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Team:Groningen/Template/MODULE/project/MBD
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Model-based design
Why modeling?
Modeling is an important tool used for understanding the behavior of variables without testing it in real time. From designing small genetic circuits to space shuttles, modeling plays a pivotal role. In our case, modeling is the backbone of our project.
The main focus of the modeling in this project lies in aiding the development of the actual prototype. The results from modeling should help the design of a bandage that detects pathogens in burnwounds and secretes molecules that either kill- or inhibit growth of these pathogens. Key here is the ability to produce nisin, DspB and AHLase upon sensing quorum molecules from the pathogens. In order to provide useful information to the ‘material’-people in our team, we made a first model that shows how nisin, DspB and AHLase are produced by L. lactis and diffuse through the bandage. By using this model, we can estimate which of six possible designs (LINK) is the best.
Awesome! The ‘why modeling’-question has been answered, let’s show its actual use!
Staphylococcus aureus and Pseudomonas aeruginosa are the two pathogens that cause most infections in burn wounds. The aim of our project is to design a smart bandage that can produce Infection Prevention Molecules (IPMs) only in the presence of these two pathogens. This kind of bandage will reduce the problem of antibiotic resistance by reducing the amount of antibiotics applied to the wound. The IPMs are produced by our genetically modified Lactococcus lactis. These bacteria are fixed in the hydrogel. It is key that the molecules produced by L. lactis reach the infected wound. Understanding the nuances that are associated with the diffusion of IPMs is therefore important for us.
To start with actual modeling, we will first give an overview of the parameters involved in the modeling.
Model-based bandage design
Our main goal for this project is to design a bandage prototype for burn wounds. Burn wounds are mainly infected with S. aureus and P. aeruginosa. The quorum molecules produced by these two pathogens should diffuse through the bandage and activate the production of Nisin, Aiia and DspB proteins. These three proteins should diffuse out of the bandage and act on the pathogens.
Figure 4:
Scheme for modeling the bandage
To evaluate different bandage designs, we develop a multi-scale dynamic model of the bandage. The bandage is discretized into lattices where each lattice contains differential equations describing the growth of bacteria, production of nisin, production of Aiia, production of DspB and the detection of quorum molecules. Apart from the differential equations for the productions of the three IPM molecules we also consider the diffusion parameters. This makes the model more dynamic to study characteristics of our bandage.
Each state variable in each lattice is initialized according to the different bandage designs. Each lattice contains few bacteria which uses glucose as nutrient source and grows. Actively growing bacteria produce Nisin, Aiia and DspB only in response to the quorum molecules produced by both Staphylococcus aureus and Pseudomonas aeruginosa. In presence of quorum molecules in the lattice, the bacteria starts producing Nisin, Aiia and DspB. Nisin, Aiia and DspB produced in a lattice diffuses to nearby lattices until equilibrium is reached.
Studying the diffusion rates of Nisin, Aiia and DspB is important to estimate the time taken to reach the threshold concentrations. The threshold concentration is the minimum concentration of the proteins that is required to breakdown biofilm, kill S. aureus and quorum quench P. aeruginosa population. The diffusion constants for these three proteins were not available directly.
For more info on the rate equations look here.
Bacteria modeling
Text on bacteria modeling
Figure 1:
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Modeling experiments
The hydrogel in the bandage should have nutrient source for the bacteria to grow. Using rich media like M17 in the bandage for growing Lactococcus lactis is not a good idea. Rich media might support the growth of other bacteria’s present on the wound which might cause adverse problems. To avoid this kind of complications we decided to use chemically defined media. Chemically defined media is a buffered media containing all the aminoacid, vitamins and other metal suppliments.
1. Glucose Concentration optimization for Nisin production
To increase the lifetime of the bandage we decided to increase the amount of carbon source. Lactococcus lactis is lactic acid producing bacteria, increasing the amount of carbon source in the media results in faster production of lactic acid. Lactic acid present in the media represses the growth of bacteria. The presence of phosphate buffer in the chemically defined media solves this problem to some extent. It has been reported that Nisin is produced only in exponential growth phase. In order to evaluate the glucose concentration at which Nisin production is higher we grew Nisin producing strain in CDM media and every two hours sample was collected to perform Nisin activity test.
To increase the lifetime of the bandage we decided to increase the amount of carbon source. Lactococcus lactis is lactic acid producing bacteria, increasing the amount of carbon source in the media results in faster production of lactic acid. Lactic acid present in the media represses the growth of bacteria. The presence of phosphate buffer in the chemically defined media solves this problem to some extent. It has been reported that Nisin is produced only in exponential growth phase. In order to evaluate the glucose concentration at which Nisin production is higher we grew Nisin producing strain in CDM media and every two hours sample was collected to perform Nisin activity test.
Figure 5:
Diffusion experiment wilt L. lactis over time.
Based on the nisin activity assay we got similar diameter of halo for 20 g/l and 40 g/l of glucose concentration. We analyzed the supernatants using HPLC to find the exact concentrations of nisin. HPLC analysis showed that at 20 g/l of glucose concentration we get higher nisin yield.
Figure 5:
Growth curve for 20 g/l of glucose concentration.
initial conditions 2 matlab
initial conditions 3 matlab
initial conditions 4 matlab
initial conditions 5 matlab
initial conditions 6 matlab
two dimension model
growth curve L. lactis
Results
Based on the simulation data we compared our design outcome with the design requirements.
Performance table
| Design | Tnisin | TAiiA | TDspB | Ease of manufacturing | Rating |
|---|---|---|---|---|---|
| 1 | 0.4 | 0.5 | 0.5 | Too easy | 1 |
| 2 | 0.4 | 0.5 | 0.5 | Moderate | 3 |
| 3 | 0.3 | 0.3 | 0.3 | Moderate | 2 |
| 4 | 0.2 | 0.3 | 0.3 | Moderate | 1 |
| 5 | 1.9 | 2.2 | 2.4 | Difficult | 5 |
| 6 | 1.9 | 2.3 | 2.4 | Difficult | 4 |
