Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterise their metabolism at genome-scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modelling formalism of metabolic network modelling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded FBA models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae, and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level.

中文翻译：

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使用随机通量平衡分析算法模拟单细胞代谢

已知单细胞中基因表达的随机性会在细胞群水平上驱动代谢异质性，这被认为对微生物药物耐受性和癌症等人类疾病的治疗等问题具有重要影响。尽管在分析单细胞的基因组、转录组和蛋白质组方面取得了相当大的进步，但在基因组规模上通过实验表征它们的代谢仍然很困难。计算方法可以弥合这一差距，以实现对单细胞生物学的系统理解。为了应对这一挑战，我们开发了嵌入通量平衡分析的随机模拟算法 (SSA-FBA)，一个计算框架，用于模拟单个细胞代谢的随机动力学，使用基因组规模的代谢模型以及基因表达和酶反应速率参数的实验估计。SSA-FBA 将代谢网络建模的基于约束的建模形式扩展到单细胞机制，在实验难以处理时实现模拟。我们还开发了一种有效的 SSA-FBA 实现，它利用嵌入式 FBA 模型的拓扑结构来显着降低仿真的计算成本。作为初步案例研究，我们构建了一个简化的单细胞模型 我们还开发了一种有效的 SSA-FBA 实现，它利用嵌入式 FBA 模型的拓扑结构来显着降低仿真的计算成本。作为初步案例研究，我们构建了一个简化的单细胞模型 我们还开发了一种有效的 SSA-FBA 实现，它利用嵌入式 FBA 模型的拓扑结构来显着降低仿真的计算成本。作为初步案例研究，我们构建了一个简化的单细胞模型肺炎支原体，并使用 SSA-FBA 来说明随机性对单细胞水平代谢动力学的作用。