Dynamic modelling of the iron deficiency modulated transcriptome response in Arabidopsis thaliana roots

The iron deficiency response in plants is a complex biological process with a host of influencing factors. The ability to precisely modulate this process at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues. Despite the collected experimental data describing different aspects of the iron deficiency response in plants, no attempts have been made towards aggregating this information into a descriptive and predictive model of gene expression changes over time. We formulated and trained a dynamic model of the iron deficiency induced transcriptional response in Arabidopsis thaliana. Gene activity dynamics were modelled with a set of ordinary differential equations that contain biologically tractable parameters. The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behaviour of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors and predict the effect of double regulator mutants. The presented modelling approach illustrates a framework for experimental design, data analysis and information aggregation in an effort to gain a deeper understanding of various aspects of a biological process of interest.