Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator
Fig 3
Network inference and analysis by dynamical differential expression (DyDE).
(A) Ordinary Differential Equations (ODEs) capture the dependence of the rate of the concentration of a transcript on the concentration of another transcript. First order linear models are used to represent the dynamics between two genes. Here, a good agreement (plain line) with the data (dotted line) was found (57% goodness of fit). (B) The inverse regulation is considered. In this case, it is not possible to find a combination of parameters so that a first order linear model captures the dynamics involved. For this inverse regulation the model that best described the data obtained a goodness of fit of only 16%. (C) A threshold by which each model is (in)validated is applied on the goodness of fit of the models. As an example, a threshold of 46% would consider a link from TOC1 to PRR9 but not the other way around. The same threshold is applied to all models. (D) A first order linear model is evaluated in the presence of nicotinamide between the same species. The nu gap is then applied to compare models (A) and (D) to quantify whether the models are similar, or significantly affected by NAM.