Choosing the right moment to start flowering is essential for the reproductive success of plants. Hence, various signals are integrated in order to decide when to make the switch to flowering. In this project, we use Ordinary Differential Equations (ODEs) as a computational approach to study the regulatory network that integrates the various flowering time signals in Arabidopsis thaliana. As starting point, an ODE-model for the network is available (Leal Valentim 2015). Parameters in this model have been fitted against available time-courses of expression levels at 23°C of the network components.
The goal of this case study is to extend the model to incorporate an additional gene, FLM. Depending on temperature, one FLM splicing isoform or the other is most prominent, and the two alternative isoforms, FLM-β and FLM-δ, have different effects on flowering time (Posé 2013). Low temperature favors the formation of SVP-FLMβ complexes, that repress flowering. However, as temperature increases, not only is the amount of FLMβ downregulated, but the repressive function of SVP and FLM-β is also counteracted by a relative increase in FLMδ. The effect of the SVP/FLM complexes occurs via their regulation of SOC1. Various datasets are available to use for the model: expression time courses of the different genes at 23°C, flowering time of wild type and mutants at various temperature (16°C, 23°C, 27°C), relative expression levels of the two FLM splice variants at these temperatures, and a time course for the two FLM variants at 23°C.
After an initial model has been obtained, it could be tested/expanded in various directions, e.g. using available data on differences in response to temperature of various Arabidopsis accessions, or by considering additional effects of temperature (e.g. temperature is known to effect SVP protein stability as well).