Stochastic Partitioning and Degradation of Macromolecules

描述（由申请人提供）：许多基因、rna和蛋白质以极低的数量存在，以至于单个分子之间的随机碰撞会在原本相同的细胞之间产生丰度波动。这种“噪音”的来源和动态已经被详细分析，例如，显示方差如何响应转录和翻译的变化，正如随机基因表达模型所期望的那样。令人惊讶的是，初步的数学理论给出了一个非常不同的解释，表明几乎所有这些结果都同样与波动一致，而波动是由于细胞分裂时分子的不均匀分配引起的。其他数学结果表明，分子的随机分配确实可以至少与基因表达一样，对整体异质性做出贡献。这些结果激发了对分离的广泛实验研究，比较了不同类型大分子的随机分配：深入分析了大肠杆菌中质粒的活性分离机制，定量分析了大肠杆菌中的蛋白质水解，并对pombe Schizosaccharomyces的mRNA和蛋白质进行了调查。每个系统的选择都是因为所阐述的关键原理，因为它们在生物学上发挥的核心作用和它们在临床上构成的威胁，还因为实验室开发的新实验方法使对它们进行更定量的分析成为可能。实验分析与基本原理和一般非线性数学理论的分析模型齐头并进，共同解决循环随机过程的类别。除了在分子和动力学细节上分析和比较中心生物系统之外，这一雄心勃勃的提议可能会在细胞成分波动的定量角度上产生实质性的转变，并将提供几种可行的数学和实验方法。通过研究对所有细胞生长和分裂至关重要的中心原则，以及通过关注与临床相关的特定系统，例如导致大多数抗生素耐药性爆发的基因，以及致病菌的保护性反应，所研究的过程也与人类健康相关。

DESCRIPTION (provided by applicant): Many genes, RNAs and proteins are present in such extremely low numbers that random collisions between individual molecules create fluctuations in abundances between otherwise identical cells. The sources and dynamics of such 'noise' have been analyzed in detail, for example showing how variances respond to changes in transcription and translation as expected from models of stochastic gene expression. Preliminary mathematical theory surprisingly suggests a very different explanation, showing that almost all such results are equally consistent with fluctuations that instead originate due to unequal partitioning of molecules at cell division. Other mathematical results suggest that random partitioning of molecules indeed could contribute at least as much as gene expression to the overall heterogeneity. These results motivate a broad experimental investigation of segregation, comparing stochastic partitioning for different types of macromolecules: in-depth analyses of active segregation mechanisms of plasmids in Escherichia coli, a quantification of proteolysis in E. coli, and an mRNA and protein survey in Schizosaccharomyces pombe. Each system was chosen because of the key principles illustrated, because of the central roles they play biologically and threats they pose clinically, and because new experimental approaches developed in the lab make it possible to analyze them much more quantitatively. The experimental analyses go hand-in-hand with both analytical toy models of basic principles, general nonlinear mathematical theory that collectively addresses classes of cyclic random processes. In addition to analyzing and comparing central biological systems in molecular and kinetic detail, this ambitious proposal could produce a substantial shift in the quantitative perspective on fluctuations in cellular constituents, and will provide several enabling mathematical and experimental methods. The processes studied are also relevant to human health both by studying a central principle important to all cell growth and division, and by focusing on specific systems of clinical relevance, such as the genes responsible for the majority of antibiotic resistance outbreaks, and the protective responses of pathogenic bacteria.