Multiple light input signals to the gene network of the circadian clock

The growth of plants supports life on Earth and is vital to our economy and survival. Plant growth is important not just to farmers and foresters, and the consumers that they supply, but is also now understood to play a critical role in the global carbon cycle, and to affect the climate of our planet. Recent advances have identified the components and workings of the photosynthetic machinery in leaves and many of the genes that control the timing of plant activity, together with tools for monitoring gene activity in the laboratory. By combining these advances, we have shown that the synchronization of biological activity in plants (circadian rhythms) with external day-night cycles confers a growth and survival advantage. This result provides the first evidence of an advantage to daily plant growth arising from rhythmic behaviour; existing models of plant growth cannot account for this behaviour and are thus inadequate. Plant rhythms are synchronised to the day/night cycle, and the principles involved are very similar to the synchronisation of the human body clock that enables travellers to overcome jet lag, and that can be difficult for shift workers to achieve. Our goal now is to determine how this synchronisation works at the level of the clock genes, through a series of experiments and linked modelling studies. Previous studies in my lab and others have identified the relevant clock genes and the photoreceptor pathways involved. Both are complex. My lab has recently proposed the first mathematical models of the plant clock, which allow the many interacting parts to be simulated in a computer. The model led us to new experiments, identifying an additional part of the clock network. This was a first for plant science and is still a rare achievement in any organism, despite a wave of interest in the 'systems biology' approach that often aims to make this type of prediction. We have since developed an extended model that is even more realistic (so far as we know it's the best available anywhere) though it still simplifies or leaves out a lot of our current molecular knowledge. Fascinatingly, the new model very much resembles current models of the clocks in animal brains, despite the fact that those clocks involve connected neurones whereas our model occurs within a single plant cell. We now wish to understand how plants and animals both synchronise their clocks to the day/night cycle, despite the differences in the clock mechanisms. A major part of this proposal is based on our models, testing the effect of light on the clock genes much more carefully than before, and using these data to refine the models. We propose to use a new experimental method to get much finer data than before. This method has been proven to work but has not been extensively tested, nevertheless it is in an area of experiments that we know very well, it promises major benefits that will be useful for many other plant researchers and it will keep us ahead of our competitors. We also propose to extend the types of experiment that have proved reliable in the past. On the modelling side, we will also use new technology, part of it mathematical, part of it exploiting a very fast computer that is available to us through the Physics department in Edinburgh. If this work is successful, it will provide an example for other labs to follow, to understand other complex gene networks with multiple input signals / these are the types of network that control cancer, diabetes and other complex diseases.

The circadian clock is a small gene network with multiple feedback loops, which generates 24-hour biological rhythms in almost all eukaryotes and in some prokaryotes. The dynamics of the clock system are complex, because expression of multiple network components is also forced by the environmental day/night cycle via light signalling pathways. My lab published the first model of the plant clock. We have validated predictions of the model by experiments, and extended the model to account for additional data. The modelling of light inputs remains very simple, as the model was based on data for very simple conditions: 12h light: 12h dark cycles and constant light or darkness. We will now manipulate the light input more quantitatively and dynamically, altering wavelength, duration and fluence rate of light exposure in several experimental protocols. We will also manipulate the photoreceptors that sense the light signal and their target genes in the clock network by mutation, monitoring the system's response using conventional and novel LUC reporter gene methods and high-resolution RNA assays. These data will constrain a new generation of refined models that are calibrated to real fluence rates acting through particular photoreceptor pathways. Technical developments in the computing platform are proposed in order to exploit the IBM BlueGene supercomputer for this work. We will analyse the resulting models to understand the features that contribute to the observed patterns of entrainment, and their similarity to the clocks of animals and fungi. Finally, we will test predictions of the new models by additional experiments, including new protocols inspired by the physical sciences and engineering. This will deepen our understanding of the plant clock as a leading example of plant systems biology.