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Tripods+X:Res: Collaborative Research: Identification of Gene Regulatory Network Function from Data

Tripods X:Res：协作研究：从数据中识别基因调控网络功能

基本信息

批准号：
1839299
负责人：
Tomas Gedeon
金额：
$ 19万
依托单位：
Montana State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839299&HistoricalAwards=false
关键词：
Tripods+X Res Collaborative Research Identification

项目摘要

Molecular and cellular biology are at the forefront of our quest to understand life and improve human health. However, the details of how genes and signals interact in real time to produce cell behavior, are not well understood. The problem is a lack of approaches that can integrate experimental data into models that describe the important behavior of the system, yet do not describe nonessential details, that would make it too cumbersome to compute in a reasonable time. This project will develop a new set of tools, that can predict the entire range of behaviors of a network of interdependent genes and do it so efficiently that behavior under the effect of a few mutations or behavior of alternative sets of genes can be readily explored. Furthermore, once such a description of behavior of a set of genes is available, it will be shared and used by other scientists. Continuous build-up of these results will rapidly increase their value for data science, cellular biology, and broader science. Molecular and cellular biology have witnessed a huge leap forward since science has acquired the ability to sequence genomes. However, while genomes are relatively static, the phenotypes result from the interaction of many genes that are expressed dynamically in time. Furthermore, since phenotypes arise from complex transcriptional networks, where the interactions tend to be nonlinear, data-based models will play a key role in understanding and ultimately controlling these phenotypes. Current modeling techniques, motivated by physics, struggle to bridge a fundamental conflict between low resolution of biological measurements informing model parameter values and the fundamental fact that dynamical systems are sensitive to initial conditions and parameters. This project develops a novel mathematical framework that provides a quantitative description of global dynamics that are compatible with coarse, noisy biological measurements. Combined with computationally efficient algorithms for the construction of databases that capture biologically relevant dynamics of a network, it can become the basis for shared science in the space of gene networks. These tools will be used to construct and validate network models from experimental data, interrogate dynamic behavior of a given network, and compare dynamics summaries across networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

分子和细胞生物学处于我们探索生命和改善人类健康的最前沿。然而，基因和信号如何实时相互作用以产生细胞行为的细节尚不清楚。问题是缺乏可以将实验数据集成到描述系统重要行为的模型中的方法，但不描述非必要的细节，这将使得在合理的时间内计算变得过于麻烦。该项目将开发一套新的工具，可以预测相互依赖的基因网络的整个行为范围，并且可以高效地预测少数突变影响下的行为或替代基因组的行为。此外，一旦对一组基因的行为进行了这样的描述，它将被其他科学家共享和使用。这些成果的不断积累将迅速增加其对数据科学、细胞生物学和更广泛的科学的价值。自从科学获得了基因组测序的能力以来，分子和细胞生物学已经见证了巨大的飞跃。然而，虽然基因组相对静态，但表型是由许多随时间动态表达的基因相互作用产生的。此外，由于表型源自复杂的转录网络，其中相互作用往往是非线性的，因此基于数据的模型将在理解和最终控制这些表型方面发挥关键作用。当前受物理学启发的建模技术难以弥合通知模型参数值的生物测量的低分辨率与动力系统对初始条件和参数敏感的基本事实之间的根本冲突。该项目开发了一种新颖的数学框架，提供与粗略、嘈杂的生物测量兼容的全球动态的定量描述。与构建捕获网络生物学相关动态的数据库的计算高效算法相结合，它可以成为基因网络空间中共享科学的基础。这些工具将用于根据实验数据构建和验证网络模型，询问给定网络的动态行为，并比较跨网络的动态摘要。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。