CAREER: Network-Based Signaling Pathway Analysis: Methods and Tools for Turning Theory into Practice

职业：基于网络的信号通路分析：将理论转化为实践的方法和工具

• 批准号: 1750981
• 负责人: Anna Ritz
• 金额: $ 93.81万
• 依托单位: Reed College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750981&HistoricalAwards=false
• 关键词: CAREER; Network; Based; Signaling; Pathway

Cells receive and respond to signals in their environment, and these signals are often disrupted in disease. Experiments can help understand how proteins interact with each other to alter the cell's behavior; however deciding which proteins to test in an unbiased manner is challenging. Networks, or graphs, are commonly used to represent interactions among proteins, where proteins (nodes) are linked by pairwise interactions (edges). While network-based methods have been popular for many years, predictions from these methods are often challenging to interpret and the tools have not been made easily accessible to biologists, dramatically slowing the potential pace of scientific discovery. The goal of this research is to develop novel methods that more closely reflect the biological questions posed by experimental biologists, and enable the adoption of such tools by the scientific community. This work will be accomplished at a primarily undergraduate institution (PUI), and students who wish to pursue careers in biology need computational training. The project will establish a program for undergraduate training in computational biology at PUIs through local and national initiatives that support both student and faculty development. This project will offer frameworks for (a) introducing computational biology to undergraduates through conference attendance and (b) implementing computational biology activities and courses for undergraduate biology programs with limited resources. Results from this project can be found at http://www.reed.edu/biology/ritz/research.html.Cells respond to their environment using a series of protein-protein interactions, collectively referred to as signaling pathways, that transfer extracellular signals to the regulation of target genes. Computational methods that describe signaling pathways as graphs have been critical hypothesis-generation tools for understanding the relationship among proteins in cellular signaling response. This project identifies a unifying concept in graph theory -- that of computing directed, connected paths in graphs -- and applies this idea to signaling pathway analysis questions posed in multiple fields of biology. Novel path-finding algorithms will be developed to generate mechanistic hypotheses of active signaling, using dysregulated signaling in disease as a case study. These path-finding algorithms will be applied to signaling pathway analysis in cellular and developmental biology, including pathways that regulate changes in cell shape (morphogenesis) and eye development (retinal neurogenesis). Close collaborations with biologists will help inform the development of easy-to-use tools and broaden their applicability to other fields. The final aim will establish hypergraphs, a generalization of directed graphs, as an improved mathematical representation of signaling. The collection of novel methods produced by this project, along with a demonstration that these tools serve as hypothesis generation engines for other fields in biology, will be a significant step towards accelerating the hypothesis generation-validation-testing research cycle. Biological contributions by adopters of these methods will exponentiate this work's impact on scientific knowledge and discovery beyond the computational contributions in this project.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.

细胞接收并响应环境中的信号，这些信号通常在疾病中被破坏。 实验可以帮助理解蛋白质如何相互作用以改变细胞的行为;然而，决定以公正的方式测试哪些蛋白质是具有挑战性的。网络或图通常用于表示蛋白质之间的相互作用，其中蛋白质（节点）通过成对相互作用（边）连接。 虽然基于网络的方法已经流行了很多年，但这些方法的预测往往很难解释，而且生物学家也不容易使用这些工具，这大大减缓了科学发现的潜在速度。 这项研究的目标是开发新的方法，更密切地反映实验生物学家提出的生物学问题，并使科学界能够采用这些工具。 这项工作将在主要的本科院校（PUI）完成，希望从事生物学职业的学生需要计算培训。 该项目将通过支持学生和教师发展的地方和国家举措，在PUI建立一个计算生物学本科生培训计划。 这个项目将提供框架（a）通过参加会议向本科生介绍计算生物学和（B）在资源有限的情况下为本科生生物学课程实施计算生物学活动和课程。 该项目的结果可以在http://www.reed.edu/biology/ritz/research.html.Cells上找到，它们使用一系列蛋白质-蛋白质相互作用来响应环境，这些相互作用统称为信号通路，将细胞外信号转移到靶基因的调节中。 将信号通路描述为图形的计算方法已经成为理解细胞信号应答中蛋白质之间关系的关键假设生成工具。 该项目确定了图论中的统一概念-计算图中的有向连接路径-并将此想法应用于生物学多个领域中提出的信号通路分析问题。 将开发新的寻径算法，以疾病中失调的信号传导为案例研究，生成主动信号传导的机制假设。 这些寻路算法将应用于细胞和发育生物学中的信号通路分析，包括调节细胞形状（形态发生）和眼睛发育（视网膜神经发生）变化的通路。 与生物学家的密切合作将有助于开发易于使用的工具，并将其适用性扩大到其他领域。 最终的目标是建立超图，有向图的推广，作为一种改进的数学表示的信令。该项目产生的新方法的集合，沿着这些工具作为生物学其他领域的假设生成引擎的演示，将是加速假设生成-验证-测试研究周期的重要一步。 这些方法的采用者的生物学贡献将使这项工作对科学知识和发现的影响超出本项目中的计算贡献。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Prefix/Suffix Variation in Retinoic Acid Response Elements

视黄酸响应元件的前缀/后缀变化

• DOI: 10.1145/3388440.3414914
• 发表时间: 2020
• 期刊: Computational Biology and Health Informatics
• 作者: Zhuang, Yuan;Cerveny, Kara L.;Ritz, Anna
• 通讯作者: Ritz, Anna

A Protein-Protein Interactome for an African Cichlid

非洲慈鲷的蛋白质-蛋白质相互作用组

• DOI: 10.1145/3388440.3414916
• 发表时间: 2020
• 期刊: Computational Biology and Health Informatics
• 作者: Preising, Gabriel A.;Faber-Hammond, Joshua J.;Renn, Suzy C.;Ritz, Anna
• 通讯作者: Ritz, Anna

Lowering the Barrier for Undergraduates to Learn about Computational Research through a Course-Based Conference Experience

通过基于课程的会议体验降低本科生学习计算研究的障碍

• DOI: 10.1109/respect49803.2020.9272501
• 发表时间: 2020
• 期刊: and Technology (RESPECT
• 作者: Lazarte, Amy R.;Ritz, Anna
• 通讯作者: Ritz, Anna

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks

• DOI: 10.1089/cmb.2022.0132
• 发表时间: 2022-08
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Ananthan Nambiar;Simon Liu;Maeve Heflin;John Malcolm Forsyth;S. Maslov;Mark Hopkins;Anna M. Ritz

Graphery: a Biological Network Algorithm Tutorial Webservice

Graphery：生物网络算法教程 Web 服务

• DOI: 10.1145/3388440.3414915
• 发表时间: 2020
• 期刊: Computational Biology and Health Informatics
• 作者: Zeng, Heyuan;Ritz, Anna
• 通讯作者: Ritz, Anna