Modeling

We are first making a steady-state model, using the SimBiology software on MATLAB. The steady-state scenario is considered in order to find when the production rate of the green fluorescent protein would match its dilution and degradation rates. The final steady-state model would show two plots demonstrating the response of the system to an increase of NO in the negative signal and an increase of adenosine in the positive signal. We are then planning to make a dynamic model by breaking down the process to its basic reactions and kinetic laws, so we could find the concentration of any species at any time. The dynamic model should in the end show plots of different species’ concentrations as a function of time. We plan to use the dynamic model to carry out more analysis on how different parameters change the output or how sensitive the output is with respect to any given parameter. We could use this analysis to optimise the overall process by increasing certain values and repressing others (eg. we could alter the number of base pairs on the sRNA in order to control its binding affinity to fRNA). Next by the end of September, we plan to have a working program, where we could input concentrations of certain molecules such as IL-10 and model the response of the system by means of time. The program should also determine the amount of overshoot a specific number of e-coli could have, in order to to retain a healthy concentration of IL-10. However all this modelling would be based on specific assumptions, and would not accurately recreate the real world where random parameters are involved in the process. Therefore we would carry out some stochastic analysis on all the three previously created models, using statistics to give a better understanding of how the model would function in the real world.

Gold Medal Criterion #3

Convince the judges that your project's design and/or implementation is based on insight you have gained from modeling. This could be either a new model you develop or the implementation of a model from a previous team. You must thoroughly document your model's contribution to your project on your team's wiki, including assumptions, relevant data, model results, and a clear explanation of your model that anyone can understand.

The model should impact your project design in a meaningful way. Modeling may include, but is not limited to, deterministic, exploratory, molecular dynamic, and stochastic models. Teams may also explore the physical modeling of a single component within a system or utilize mathematical modeling for predicting function of a more complex device.

Please see the 2018 Medals Page for more information.