Team:TU-Delft/Modeling/StochasticSensitivitySpecificityAnalysis

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Twenty simulations were done for each three different input concentrations. The results of which are in the figure below.
Twenty simulations were done for each three different input concentrations. The results of which are in the figure below.
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[[File:CLE20nM.png|575px|left|thumb|'''Figure 1''': Hybrid model of the modified yeast pathway]]
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[[File:CLE20nM.png|575px|left|thumb|'''Figure 1''': Stochastic Simulation of the System for 20nM Ligand concentration]]
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[[File:CLE200nM.png|575px|left|thumb|'''Figure 2''': Hybrid model of the modified yeast pathway]]
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[[File:CLE200nM.png|575px|left|thumb|'''Figure 2''': Stochastic Simulation of the System for 200nM Ligand concentration]]
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[[File:CLE2muM.png|575px|left|thumb|'''Figure 3''': Hybrid model of the modified yeast pathway]]
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Revision as of 23:17, 26 October 2012

Team:TUDelft/CSSLaksh Menu

Single Cell Model


Stochastic Model

In Saccharomyces cerevisiae, stochasticity (noise) arising from transcription contributes significantly to the level of heterogeneity within a eukaryotic clonal population [7].
Figure 11: Hybrid model of the modified yeast pathway
In order to investigate the effects of this stochasticity, we decided to build a stochastic model of the pathway using a Hybrid ODE-SDE framework (where SDE stands for stochastic differential equation). Data from [5] suggests that the Pheromone signalling is robust against cell to cell variations. Motivated by this fact, we assume the dynamics until the activation of the transcription factor to be deterministic rather than stochastic. As a result of the gene expression being noisy, we build a hybrid stochastic model consisting of deterministic semantics until the activation of the transcription factor Ste12 and treat it as a time varying parameter modulating the reaction based gene expression module interpreted with stochastic semantics using the stochastic differential equations approach [8], the schematic of which is given in Figure 11.

Twenty simulations were done for each three different input concentrations. The results of which are in the figure below.

Figure 1: Stochastic Simulation of the System for 20nM Ligand concentration
Figure 2: Stochastic Simulation of the System for 200nM Ligand concentration

conclusion

  • The mean of the stochastic simulation runs closely follow the deterministic solutions.
  • It helped us assess the degree of variablity that is present within the system
  • The time duration of the project limited further applications of this model, the proposed use of which is presented in the future works section.
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