GRAPPLE - Iterative modelling of gene regulatory interactions underlying stress disease and ageing in C. elegans

GRAPPLE - 秀丽隐杆线虫应激性疾病和衰老的基因调控相互作用的迭代模型

• 批准号: BB/I004718/1
• 负责人: Madan Babu
• 金额: $ 26.77万
• 依托单位: MRC Centre Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI004718%2F1
• 关键词: GRAPPLE; Iterative; modelling; gene; regulatory

Systems biology aims to model quantitatively how complex systems work, the resulting model being refined by means of an iterative loop of experimental testing and further modelling. Our project is unique in the sense that we will identify regulatory interactions underlying stress response and lifespan using a combination of natural genetic variation in gene transcription profiles, identification of regulatory and regulated genes and intensive computational modelling. The resulting networks will then be tested and refined using iterative perturbation experiments. In this way the new data will generate new, stronger network models to define genes for perturbation analysis, which in turn will furnish new data for modelling using new computational tools, and so on. The novelty and the strength of our approach is also based on simultaneous use of a variety of new methods for statistical analysis of networks, including those for network matching to be developed by the members of our team. To date, systems biology has not taken advantage of natural genetic variation in predicting the regulatory interactions underpinning important biomedical phenotypes. By combining the most powerful experimental and computational methods with the simplest model animal system, our project will significantly advance both our understanding of a complex regulatory system with direct relevance to human health and in the advancement of methodology that can be applied to other systems. Molecular and quantitative geneticists within this EU-wide group will work together with modellers to connect the dots from genome to phenotype, and on to predictive uses in biomedicine and healthcare.

系统生物学的目标是定量地模拟复杂系统的工作方式，通过实验测试和进一步建模的迭代循环来完善最终模型。我们的项目在某种意义上是独一无二的，我们将通过结合基因转录谱中的自然遗传变异、调节和受调节基因的鉴定以及密集的计算模型来确定应激反应和寿命背后的调节相互作用。然后将使用迭代摄动实验对所得网络进行测试和改进。通过这种方式，新的数据将产生新的、更强大的网络模型来定义基因进行扰动分析，这反过来将为使用新的计算工具进行建模提供新的数据，等等。我们方法的新颖性和优势还基于同时使用各种新的网络统计分析方法，包括我们团队成员将开发的网络匹配方法。迄今为止，系统生物学还没有利用自然遗传变异来预测支撑重要生物医学表型的调控相互作用。通过将最强大的实验和计算方法与最简单的模型动物系统相结合，我们的项目将大大促进我们对与人类健康直接相关的复杂调节系统的理解，并推动可应用于其他系统的方法的进步。这个欧盟范围内的小组的分子和数量遗传学家将与建模者一起工作，将从基因组到表型的点连接起来，并在生物医学和医疗保健方面进行预测。

项目成果

Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins.

• DOI: 10.1038/nsmb.3441
• 发表时间: 2017-09
• 期刊: Nature structural & molecular biology
• 影响因子: 16.8
• 作者: Chavali S;Chavali PL;Chalancon G;de Groot NS;Gemayel R;Latysheva NS;Ing-Simmons E;Verstrepen KJ;Balaji S;Babu MM
• 通讯作者: Babu MM

The ribosome-engaged landscape of alternative splicing.

• DOI: 10.1038/nsmb.3317
• 发表时间: 2016-12
• 期刊: Nature structural & molecular biology
• 影响因子: 16.8
• 作者: Weatheritt RJ;Sterne-Weiler T;Blencowe BJ
• 通讯作者: Blencowe BJ