<p>Flowering time, the moment that plants switch from vegetative to reproductive growth, is a key parameter for yield and adaptation to environmental conditions. Proper timing is essential to ensure optimal reproduction. Plants have evolved elaborate networks that sense the environment, including various stresses, and integrate this information with endogenous signals to determine whether or not to flower.</p>
Proper regulation of flowering time is essential for plant adaptation to the environment and to ensure optimal yield. A key environmental signal that influences flowering time is the variation in ambient temperature. Knowledge about the molecular components and networks involved in ambient temperature control of flowering time is accumulating, but quantitative understanding of this process is missing. However, such understanding is essential to understand plant adaptation to changes in temperature, which is relevant in the context of e.g., climate change, but also for the prediction of flowering time for plant breeding and crop production.
Here we propose to apply a systems biology approach to flowering time control by external signals in Arabidopsis thaliana. Besides the ambient temperature signalling pathway we will include the photoperiod pathway in our modelling, because this pathway has extensive cross-talk with temperature.
The basis will be a network of transcription factors integrating flowering time related signals. We will obtain models for these different networks, connect them with each other, and apply the models to study the dynamics of the network. Flowering time has a substantial effect on the growth of the vegetative phase, i.e. at the moment of flowering the maximum biomass of the vegetative part is reached and resources are allocated for the reproductive part of the plant. Therefore, we will include plant growth and plant production as a next integration level in the models.
Importantly, we will make use of the large amount of data that are currently available in our lab. This includes time course data at the protein and RNA level, flowering time data for numerous mutants and Arabidopsis ecotypes, various datasets describing temperature effects, etc. Our aim is to obtain a model that predicts how temperature affects flowering time and subsequently, how it relates to biomass production.
Plants have evolved elaborate networks that sense the environment, including various stresses, and integrate this information with endogenous signals to determine whether or not to flower. Genetic and molecular studies on Arabidopsis identified many stimulating and repressing factors involved in these flowering time pathways. Pathways involved in the decision to start flowering include the autonomous pathway, gibberellin pathway, the photoperiod pathway, the vernalization pathway (which involves cold induction during the winter), and the ambient temperature pathway.A small increase in temperature can already lead to a potent triggering of flowering, although there is extensive natural variation in this response. How temperature is perceived and how this external signal is translated into molecular actions is largely unknown. Nevertheless, in the last few years, important information about ambient temperature regulation and the factors involved has been obtained.
A Quantitative and Dynamic Model of the Arabidopsis Flowering Time Gene Regulatory Network
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Combining modelling and experimental data towards modelling the regulatory network of flowering time genes