Our cell model examines the characteristics of the FrmR and promotor PfrmAB regulatory pathways. By this model, we predicted the behaviour of our biosensor under it’s designed conditions. If this is consistent with the experimental observations, we can confirm that the FrmR and promotor PfrmAB tuning networks operate in the same way as originally assumed.[1]
The main modeling environment used in the project is the MATLAB SimBiology toolkit, a plug-in designed for biology of computational systems.The toolbox transforms the basic mathematics of system biology into a graphical interface, making it an ideal choice for gene conditioning network modeling. SimBiology will be used with deterministic differential equation solver methods. In other words, each time the simulation is run, the calculations are the same and give the same solution. This means that there is no randomized result. In real life, the number has a certain degree of random variation, which means a random model is useful. However, on the cell population, these random variants yielded definitive results on average.

Our cellular model develops on the characterization of the hoxBC-hoxJ and hoxA regulation pathway as well as predicting the behavior of our Hydrogen biosensor under its designed conditions, through which we can learn more details about the procession of hoxBC-hoxJ and hoxA regulation pathway[1].
The main modelling environment utilized in this project is the MATLAB SimBiology toolbox, a plugin designed specifically for computational systems biology. SimBiology will be utilized with a deterministic differential equation solver method. However in real life, there is some degree of random variation in the quantities - this is where a stochastic model would be useful. However, over a cell population these random variations average out to give deterministic results.