In this part we will model the amplification module. Our work in this module is subdivided in three main parts: a deterministic model of the reactions at the local scale, another version of the former taking into account random noise and perturbations, and a model including diffusion in space and time.
In the deterministic model, we determine the sensitivity of our system and we link it to the signaling module. Then, in the diffusion part we check if our system has a fast answer. Eventually, in the random perturbations model, we check that it is robust to perturbations.
Overview
Remark:
When our biosensor detects a dipeptide specific to a pathogen,the bacterium activates the production of a green fluorescent
protein (GFP). In our system the production of GFP begins when the production of an other protein, the adenylate cyclase (Ca) begins. Indeed, they are under the control of the same promotor, paraBAD (pBad), and thus they have exatly the same behavior:
The protein GFP is a reporter protein for adenylate cyclase. Thus, in the development, gfp will be omitted, and we will consider that the adenylate cyclase gives us the signal.
Why an amplification module?
When one bacterium detects the dipeptide, it will become green. However, if only one bacterium becomes green, we will not be able to observe the signal. That is why we decided to use the communication between the bacteria, called quorum sensing: if one bacterium becomes green, the surrounding bacteria will become green too, and thus we will be able to get the signal.
How to do this?
First, we chose a molecule, which is produced by bacteria and diffuses from bacteria to the other bacterium. We chose cyclic AMP, whose production is catalyzed by the adenylate cyclase. Thus, we designed:
However, with this design, the whole adenylate cyclase would have been used too quickly to produce enough cAMP to enable the communication between the bacteria. That is why we did this modification:
As soon as some cAMP is produced, it will increase the production of adenylate cyclase, which will catalyse the production of even more cAMP, and so forth. However, if some adenylate cyclase was produced when it shouldn’t (because of the promotor let off for example), the system would have started. Thus, we needed to increase the robustness of our system to false positives which we did by adding a classic feed forward loop. The production of the aenylate cyclase now begins if and only if there is enough cAMP AND enough of an intermediary protein, here AraC. We finally got:
Now that we have the topology of the entire system we need to study precisely if it works, and how it works.
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