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

系统生物学研究所获得了一笔拨款，用于应用系统生物学原理和并行计算的能力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，并产生可预测的结果。它还将揭示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理。虽然这项工作将在两种生物上进行-大肠杆菌，一种众所周知的，被广泛研究的细菌，和盐盐杆菌，一种在高盐环境中茁壮成长的极端微生物，但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。除了在系统建模方法论方面培训一名博士后和一名研究生外，该项目还将开发探究驱动的、基于标准的高中（HS）教材。这些教育材料将包含一些概念和方法，用于推断和数学上表示（建模）一个系统作为一个相互作用的部分的网络，这将帮助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一个临界点，在整个生态系统中产生级联效应。教材将采用一种系统的方法，并将与当地的科学教育工作者一起开发。虽然该模块的目标将是教育学生各种统计建模和模型推断的方法，但它也将试图强化这样的观念：计算机并不总是有正确的答案，模型只能与它们所基于的数据一样好，预测的统计信心必须经过严格评估，以避免错误的结论。该项目资助的科学家将通过与高中学生和教育工作者的直接互动参与教育材料的开发。欲了解更多有关该项目及其产品的信息，请访问研究所。网址：https://www.systemsbiology.org。

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.