Predicting plant growth in naturally fluctuating conditions

预测自然波动条件下的植物生长

• 批准号: BB/Y513945/1
• 负责人: Antony Dodd
• 金额: $ 32.31万
• 依托单位: John Innes Centre
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513945%2F1
• 关键词: Predicting; plant; growth; naturally; fluctuating

There is an urgent need to understand how climate change will impact plant growth and development in natural and agricultural environments. Plants adapt their physiology and development to the local environment by detecting and responding to the environmental conditions. Daily (24 h) and seasonal environmental fluctuations have a profound impact on plant growth and development. Responses of plants to these regular changes often involve the circadian clock. This is a cellular timer that coordinates physiology and development with the 24 h day, and makes essential contributions to growth, stress responses and fitness of plants, including crops. Much of our knowledge of plant circadian rhythms and their benefits come from experiments in controlled (laboratory) conditions. In contrast, the regulation and consequences of circadian rhythms in field-grown plants are poorly understood. We thus have a major knowledge gap in an area that has great importance for predicting the growth and development of both crops and natural plant populations. Gaining this knowledge will be key for understanding how current and future climates, with altered daily and seasonal temperature patterns, impact plant growth and crop performance.Understanding the importance of circadian rhythms under naturally fluctuating conditions represents a major challenge. The complexity of this problem coupled with the availability of extensive yet largely unexplored datasets make this problem attractive both for exploiting the latest developments in AI, and evaluating their performance. Applying this to field-grown plants requires expertise in circadian regulation, field transcriptomics, and data science. To achieve this, we have assembled a team combining expertise of Antony Dodd (John Innes Centre, JIC) in chronobiology, Pirita Paajanen (JIC) in pure mathematics and data science, and Hiroshi Kudoh (Kyoto University) in molecular ecology. Through this combination of expertise, we will advance understanding of how environmental alterations caused by climate change, such as warmer nights and winters, will affect the outputs of plant circadian regulation. To harness our multidisciplinary collaboration, we include a comprehensive set of researcher exchanges to allow joint working and cross-training, developing a new research area with considerable potential for extension into questions about circadian programs in natural plant populations and crops.

迫切需要了解气候变化将如何影响自然和农业环境中的植物生长和发育。植物通过探测和响应环境条件，使其生理和发育适应当地环境。日（24 h）和季节环境波动对植物生长发育有深远的影响。植物对这些规律变化的反应通常与生物钟有关。这是一个细胞计时器，与一天24小时协调生理和发育，并对植物（包括作物）的生长、应激反应和适应性做出重要贡献。我们对植物昼夜节律及其益处的了解大多来自于受控（实验室）条件下的实验。相比之下，在田间种植的植物中，人们对昼夜节律的调节和后果知之甚少。因此，我们在一个对预测作物和自然植物种群的生长和发育具有重要意义的领域存在重大的知识缺口。获得这些知识将是了解当前和未来气候如何影响植物生长和作物性能的关键，这些气候会改变日常和季节性的温度模式。理解昼夜节律在自然波动条件下的重要性是一个重大挑战。这个问题的复杂性，加上大量尚未开发的数据集的可用性，使得这个问题对利用人工智能的最新发展和评估它们的性能都很有吸引力。将其应用于田间种植的植物需要在昼夜节律调节、田间转录组学和数据科学方面的专业知识。为了实现这一目标，我们组建了一个团队，将安东尼·多德（约翰·英纳斯中心，JIC）在时间生物学方面的专业知识，皮里塔·帕亚宁（JIC）在纯数学和数据科学方面的专业知识，以及Hiroshi Kudoh（京都大学）在分子生态学方面的专业知识结合起来。通过这种专业知识的结合，我们将推进对气候变化引起的环境变化（如温暖的夜晚和冬天）如何影响植物昼夜节律调节输出的理解。为了利用我们的多学科合作，我们包括一套全面的研究人员交流，以允许联合工作和交叉培训，开发一个具有相当大潜力的新研究领域，扩展到自然植物种群和作物的昼夜节律程序问题。