Details of talk

TitleDeterministic and Stochastic Model of Arabidopsis Flowering
PresenterMaia Nikolova Angelova (Deakin University)
Author(s)Maia Nikolova Angelova
SessionContributed Talks
Time11:00:00 2017-09-25
Abstract


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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