Stochastic Modeling and Inference of Gene Networks

基因网络的随机建模和推理

• 批准号: 1312926
• 负责人: Abhyudai Singh
• 金额: $ 21.66万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1312926&HistoricalAwards=false
• 关键词: Stochastic; Modeling; Inference; Gene; Networks

The environment within living cells is incredibly noisy with biochemical species randomly bumping and reacting with each other. This inherent probabilistic nature along with low population counts of cellular species creates considerable stochastic fluctuations in protein copy numbers over time inside individual cells. Increasing evidence suggests that this stochastic dynamics plays important functional roles within cells. Moreover, many diseased states have been attributed to elevated noise levels in specific proteins. Stochastic analysis of biochemical processes relies heavily on Monte Carlo simulation techniques that come at a significant computational cost. In addition, these techniques do not provide closed-form solutions that enable a systematic understanding of how stochastic variability is regulated in these systems. This project will overcome these challenges by developing computationally tractable methodologies based on moment closure schemes for studying stochastic dynamics of gene regulatory networks. Various analytical approximations that relate statistical properties of the system to biologically relevant parameters will be investigated. Far from being a hindrance, signatures of protein noise levels can be informative of the underlying gene network topology. This project will build mathematical techniques that harness quantitative measurements of stochasticity in protein levels for inferring regulatory interactions between genes and proteins.Biological data is being collected at a rapid rate and innovative methods for analyzing data are critically needed. Advances in experimental techniques allow measurements of fluctuations in protein levels in individual cells, which carry useful information to probe interactions between genes and proteins. In this project, tools exploiting statistical properties of these fluctuations to characterize cellular processes will be developed and will be made available for the broad scientific community to use. Stochastic variability in protein levels has been implicated in bacterial antibiotic resistance, mutation-independent selection of tumors and driving pathogenic human viruses, e.g., HIV, into a drug-resistant dormant state. This research will improve the characterizations of gene networks underlying these disease systems and thus this research will have a broader impact on medicine. Many of the results of this project will be incorporated into various courses offered across different departments providing interdisciplinary training and research experience to students at the interface of mathematical and biological sciences.

活细胞内的环境非常嘈杂，生化物质彼此随机碰撞和反应。这种固有的概率性质以及细胞物种的低种群计数导致单个细胞内蛋白质拷贝数随时间的推移产生相当大的随机波动。越来越多的证据表明，这种随机动力学在细胞内发挥着重要的功能作用。此外，许多疾病状态归因于特定蛋白质的噪音水平升高。生化过程的随机分析在很大程度上依赖于蒙特卡罗模拟技术，但计算成本很高。此外，这些技术不提供封闭式解决方案，无法系统地理解这些系统中如何调节随机变异性。该项目将通过开发基于矩闭合方案的计算易于处理的方法来克服这些挑战，用于研究基因调控网络的随机动力学。将研究将系统的统计特性与生物学相关参数联系起来的各种分析近似。蛋白质噪声水平的特征非但不会成为障碍，反而可以为潜在的基因网络拓扑提供信息。该项目将建立数学技术，利用蛋白质水平随机性的定量测量来推断基因和蛋白质之间的调控相互作用。生物数据正在快速收集，迫切需要用于分析数据的创新方法。实验技术的进步允许测量单个细胞中蛋白质水平的波动，这携带有用的信息来探测基因和蛋白质之间的相互作用。在该项目中，将开发利用这些波动的统计特性来表征细胞过程的工具，并将供广大科学界使用。蛋白质水平的随机变异与细菌抗生素耐药性、肿瘤的突变独立选择以及驱动人类致病病毒（例如艾滋病毒）进入耐药休眠状态有关。这项研究将改善这些疾病系统背后的基因网络的特征，因此这项研究将对医学产生更广泛的影响。该项目的许多成果将被纳入不同部门提供的各种课程中，为学生提供数学和生物科学交叉学科的跨学科培训和研究经验。