Team:INSA-UPS France/Model
iGEM UPS-INSA Toulouse 2017
Model overview
Our general strategy is based on i) the detection of V. cholerae using as input signal a quorum sensing molecule, ii) a molecular communication between two organisms thanks to the production of a signaling metabolite and its detection, and finally iii) the production of antimicrobial peptides as output, at a lethal dose for Vibrio cholerae. Compared to synthetic biology approaches based on a single microorganism, multi-organisms synthetic biology implies a more complex global behaviour. To describe and predict the behaviour of the microbial consortium, we have developed a mechanistic, dynamic model that can be used to test the feasibility of the system and improve its behaviour:
Would the quorum sensing molecule (CAI-1) induce a sufficient answer to activate the sensor (Vibrio harveyi)? Would the receptor be able to produce enough molecular message (diacetyl) to communicate with the effector Pichia pastoris? Would the effector produce enough antimicrobial peptides to deliver the guessed output, which is the lysis of V. cholerae to reach a non-toxic concentration?
A model was also crucial regarding the entrepreneurship and the integrated human practices parts of our project: we needed to show to clients and investors, but also to citizens, how our system would work, how we will dimensionate our device, and how long do you have to wait before drinking a non-contaminated water.
Working with a complex biological system involving three microorganisms and several molecules, the Systems Biology Graphical Notation (SBGN) was a perfect way to ordinate the system and represent the interactions between organisms and the molecules involved. A system of Ordinary Differential Equations (ODEs) was then written to gather the physical and biological processes and simulate the dynamics of our microbial consortium. Precise data from articles and experiments have been used and are listed on the ODEs page. This mathematical model have been used to simulate our system, and analyse its sensitivy and robustness using an extension of Metabolic Control Analysis (MCA). Our model had impacts on wet lab strategy and device design, and have been modified with experimental data, which is summed up in optimization. Finally, to discuss with non-mathematicians, a user-friendly interface has been created.
Modeling a microbial consortium
Approaches
