Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
NeuroSpectroScopics LLC, Sherman Oaks, CA, USA.
Metabolomics. 2021 Jun 19;17(7):61. doi: 10.1007/s11306-021-01807-1.
Carbon isotope tracers have been used to determine relative rates of tricarboxylic acid cycle (TCA) cycle pathways since the 1950s. Steady-state experimental data are typically fit to a single mathematical model of metabolism to determine metabolic fluxes. Whether the chosen model is appropriate for the biological system has generally not been evaluated systematically. An overly-simple model omits known pathways while an overly-complex model may produce incorrect results due to overfitting.
The objectives were to develop and study a method that systematically evaluates multiple TCA cycle mathematical models as part of the fitting process.
The problem of choosing overly-simple or overly-complex models was approached by developing software that automatically explores all possible combinations of flux through pyruvate dehydrogenase, pyruvate kinase, pyruvate carboxylase and anaplerosis at propionyl-CoA carboxylase, and equivalent pathways, all relative to TCA cycle flux. Typical TCA cycle metabolic tracer experiments that use C nuclear magnetic resonance for detection and quantification of C-enriched glutamate products were simulated and analyzed. By evaluating the multiple model fits with both the conventional sum-of-squares residual error (SSRE) and the Akaike Information Criterion (AIC), the software helps the investigator understand the interaction between model complexity and goodness of fit.
When fitting alternative models of the TCA cycle metabolism, the SSRE may identify more than one model that fits the data well. Among those models, the AIC provides guidance as to which is the simplest of the candidate models is sufficient to describe the observed data. However under some conditions, AIC used alone inappropriately discriminates against necessary metabolic complexity.
In combination, the SSRE and AIC help the investigator identify the model that best describes the metabolism of a biological system.
自 20 世纪 50 年代以来，碳同位素示踪剂已被用于确定三羧酸循环 (TCA) 循环途径的相对速率。稳态实验数据通常拟合单个代谢数学模型来确定代谢通量。所选择的模型是否适合生物系统通常没有系统地评估。过于简单的模型会忽略已知的途径，而过于复杂的模型可能会由于过度拟合而产生不正确的结果。
本研究旨在开发并研究一种方法，该方法可作为拟合过程的一部分，系统地评估多种 TCA 循环数学模型。
通过开发软件自动探索丙酮酸脱氢酶、丙酮酸激酶、丙酮酸羧化酶和丙酰辅酶 A 羧化酶以及等效途径中通过丙酮酸、丙酮酸激酶和丙酮酸羧化酶的通量的所有可能组合，解决选择过于简单或过于复杂模型的问题，所有这些都相对于 TCA 循环通量。模拟并分析了使用 C 核磁共振检测和量化 C 富集谷氨酸产物的典型 TCA 循环代谢示踪实验。通过使用传统的平方和残差 (SSRE) 和赤池信息量准则 (AIC) 评估多个模型拟合，可以帮助研究人员了解模型复杂性与拟合度之间的相互作用。
在拟合 TCA 循环代谢的替代模型时，SSRE 可能会确定多个拟合数据良好的模型。在这些模型中，AIC 提供了指导，即候选模型中最简单的模型足以描述观察到的数据。然而，在某些条件下，单独使用 AIC 不适当地歧视了必要的代谢复杂性。
SSRE 和 AIC 相结合，可帮助研究人员确定最能描述生物系统代谢的模型。
