Difference between revisions of "Team:Imperial College/GRO"

\[\frac{-b\pm\sqrt{b^2-4ac}}{2a}\]

gro is a programming language developed at the University of Washington which simulates the behaviour of cells in microcolonies including intrinsic and extrinsic noise.In this way the platform allows for a basic stochastic model to simulate the multicellular behaviour of our circuit. The model we have produced is a high level model, designed to visually communicate the behaviour of our system at a population level.
After implementing a simplified version of the circuit with GP0.4 growth regulation into two populations of e.coli, the simulation was run 30 times per model. 8 models in total were run, 6 which use our circuit, and 2 control systems to compare the results, and all 30 runs were then plotted on a single graph per model with an average to show the [ average] behaviour of the system. Below each set of results is a small description of the model corresponding to those results.

Graph1

Graph2
Above are the results of the models which implemented each stage of our molecular circuit from the cell receiving the quorum molecule to the binding of GP0.4 to the FtsZ molecules of that cell. The model was run 60 times in total, 30 starting at a ratio of 0.6 and 30 starting from a ratio of 1.5. Each plot has an average plotted in black.

This model shows underdamped oscillations which reach the desired ratio of 1:1 at roughly 170 minutes. Furthermore it can be seen that the further away from the desired ratio the initial ratio is at the beginning of the simulation, the longer it would take to reach the final ratio as expected. Hence in graph 1 the time to reach a ratio of 1:1 is x minutes whereas in graph 2 it is y minutes due to the value of the initial ratio.

A control for each of these starting ratios was also run (figure x), with no circuit implemented to compare the random growth of two e.coli populations against the growth of e.coli populations in which we have implemented our circuit. Due to the nature of the control, each population is shown to grow at the ratio it began at, whereas in reality we expect that one population will outcompete the other for nutritional resources and space. These aspects were not implemented into the initial model due to computational power and hence for continuity were also not implemented into the control. The full code for both of these models and all following models can be found on our github.