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 eventually iii) the detection of this signal to activate 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, understand and predict the behaviour of the microbial consortium, we have developed a mechanistic, dynamic model. This model allowed to test the feasibility of the system, its robustness, and ultimately optimize its behaviour by adapting some of the parameters that we can control (e.g. the initial concentrations of each microbial specie).
Would the quorum sensing molecule (CAI-1) induce a sufficient response to activate the sensor (Vibrio harveyi)? Would the receptor be able to produce enough molecular message (diacetyl) to transmit the signal to the effector Pichia pastoris? Would the effector produce enough antimicrobial peptides to deliver the expected output, which is the death 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 works, 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 as well as the molecules involved. A system of Ordinary Differential Equations (ODEs) was then written to describe and integrate the individual physical and biological processes to simulate the dynamics of our microbial consortium. Many data gathered from publications and experiments have been used to build the kinetic model. This model has been used to simulate the functioning of our system and understand its properties by analyzing its sensitivity and robustness using different mathematical tools (global sensitivity analyses, extension of the metabolic control analysis framework). Following an iterative systems biology approach, our model has driven both the wet lab strategy and the device design, and has been updated with experimental data, which is summed up in optimization. Finally, to discuss with non-mathematicians, a user-friendly interface has been created.
Our system is composed of microorganisms interacting by responding to stimulations (inputs) and producing a molecular response (outputs). This is a perfect example of a systematic use of biological material, at the scale of both a unique microorganism and a microbial consortium.
Abbreviations:
Our SBGN representation is a way to present our system to different interlocutors (biologist, bioinformatician, mathematician), and to make it reusable and adaptable. This SBGN representation is also an rational way to visualize and inventory all the elements we had to consider to design our synthetic system.
The dynamics of our microbial consortium was summed up in twelve differential equations determining the concentrations of all the components (from genes to populations) and their dynamics. Every biological or physical process was described mathematically, and was gathered to constitute our system of ODEs. Data from publications and from preliminary experiments (growth kinetics essay) were used to define our parameters. An ODEs solver from MATLAB R2017a was used to simulate the system. Under realistic conditions, simulation showed we needed to wait less than a hour (53.6 minutes) to reach a non-pathogenic concentration. Two tools used in metabolic modeling and systems biology were used to analyze different properties of the synthetic system, such as robustness and sensitivity, and understand how we can control it:
- Global sensitivity analyses were carried out to determine if our system was robust to perturbations of parameters
Global sensitivity analysis confirmed the robustness, showing a small variation of the response time when the parameters are varying randomly. Extension of Metabolic Control Analysis (MCA) showed low control coefficients, which prooves that each parameter exerts a limited influence on our model. It highlighted the parameters having the biggest influence: antimicrobial peptides properties, Pichia pastoris and Vibrio cholerae initial concentrations and device volume. The influence of two major parameters (Pichia pastoris and Vibrio cholerae initial concentrations) is described on the 3D graph above. These two parameters were chosen to create a simulation interface. We wanted to show the behaviour of our synthetic system without the use of numerical computing environment such as Matlab. Thus, we developed a visual interface with Javascript to show the kinetics of Vibrio cholerae death depending of two major parameters. Our mathematical model was useful for different parts of our project, and was also implemented by other parts:Modeling a microbial consortium from gene to population
Approaches
SBGN: a standard representation of models in systems biology
“Systems biology is based on the understanding that the whole is greater than the sum of the parts.”
(1)
Systems biology is a new approach to understand and to manipulate biological material. It proposes to represent biological systems such as networks transforming inputs into outputs that could be modeled mathematically, similarly to technological and electronical devices.(1) It requires communication between biochemists, mathematicians and computer scientists. The SBGN (Systems Biology Graphical Notation) project has been launched in 2005 to facilitate this communication. It proposes a standard notation to represent biochemical and cellular processes, and to easily share biological systems to the community.(2) The standard notation can be found there.
ODEs
→ Data, solver files and simulation results can be found in our simulation page
Sensitivity and robustness
→ Sensitivity analysis methods and their results are described in our model analysis page
Visual interface
→ Our visual interface is available on our interface page
Model driven optimization
References