Stochastic Partitioning and Degradation of Macromolecules

描述(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造成了其他相同细胞之间的丰度波动。这种“噪音”的来源和动态已经被详细分析过了，例如，从随机基因表达的模型中可以看出，差异是如何响应转录和翻译的变化的。令人惊讶的是，初步的数学理论提出了一种非常不同的解释，表明几乎所有这样的结果都与起伏一致，而起伏是由于细胞分裂时分子的不均匀划分造成的。其他数学结果表明，分子的随机分割确实可能至少与基因表达对整体异质性的贡献一样大。这些结果推动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，对大肠杆菌中的蛋白质分解进行了量化，并对裂殖酵母中的mRNA和蛋白质进行了调查。选择每个系统是因为所说明的关键原理，因为它们在生物学上扮演的核心角色和它们在临床上构成的威胁，以及因为在实验室开发的新的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的分析玩具模型齐头并进，这些基本原理是一般的非线性数学理论，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央生物系统外，这一雄心勃勃的提议可能会使对细胞成分波动的量化观点发生重大转变，并将提供几种可行的数学和实验方法。所研究的过程也与人类健康有关，既研究了对所有细胞生长和分裂都很重要的中心原则，也关注了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，以及病原菌的保护反应。

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.