Details of talk
|Title||Deterministic and Stochastic Model of Arabidopsis Flowering|
|Presenter||Maia Nikolova Angelova (Deakin University)|
|Author(s)||Maia Nikolova Angelova|
Experimental studies of the flowering of Arabidopsis Thaliana have shown that a large and complex Gene Regulatory Network (GRN) is responsible for its regulation. We consider a deterministic and stochastic delayed non-linear dynamical model based on six stochastic differential equations and two delays. By decoupling, the GRN is reduced to sub-network composed of the transcription factor Suppressor of Overexpression of Constants 1 (SOC1) and two essential floral meristem identity genes, Leafy (LFY) and Apetal (AP1). We consider three motifs from the reduced network, based on LFY and AP1. The steady state regimes and the effects of stochasticity are investigated analytically and numerically for the full and reduced models. The results contribute to the understanding of the roles of LFY and AP1 in the flowering of Arabidopsis Thaliana.
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