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。随着未来几十年气候的变化，目前的趋势表明全球气温将会上升。不仅平均气温将发生变化，天气系统也变得越来越难以预测：今年前所未有的霜冻导致加州柑橘歉收，给该行业造成了4.5亿美元的损失。农业需求的增加与全球气候和极端天气的变化相结合，是21世纪科学面临的重大挑战。为了应对这一挑战，我们需要知道植物是如何应对和抵御温度变化的。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，在生物途径孤立研究的还原论科学中，还没有发现植物温度传感器。它也不能解释温度的影响是如何跨越许多相互作用的途径的，我们现在知道这些途径是相关的。我们采取多学科方法，重点研究植物中最具特征的信号网络之一。我们将结合专门研究分子和细胞生物学、植物生理学和气候变化的生物学家的专业知识；以及专门研究统计、数学和计算机科学方法来分析和模拟生物系统的理论家。为了提供重要的独立专业知识和合作途径，我们邀请了一个由工业界和学术界专家组成的小组，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联系的途径。我们在模式植物拟南芥中进行研究，因为它具有以下几个优势：1。我们已经为我们网络的一部分开发了最先进的植物信号数学模型；2. 我们的网络通路已经很好地定义了，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转化为其他物种。为了对温度调节的分子事件如何转化为重要的生理性状有一个有意义的看法，我们将在分子、细胞和整个植物水平上进行分析。我们的第一个任务是用网络中其他部分的已有知识来扩展我们的模型。我们将测量所有网络组件在一定温度范围内的响应，并将这些数据集成到我们的初步模型中。这种方法将以一种无偏的方式定位网络中温度敏感和耐受部分：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，我们就无法理解这种复杂性。我们的模型将为我们的实验提供信息，从而专注于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，该项目将开发最先进的植物信号网络模型，定义网络特征，允许响应和容忍，并识别植物温度传感器。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目前没有专业知识的大学和我们的工业合作者提供“系统生物学”方法的培训和扩展。

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.