic labelling experiments during yeast osmotic stress response, we find mRNA production and degradation to be strongly co-regulated. In addition, mRNA degradation appears overall uncorrelated with mRNA level. Comparison of different system identification approaches using semi-empirical synthetic data revealed the superiority of single-cell tracking for parameter identification. Generally, we find that even within restrictive error bounds for deviation from experimental data, the number of viable regulation types may be large. Indeed, distinct regulation can lead to similar expression behaviour over time. Our results demonstrate that molecule production and degradation rates may often differ from classical constant, linear or Michaelis-Menten type kinetics.
动力学模型是系统识别的核心。然而，先验选择的速率函数对于复杂的或先前未预料到的调节可能不合适或限制过多。我们使用已发表的关于[具体内容未给出]的流式细胞术数据，将通用分段线性函数应用于一维随机系统识别，并报告平衡态和动态时间序列的识别结果。在酵母渗透应激反应期间的代谢标记实验中，我们发现mRNA的产生和降解受到强烈的共同调节。此外，mRNA降解总体上似乎与mRNA水平不相关。使用半经验合成数据对不同系统识别方法的比较揭示了单细胞跟踪在参数识别方面的优越性。一般来说，我们发现即使在与实验数据偏差的严格误差范围内，可行的调节类型数量也可能很大。事实上，不同的调节可能导致随时间的相似表达行为。我们的结果表明，分子产生和降解速率可能经常不同于经典的常数、线性或米氏动力学类型。
