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

系统生物学的目标是对复杂系统如何工作进行定量建模，通过实验测试和进一步建模的迭代循环来改进结果模型。我们的项目的独特之处在于，我们将结合基因转录谱中的自然遗传变异、调控和调控基因的识别以及密集的计算建模，来确定压力反应和寿命背后的调控相互作用。然后，将使用迭代扰动实验对所得到的网络进行测试和改进。通过这种方式，新数据将生成新的、更强大的网络模型来定义扰动分析的基因，这反过来将为使用新的计算工具进行建模提供新的数据，以此类推。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。到目前为止，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动物模型系统相结合，我们的项目将极大地促进我们对与人类健康直接相关的复杂监管系统的理解，并推动可应用于其他系统的方法的进步。这个欧盟范围内的分子和数量遗传学家将与模型师合作，将这些点从基因组到表型，再到生物医学和医疗保健中的预测用途。

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.