Team:Sevilla/Modelling

Our approach to the modeling of our project is centered in the properties of wires. A wire is composed by three elements: EHW, the Emitter Half-Wire, a cell with the hability of produce an arbitrary BioBit i with a spacefic rate; the SINK, a cell with the hability of degradate the BioBit i with a specefic rate and a RHW, the Receptor Half-Wire, a cell that can sense the concentration of i in the culture media and induce gene expresion in response to it. Our project is based in CBW, Chemical-Based Wires, so the BioBit is a chemical compound, in our case, a HSL molecule from quorum sensing, it doesn't matter which one (we also consider the LBW, Light-Based Wires, where a light with a specific spectrum is the BioBit). We are going to assume that the RHW and concentration of i, [i], is solved. We don't want to know the association constant of the molecule to its receptor, but the relation between the concentration of i and the induction of a reporter gene. It would be perfect if we could know the concentration of a BioBit i inferred from the properties of the EHW and the SINK, the two types of cells, in our case bacteria, that are going to define the final concentration of the BioBit i.

The first consideration we must know is: Is our circuit going to run in the exponential phase of growth, or in the stationary phase? We are going to assume that it is going to work in the stationary phase, where the bacterial densities are constants. So, let next equation be the rate of BioBit i production:

Where K_B is the rate of biosynthesis of BioBit i by the EHW and ρ_E is the density of EHW bacteria in the culture, and K_D is the rate of degradation of BioBit i by the SINK and ρ_S is the density of SINK bacteria en the culture. Now, you can see the sequence of calculus until get the primitive:

Now, we assume that the concentration of i(t) at t=0 is i(0) to find the value of the constant:

And this is the BioBit i concentration function of time and and taking into account the density of bacteria and the rates of production and degradation of i:

These activities are complemented by other groups in the SynBioNT Synthetic Biology Network for Modelling and Programming Cell-Chell Interactions.

Infobiotics is the synergy of executable biology, evolutionary and machine learning methods, mesoscopic simulation techniques and experimental data for a more principled practice of origins of life, bioinformatics, computational systems and synthetic biology research.

These activities are complemented by other groups in the SynBioNT Synthetic Biology Network for Modelling and Programming Cell-Chell Interactions.

Infobiotics is the synergy of executable biology, evolutionary and machine learning methods, mesoscopic simulation techniques and experimental data for a more principled practice of origins of life, bioinformatics, computational systems and synthetic biology research.

These activities are complemented by other groups in the SynBioNT Synthetic Biology Network for Modelling and Programming Cell-Chell Interactions.