用混合模型计算单细胞代谢物分布
Computation of single-cell metabolite distributions using mixture models
摘要：
代谢异质性被广泛认为是我们理解非遗传变异的下一个挑战。越来越多的证据表明，代谢异质性可能是由于细胞内事件的内在随机性。然而，代谢传统上被认为是一个纯粹的确定性过程，其基础是高度丰富的代谢物往往会过滤掉随机现象。在这里，我们用一种预测单细胞代谢物分布的通用方法来弥合这一差距。通过利用酶表达和酶动力学之间的时间尺度的分离，我们的方法产生代谢物分布的估计，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，其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。建议的混合物模型提供了一个系统的方法来预测生化参数对代谢物分布的影响。我们的方法为确定形成代谢异质性的分子过程及其在疾病中的功能意义奠定了基础。
英文摘要：
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.
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