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.

系统生物学研究所获得了一笔赠款，用于应用系统生物学的原理和并行计算的力量来推进计算和实验方法，这些方法可以非常快速地描绘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”）实验数据的分析相结合，作为该项目一部分正在开发的计算方法将对网络模型进行逆向工程。这些模型将具有预测性，并将具有非凡的细节（低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。它还将揭示即使是最简单的微生物也能应对复杂环境变化的基本原理。虽然这项工作将在两种生物体上进行- E。大肠杆菌，一种众所周知的，被广泛研究的细菌，和盐生盐杆菌，一种在高盐环境中茁壮成长的极端微生物，它很容易适用于所有测序的细菌，藻类和其他具有巨大工业，农业和医学重要性的生物。除了培养博士后研究员和研究生在系统建模的方法，该项目将开发调查驱动的，基于标准的，高中（HS）的教育材料。这些教育材料将纳入概念和方法，用于推断和数学表示（建模）系统作为相互作用的部分网络，这将有助于学生理解和批判性地评估复杂和重要的现象，例如气候变化如何达到临界点，在整个生态系统中产生级联效应。这些教材将采用系统方法，并将与当地科学教育工作者共同编写。虽然该模块的目标是教育学生各种方法的统计建模和模型推理，它也将试图加强的概念，计算机并不总是有正确的答案，模型是唯一的数据一样好，他们是基于，预测的统计信心必须严格评估，以避免错误的结论。该项目资助的科学家将通过与HS学生和教育工作者的直接互动参与教材的开发。有关该项目及其产品的更多信息，请访问研究所？的网站https://www.systemsbiology.org。