Unseasonal frosts this April destroyed roughly 100,000 tonnes of European fruit after a mild late winter, and scientists are now building mathematical models to predict how plants cope with such temperature shocks. Current understanding of cold response in plants comes almost entirely from lab experiments under constant temperatures. But in the field, plants face wild daily swings—warm afternoons followed by freezing nights—while also managing two separate cold responses: long-term winter adaptation (vernalisation) and short-term stress preparation (cold acclimation). These processes interact in ways constant-temperature studies miss, partly because temperature fluctuations interfere with the plant’s internal circadian clock. The team will use the model plant *Arabidopsis thaliana* to build predictive gene-regulatory-network models, then test them against real fluctuating temperature profiles and mutant plants. This is fundamental science: it aims to uncover the molecular wiring that integrates temperature signals across timescales. If successful, the models will identify which gene variants—and combinations of variants—confer resilience to erratic cold. Breeders could then target those homologous genes in crops like wheat or oilseed rape, while the same models could flag high-risk temperature profiles for policymakers and farmers.
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This project aims to uncover the molecular mechanisms of how plants respond to cold in fluctuating natural environments and thereby provide a foundation for breeding more climate-change-resistant crops. Reduced or unexpected cold periods are already causing significant losses in crop yields. This year, much of Europe experienced very mild conditions in late winter followed by extreme frost in April which led to flowers dying with approximately 100,000 tonnes of fruit lost as a result. Such extreme and unseasonable conditions are only expected to worsen with further climate change. Therefore, we need to understand plant temperature responses to aid plants in coping with (and without) the cold and to prepare our crops for the future. There are two aspects of cold response in plants, long-term winter response (vernalization) and short-term preparation for cold stress (cold acclimation). These two aspects have traditionally been studied separately, with different genes found to be important in each case. However, in the field these processes are occurring simultaneously, and recent work has identified overlaps between them. Until now, these cold responses have been primarily studied under constant temperature conditions, but in the field, plants can experience wide temperature fluctuations within each day. We have found that plants respond differently to fluctuating temperatures than would be predicted based on their constant temperature responses. Part of this difference is caused by the interplay between response to temperature fluctuations and the circadian clock, the internal timekeeping mechanism of plants. This delicate interplay, combined with the two timescales of response and the complexity of the naturally-occurring fluctuating temperature profile, necessitates the use of mathematical modelling to uncover the underlying molecular mechanisms of cold response. Our work will use the plant model system Arabidopsis thaliana, where extensive datasets in constant temperatures are already available and where many of the genes important in those conditions are known. We will develop predictive and testable mathematical models to simulate our hypothesised gene regulatory network and improve and refine this through cycles of modelling and experiments. We will use these models to predict the consequence of environmental and genetic changes, and we will test these predictions by treating plants under new temperature profiles and using mutants. Zooming into a subsection of this network will allow us to unpick how the structure of the network integrates temperature information. Finally, we will investigate the interplay between the full cold response network and other processes, such as the circadian clock and growth. This work will allow us to understand the gene network and make predictions about different mutants and their combinations based on the model. The model predictions will therefore form a basis to inform breeding decisions for homologous genes in crops. We will also use different temperature profiles to make model predictions in order to test robustness and identify high risk conditions, thus informing policy, prioritisation and agricultural practices.
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