Gorin Gennady, Vastola John J, Pachter Lior
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125.
Department of Neurobiology, Harvard Medical School, Boston, MA, 02115.
bioRxiv. 2023 May 29:2023.05.17.541250. doi: 10.1101/2023.05.17.541250.
Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.
全基因组RNA定量方面的最新实验进展为系统生物学带来了巨大希望。然而，要严格探究活细胞的生物学特性，需要一个统一的数学框架，该框架要在与基因组学检测相关的技术变异背景下考虑单分子生物随机性。我们综述了各种RNA转录过程的模型，以及基于微流控的单细胞RNA测序的封装和文库构建步骤，并提出了一个通过生成函数的操作来整合这些现象的框架。最后，我们使用模拟场景和生物学数据来说明该方法的意义和应用。
