ABI Innovation: Empirical Dynamics: A Next-Generation Approach For Uncovering Hidden Causal Links in Gene Expression

ABI 创新：经验动力学：揭示基因表达中隐藏因果关系的下一代方法

• 批准号: 1660584
• 负责人: George Sugihara
• 金额: $ 65.86万
• 依托单位: University of California-San Diego Scripps Inst of Oceanography
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1660584&HistoricalAwards=false
• 关键词: ABI; Innovation; Empirical; Dynamics; Next

The full human genome of 3.3 billion base pairs was first sequenced nearly 15 years ago. Nonetheless, genetic science is still a long way off from understanding how this exhaustive list of "parts" fits together. This project heralds a transformative shift in the basic mathematical approach to understanding how genes interact, by exploiting the fact that gene expression is a temporal and context dependent process. For example, every person has the genetic coding to produce melatonin; however, its expression varies through the day (keying on genes in the circadian clock) and it is sensitive to environmental factors like light exposure. Thus the temporal sequence and context of expression are important. However, current approaches to understanding variability in expression have relied on non-temporal statistical frameworks based on correlation. They assume that if genes interact, they will always either be positively correlated (simultaneously expressed among all samples) or negatively correlated (expressed only when the other is not) regardless of context or the changing cellular environment. Though a convenient simplification, such correlative approaches are obviously incomplete, and are likely to overlook essential processes such as thresholding, regime shifts, and gene check-pointing that specifically arise from dynamic, context-dependent behavior. This project will investigate empirical dynamic modeling (EDM) as an emerging framework that explicitly accounts for both the context and temporal sequence of expression events. The work will be a mixture of proof-of-principle and application to a cell differentiation pathway in mice associated with breast cancers. Development of EDM will provide an important complement to current approaches, with the capability to dramatically increase the efficacy of bioinformatics and systems biology research, and to reveal important regulatory hubs of genes that are non-correlated and thus invisible to current approaches.This project will investigate and further develop empirical dynamic modeling (EDM) as a conceptual framework to identify causal interactions among genes and produce mechanistic understanding that can be validated by prediction. As an approach that accommodates the reality of natural (non-engineered) nonlinear (context-dependent) interconnected systems, EDM is well-suited to leverage temporally-explicit genomic datasets that have only recently become feasible. In the first phase, the approach will be applied to construct gene interaction networks during the yeast (S. cerevisiae) cell cycle to explore how an explicitly nonlinear and dynamic approach can reveal information that is hidden to current analytical methods. Building on this, the second phase will seek to develop and implement an EDM approach (static cross-map) to single cell data that necessarily lack an explicit time sequence. The approach will be developed and tested on computational models of gene circuits, then applied to single cell data obtained from a stem-cell differentiation sequence in mice. Work will be validated by comparison to existing ontologies and by tailored experiments to be carried out by a collaborating lab at The Salk Institute. Software will be distributed through BioConductor: https://www.bioconductor.org/, all other materials (video animations, interactive demos, etc.) will be hosted on the Sugihara Lab website: http://deepeco.ucsd.edu.

大约15年前，33亿碱基对的完整人类基因组首次测序。尽管如此，遗传科学距离理解这一详尽的“部分”清单如何组合在一起还有很长的路要走。这个项目预示着一个变革性的转变，在基本的数学方法来理解基因如何相互作用，通过利用基因表达是一个时间和上下文依赖的过程。 例如，每个人都有产生褪黑激素的遗传编码;然而，它的表达在一天中会有所不同（在生物钟中键入基因），并且它对环境因素（如光照）敏感。因此，表达的时间顺序和背景是重要的。然而，目前的方法来理解表达的变化依赖于基于相关性的非时间统计框架。他们假设，如果基因相互作用，它们将总是正相关（在所有样本中同时表达）或负相关（仅在另一个不表达时表达），而不管背景或细胞环境的变化。虽然是一个方便的简化，这种相关的方法显然是不完整的，并且可能会忽略基本的过程，如阈值，政权转移，基因检查点，特别是从动态的，上下文相关的行为。这个项目将调查经验动态建模（EDM）作为一个新兴的框架，明确占表达事件的上下文和时间序列。这项工作将是原理证明和应用于与乳腺癌相关的小鼠细胞分化途径的混合物。EDM的发展将为当前的方法提供重要的补充，能够显著提高生物信息学和系统生物学研究的效率，并揭示基因的重要调控中心，这些基因是不相关的，因此在目前的方法中是不可见的。本项目将研究并进一步发展经验动态建模（EDM）作为一个概念框架，以确定基因之间的因果相互作用，并产生可以通过预测验证的机械理解。作为一种适应自然（非工程）非线性（上下文相关）互连系统现实的方法，EDM非常适合利用最近才变得可行的时间明确的基因组数据集。在第一阶段中，我们将应用该方法构建酵母（S。酿酒酵母）细胞周期，探索如何明确的非线性和动态的方法可以揭示隐藏在当前的分析方法的信息。在此基础上，第二阶段将寻求开发和实施EDM方法（静态交叉图），以处理必然缺乏明确时间序列的单细胞数据。该方法将在基因电路的计算模型上进行开发和测试，然后应用于从小鼠干细胞分化序列中获得的单细胞数据。工作将通过与现有本体的比较以及由索尔克研究所的合作实验室进行的定制实验进行验证。软件将通过BioConductor分发：https：//www.bioconductor.org/，所有其他材料（视频动画、交互式演示等）将托管在Sugihara Lab网站上：http://deepeco.ucsd.edu。

项目成果

A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling

基于经验动态建模的生态系统动力学可视化分析方法

• DOI: 10.1109/tvcg.2020.3028956
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Natsukawa, Hiroaki;Deyle, Ethan R.;Pao, Gerald M.;Koyamada, Koji;Sugihara, George
• 通讯作者: Sugihara, George

Recent developments in empirical dynamic modelling

经验动态模型的最新进展

• DOI: 10.1111/2041-210x.13983
• 发表时间: 2023
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
• 作者: Munch, Stephan B.;Rogers, Tanya L.;Sugihara, George
• 通讯作者: Sugihara, George

Frequently asked questions about nonlinear dynamics and empirical dynamic modelling

• DOI: 10.1093/icesjms/fsz209
• 发表时间: 2020-07-01
• 期刊: ICES JOURNAL OF MARINE SCIENCE
• 影响因子: 3.3
• 作者: Munch, Stephan B.;Brias, Antoine;Rogers, Tanya L.
• 通讯作者: Rogers, Tanya L.

Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata

• DOI: 10.1177/1536867x211000030
• 发表时间: 2021-03-01
• 期刊: STATA JOURNAL
• 影响因子: 4.8
• 作者: Li, Jinjing;Zyphur, Michael J.;Laub, Patrick J.
• 通讯作者: Laub, Patrick J.

Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits

减少白令海白眼狭鳕渔业中奇努克鲑鱼兼捕的综合激励措施：个人可交易遭遇积分

• DOI: 10.1016/j.rsma.2018.06.002
• 发表时间: 2018
• 期刊: Regional Studies in Marine Science
• 影响因子: 2.1
• 作者: Sugihara, George;Criddle, Keith R.;McQuown, Mac;Giron-Nava, Alfredo;Deyle, Ethan;James, Chase;Lee, Adrienne;Pao, Gerald;Saberski, Erik;Ye, Hao
• 通讯作者: Ye, Hao