Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

中文翻译：

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使用混合模型计算单细胞代谢物分布

在我们对非遗传变异的理解中，代谢异质性被广泛认为是下一个挑战。越来越多的证据表明，代谢异质性可能源于细胞内事件的固有随机性。然而，传统上，新陈代谢被视为纯粹的确定性过程，其基础是高度丰富的代谢物往往会滤除随机现象。在这里，我们用预测单个细胞间代谢物分布的一般方法弥合了这种差距。通过利用酶表达和酶动力学之间的时间尺度的分离，我们的方法可以生成代谢物分布的估算值，而无需大型代谢模型通常需要的冗长的随机模拟。代谢物分布采用高斯混合模型的形式，该模型可直接从单细胞表达数据和代谢途径的标准确定性模型计算得出。拟议的混合物模型提供了一种系统的方法来预测生化参数对代谢物分布的影响。我们的方法为确定影响代谢异质性及其对疾病的功能影响的分子过程奠定了基础。