Regulation of biological signalling by temperature (ROBUST)

农业是欧洲工业的基础，年营业额超过1万亿欧元，对我们的生存至关重要。随着资源的减少和世界人口的增长，我们对农业的需求也将增加。随着未来几十年的气候变化，目前的趋势表明全球气温将上升。不仅平均气温将发生变化，而且天气系统也变得越来越不可预测：今年前所未有的霜冻导致加州柑橘作物歉收，使该行业损失4.5亿美元。对农业需求的增加以及全球气候和极端天气的变化是21世纪科学面临的重大挑战。为了应对这一挑战，我们需要了解植物如何应对和保护温度变化。同样的问题也适用于所有生物系统中的其他环境因素，因此，理解这一点是实验和理论科学家的主要目标。近年来，还原论科学，其中生物途径的研究是孤立的，还没有确定植物温度传感器。它也不能解决温度如何影响跨越许多相互作用的途径，我们现在知道这些途径都参与其中。我们采取多学科的方法，并将我们的研究集中在植物中最具特征的信号网络之一。我们将结合联合收割机专业知识，从生物学家，专门从事分子和细胞生物学，植物生理学和气候变化;和理论家，专门从事统计，数学和计算机科学的方法来分析和模拟生物系统。为了提供重要的独立专业知识和合作途径，我们邀请了来自行业和学术界的专家小组，每年与我们会面。我们将分析温度如何影响光，24小时时钟和冷信号的相互关联的路径。我们在模式植物拟南芥中进行我们的研究，因为它提供了几个优点：1。我们已经为我们的一部分网络开发了最先进的工厂信号数学模型; 2.我们的网络途径已经很好地定义，在拟南芥中有许多有用的工具和资源;和3。在具有经济和生态重要性的植物中，这些途径似乎是密切相关的，因此我们的结果可以很容易地应用于其他物种。为了获得温度调节分子事件如何转化为重要生理性状的有意义的观点，我们将在分子，细胞和整株植物水平上进行分析。我们的第一个任务将是扩展我们的模型与我们的网络的其余部分的预先存在的知识。我们将测量所有网络组件在一系列温度下的响应，并将这些数据整合到我们的初步模型中。这种方法将以无偏的方式定位网络的温度敏感和温度耐受部分：重要的一点是，重要的温度响应不是由单个组件引起的，而是由许多组件共同作用引起的。如果没有计算机模型，我们无法理解这种复杂性。我们的模型将有助于为我们的实验提供信息，从而找到控制网络特性的分子机制。最后，我们将测试重要的网络组件在控制整个植物的农业和生态相关性状中的作用。总之，该项目将开发工厂中最先进的信号网络模型，定义允许响应性和耐受性的网络功能，并识别工厂温度传感器。我们的工作将解决生物学中的基本问题，并创建所需的知识基础，以应对开发能够更好地承受一系列气候条件的作物的挑战。我们的多学科合作还将为没有当前专业知识的大学和我们的工业合作者提供“系统生物学”方法的培训和推广。

Agriculture underpins European industry with an annual turnover of more than ¤1 trillion and is essential for our survival. As resources dwindle and world populations grow, our demands on agriculture will also increase. As climate changes in the coming decades, current trends suggest that global temperatures will rise. Not only is mean temperature set to change but weather systems are also becoming less predictable: an unprecedented frost this year resulted in a failure of the Californian citrus crop, costing the industry $450 million. The combination of increased demand on agriculture and the changes in global climate and weather extremes represent a major challenge for science in the 21st century. To meet this challenge, we need to know how plants both respond to and protect against temperature changes. The same issues apply to other environmental factors across all biological systems, therefore, understanding this is a major goal for experimental and theoretical scientists. In recent years reductionist science, where biological pathways are studied in isolation, has not identified plant temperature sensors. It also cannot address how temperature effects that cross the many, interacting pathways, which we now know are involved. We take a multi-disciplinary approach and focus our studies on one of the best characterised signalling networks in plants. We will combine expertise from biologists that specialise in molecular and cell biology, plant physiology and climate change; and theoreticians that specialise in statistical, mathematical and computer science approaches to analyse and model biological systems. To provide vital independent expertise and avenues for collaboration we have invited a panel of experts from industry and academia, to meet with us on a yearly basis. We will analyse how temperature influences the interlinked pathways of light, 24-hour clock and cold signalling. We conduct our studies in the model plant Arabidopsis as it offers several advantages: 1. we have already developed the most advanced mathematical model in plant signalling, for a section of our network; 2. our network pathways are already well defined, with many useful tools and resources in Arabidopsis; and 3. the pathways in plants of economic and ecological importance appear to be closely related, so our results can readily be translated to other species. To capture a meaningful view of how temperature-regulated molecular events translate to important physiological traits we will conduct our analysis at molecular, cellular and whole plant levels. Our first task will be to expand our model with the pre-existing knowledge for the rest of our network. We will measure the response of all our network components over a range of temperatures and integrate these data into our preliminary model. This, approach will locate the temperature-sensitive and -tolerant parts of the network in an unbiased fashion: the important point is that the temperature responses that matter will not be caused by single components, but by many acting together. We cannot understand this complexity without computer models. Our models will help inform our experiments, to home in on the molecular mechanisms that control the network's properties. Finally, we will test the role of important network components in controlling agriculturally and ecologically relevant traits in whole plants. In summary, this project will develop the most advanced signalling network model in plants, define network features that permit responsiveness and tolerance, and identify plant temperature sensors. Our work will address fundamental questions in biology and create the knowledge base required to meet the challenge to develop crops better able to withstand a range of climatic conditions. Our multidisciplinary collaboration will also provide training and extension of 'Systems Biology' approaches to universities with no current expertise and to our industrial collaborators.