Irving Institute for Cancer Dynamics, Columbia University, New York, New York; School of Mathematics, University of Birmingham, Birmingham, United Kingdom.
Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
Biophys J. 2021 Dec 7;120(23):5231-5242. doi: 10.1016/j.bpj.2021.10.038. Epub 2021 Oct 30.
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 characterize their metabolism at the 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 modeling formalism of metabolic network modeling 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 flux-balance analysis 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.
单细胞基因表达的随机性已知会在细胞群体水平上引发代谢异质性，这对于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意义。尽管在单个细胞的基因组、转录组和蛋白质组的分析方面取得了相当大的进展，但在实验上对其基因组规模的代谢进行特征描述仍然具有挑战性。计算方法可以弥补这一差距，实现对单细胞生物学的系统理解。为了解决这一挑战，我们开发了嵌入通量平衡分析的随机模拟算法（SSA-FBA），这是一种使用基于实验估计的基因表达和酶反应速率参数的基因组规模代谢模型来模拟单个细胞代谢的随机动力学的计算框架。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范围，在实验难以进行时可以进行模拟。我们还开发了 SSA-FBA 的高效实现，利用嵌入式通量平衡分析模型的拓扑结构，显著降低了模拟的计算成本。作为初步的案例研究，我们构建了肺炎支原体的简化单细胞模型，并使用 SSA-FBA 来阐明随机性在单细胞水平上对代谢动力学的作用。
