This webapp aims to provide a simulation of oscillatory behavior in the Arabidopsis root, which underlies the formation of lateral root primordia.
Check out the demo at https://why-jay.github.io/root-growth.
This project is being developed as part of a research project at Duke University's Benfey Laboratory.
Arabidopsis lateral roots form at periodic intervals along the main root. Evidence suggests that the activator-inhibitor theory might explain this periodic behavior. In an activator-inhibitor system, a group of cells expresses an activator protein and secretes an inhibiting mobile substance. This substance represses its own expression and the expression of the activator. As new cells form and the secreting cells move away from the inhibitor source cells, the concentration and thus the effect of the inhibitor decline, allowing a new phase of activator- and inhibitor-expressing cells.
The theory of activator-inhibitor systems has successfully explained a few other biological systems. Our computational model is designed to simulate the root growth and the periodic behavior underlying lateral root initiation, using the partial differential equations from the theory. The formation of lateral roots in Arabidopsis shows a number of characteristics that justify this approach. For example, certain cells in the oscillation region express putative secreted peptides, possibly to prevent neighboring cells from developing into lateral roots or into peptide-secretors. As new cells emerge in the root meristem, distal cells are exposed to a reducing concentration of these inhibiting peptides, which might allow them to initiate a new phase of peptide expression. Additionally, these peptides should inhibit the expression of cell-autonomous activators, which are assumed to prime the cells as competent to become lateral root primordia. The entire dynamic is described by two PDEs which we will use as the basis of our in-silico model.
The objectives of the model are twofold. First, it is expected to support biological observations. Second, changing parameters can simulate various genetic manipulations, such as removing the inhibiting substance to mimic a knockout. Such simulations will help design and predict further experiments.
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