ABI Innovation: An approach to construct a systems-scale predictive model of a gene regulatory network complete with mechanisms at single nucleotide resolution

ABI Innovation：一种构建基因调控网络的系统规模预测模型的方法，该模型具有单核苷酸分辨率的机制

• 批准号: 1262637
• 负责人: Nitin Baliga
• 金额: $ 104.71万
• 依托单位: Institute for Systems Biology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262637&HistoricalAwards=false
• 关键词: ABI; Innovation; approach; construct; systems

The Institute of Systems Biology is awarded a grant to apply principles of systems biology and the power of parallel computing to the advancement of computational and experimental methods that can very rapidly delineate the gene networks in any organism. By integrating genome sequence analysis with the analysis of large amounts of genome-wide ("high-throughput") experimental data, the computational methods being developed as part of this project will reverse engineer the network models. These models will be predictive and will have extraordinary detail (down to the level of biomolecular mechanisms) that could drive rational improvements in the gene networks, with predictable outcomes. It will also uncover fundamental principles underlying the ability of even the simplest microbes to deal with complex environmental changes. While the work will be performed on two organisms - E. coli, a well-known, widely studied bacterium, and Halobacterium salinarum, an extremophile that thrives in high salt environments, it is readily applicable to all sequenced bacteria, algae, and other organisms that are of huge industrial, agricultural, and medical importance. In addition to training a postdoctoral fellow and a graduate student in the methodology of systems modeling, this project will develop inquiry-driven, standards-based, high school (HS) educational materials. These education materials will incorporate concepts and methods for inferring and mathematically representing (modeling) a system as a network of interacting parts, which will help students understand and critically assess complex and important phenomena, e.g. how climate change can reach a tipping point to have cascading effects throughout an ecosystem. The education materials will incorporate a systems approach, and will be developed together with local science educators. While the goal of the module will be to educate students in various methods for statistical modeling and model inference, it will also attempt to reinforce the notion that computers do not always have the right answer, that models are only as good as the data they are based upon, and that statistical confidence of predictions must be rigorously assessed to avoid wrong conclusions. The scientists funded by this project will participate in development of educational materials through direct interactions with HS students and educators. For further information about this project and its products visit the Institute?s website at https://www.systemsbiology.org.

授予系统生物学研究所，以将系统生物学原理和并行计算的力量应用于计算和实验方法的进步，这些方法可以很快地描述任何生物体中的基因网络。通过将基因组序列分析与大量全基因组（“高通量”）实验数据进行分析，作为该项目的一部分开发的计算方法将逆转网络模型。这些模型将具有预测性，并具有非凡的细节（直至生物分子机制的水平），可以推动基因网络的合理改进，并具有可预测的结果。它还将发现即使是最简单的微生物处理复杂环境变化的能力的基本原则。虽然这项工作将在两个生物体上进行 - 大肠杆菌（一种众所周知的，广泛研究的细菌）和盐酸盐盐（Salinarum），这是一种在高盐环境中繁衍生息的极端细菌，但它很容易适用于所有测序细菌，藻类和其他生物体，这些生物具有巨大的工业，农业，农业，农业和医疗强度。除了培训博士后研究员和系统建模方法学研究生外，该项目还将开发以询问为基础的，标准，高中（HS）教育材料。这些教育材料将结合推断和数学代表（建模）作为互动零件网络的概念和方法，这将帮助学生理解并批判性地评估复杂而重要的现象，例如气候变化如何达到临界点，以在整个生态系统中产生级联效应。教育材料将结合一种系统方法，并将与当地科学教育者一起开发。尽管该模块的目的是教育学生以各种方法进行统计建模和模型推理，但它还将试图加强计算机并不总是具有正确答案的观念，即模型与他们所基于的数据一样好，并且必须严格评估预测的统计信心，以避免错误的结论。该项目资助的科学家将通过与HS学生和教育者的直接互动来参与教育材料的开发。有关该项目及其产品的更多信息，请访问该研究所的网站https://www.systemsbiology.org。