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首页> 外文期刊>NPJ systems biology and applications. >Stochastic system identification without an a priori chosen kinetic model—exploring feasible cell regulation with piecewise linear functions
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Stochastic system identification without an a priori chosen kinetic model—exploring feasible cell regulation with piecewise linear functions

机译:没有先验选择的动力学模型的随机系统识别-利用分段线性函数探索可行的细胞调节

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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 E.coli 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.
机译:动力学模型是系统识别的核心。然而,对于复杂的或先前未曾预料到的调节,先验选择的速率函数可能不合适或过于局限。我们使用在大肠杆菌上公开的流式细胞术数据在一维中应用通用分段线性函数进行随机系统识别,并报告平衡状态和动态时间序列的识别结果。在酵母渗透应激反应过程中的代谢标记实验中,我们发现mRNA的产生和降解受到强烈的共调节。另外,mRNA的降解似乎总体上与mRNA水平无关。使用半经验的综合数据对不同系统识别方法的比较揭示了单细胞跟踪用于参数识别的优越性。通常,我们发现即使在偏离实验数据的限制性误差范围内,可行的调控类型的数量也可能很大。实际上,随着时间的流逝,不同的调控可导致相似的表达行为。我们的结果表明,分子的产生和降解速率通常可能不同于经典的常数,线性或米利斯-门腾型动力学。

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