Deterministic and stochastic models of Arabidopsis Thaliana flowering

Experimental studies of the flowering of Arabidopsis Thaliana have shown that a large complex gene regulatory network (GRN) is responsible for its regulation. This process has been mathematically modelled with deterministic differential equations by considering the interactions between gene activators and inhibitors [26, 28]. However, due to complexity of the model, the properties of the network and the roles of the individual genes cannot be deducted from the numerical solution the published work offers. Here, we propose simplifications of the model, based on decoupling of the original GRN to motifs, described with three and two differential equations. A stable solution of the original model is sought by linearisation of the original model which contributes to further investigation of the role of the individual genes to the flowering. Furthermore, we study the role of noise by introducing and investigating two types of stochastic elements into the model. The deterministic and stochastic non-linear dynamic models of Arabidopsis flowering time are considered by following the deterministic delayed model introduced in [26]. Steady state regimes and stability of the deterministic original model are investigated analytically and numerically. By decoupling some concentrations, the system was reduced to emphasise the role played by the transcription factor Suppressor of Overexpression of Constants1 (SOC1) and the important floral meristem identity genes, Leafy (LFY ) and Apetala1 (AP1). Two-dimensional motifs, based on the dynamics of LFY and AP1, are obtained from the reduced network and parameter ranges ensuring flowering are determined. Their stability analysis shows that LFY and AP1 are regulating each other for flowering, matching experimental findings. New sufficient conditions of mean square stability in the stochastic model are obtained using a stochastic Lyapunov approach. Our numerical simulations demonstrate that the reduced models of Arabidopsis flowering time, describing specific motifs of the GRN, can capture the essential behaviour of the full system and also introduce the conditions of flowering initiation. Additionally, they show that stochastic effects can change the behaviour of the stability region through a stability switch. This study thus contributes to a better understanding of the role of LFY and AP1 in Arabidopsis flowering

Mathematical modelling of arabidopsis flowering time gene regulatory network

Experimental studies of the flowering of Arabidopsis Thaliana have shown that a large complex gene regulatory network (GRN) is responsible for its regulation. This process has recently been modelled with deterministic differential equations by considering the interactions between gene activators and inhibitors [Valentim et al., 2015, van Mourik et al., 2010]. However, due to the complexity of the models, the properties of the network and the roles of the individual genes cannot be deduced from the numerical solution the published work offers. In this study, deterministic and stochastic dynamic models of Arabidopsis flowering GRN are considered by following the deterministic delayed model introduced in [Valentim et al., 2015]. A stable solution of this model is sought by its linearisation, which contributes to further investigation of the role of the individual genes to the flowering. By decoupling some concentrations, the system has been reduced to emphasise the role played by the transcription factor Suppressor of Overexpression of Constants1 (SOC1) and the important floral meristem identity genes, Leafy (LFY) and Apetala1 (AP1). Two-dimensional motifs, based on the dynamics of LFY and AP1, are obtained from the reduced network and parameter ranges ensuring flowering are determined. Their stability analysis shows that LFY and AP1 are regulating each other for flowering, matching experimental findings (see e.g. [Bl´azquez et al., 2001, Welch et al., 2004, Yeap et al., 2014]). Moreover, the role of noise is studied by introducing and investigating two types of stochastic elements into the motifs. New suffient conditions of mean square stability and their domain are obtained analytically for the stochastic models using Lyapunov stability theory. Numerical solutions are obtained by using Euler-Maruyama method and Ito stochastic formula. We demonstrate that the stochastic motifs of Arabidopsis flowering time can capture the essential behaviour of the full system and that stochastic effects can change the behaviour of the stability region through a stability switch. Furthermore, the problem of designing an observer and a controller, in which FT is seen as a control input, is considered in the objective of ensuring flowering conditions are met. This study thus contributes to a better understanding of the role of LFY and AP1 in Arabidopsis flowering