Overview

In light of our guiding principles specificity, regulation and biocontainment, we modelled four different aspects of BeeT. The modelling work can inform and improve wet-lab experiments, providing a more robust and well rounded final product. Another facet is to assess the optimal application strategy for our project. We asked ourselves; what critical parts of our system can benefit the most from an interplay between modelling and experimental work? These considerations led us to ask the following questions;

• How can we assure optimal toxin production using quorum sensing and sub populations?
• What are important parameters for the killswitch to function optimally?
• Will BeeT be capable of surviving in sugar water?
• What is the best application strategy for BeeT?

Quorum Sensing

For the final product, BeeT, we intend to use toxins produced by Bacillus thuringiensis also called BT toxins. However, these toxins are also harmful to our chassis and would result in a reduction of toxin production. To counteract this effect we envision the use of quorum sensing that activates BT toxin production only when there is a large quantity of BeeT present. Synchronization of BT toxin production across the entire population would result in BeeT only producing a single burst of BT toxin before dying to its effects. Ideally, BeeT is able to produce BT toxin over a long period and dramatically improving effectiveness. To accomplish this we need multiple sub populations of BeeT, some producing BeeT while others are recuperating. This project is ideal for dynamic modelling as it represents a complex system with tune-able parameters, each parameter set can produce dramatically different population dynamics.

Introduction
For the iGEM project a toxin producing system has been made. We wanted to create a system where bacteria can produce toxin in waves and hereby create different cell populations. With the use of quorum sensing and a toxin/anti-toxin system, as shown in Figure 1, we expect to find different cell populations.

Methods
During the research Matlab version R2016a has been used.
Because there was no data from the wet lab we assumed that all the parameters in the system were random. The parameters are all obtained by latin hypercube Latin hypercube is a statistical method to get random numbers from a box of n by n numbers. For example x = 4 with x is divisions and n = 2 with n is number of samples. You will obtain a box with 4 square times 2 square, give you 24 random numbers. For each parameter one number out of this box is randomly chosen. samples.

What is quorum sensing
Quorum sensing is a cell-cell communication system. The detection of chemical molecules allows the bacteria to distinguish between low and high cell densities, in this way control gene expression in response to changes in cell number ¹.

Why subpopulation system

How could quorum sensing develop spatial inhomogeneities in toxin/anti-toxin systems?
When quorum sensing ensures that the toxin is only produced when the density of bacteria is high enough to produce significant amounts of toxin, this ‘standardizes’ the amount of toxin produced by the bacteria. The toxin/anti-toxin system will be coupled to the quorum sensing system. Together, quorum sensing and forming of non-producing subpopulations by the toxin/anti-toxin system, allow bacteria to produce ‘waves’ of toxin.

Results

Quorum sensing

Subpopulation

When lambda is present in big amounts the RFP response will be high. You need a lot more lambda than 434 to get high RFP responses. This can be expected when you look at the subpopulation system, the system is inhibited by 434 which represses the RFP production and lambda activates the RFP production. In the Heat Map 1 you can see that there is little difference between the 434 and lambda amounts that are present for the output of RFP. This means that the initial conditions do not have so much influence on the 434 and lambda. With this data we can conclude that the translation rates are more important for the RFP response than the initial conditions.

Combined system

Conclusion

Quorum sensing system

Light Kill Switch

To prevent BeeT from escaping into the environment around the hive and spreading we built a light kill switch. This system consists of a light switch that triggers when blue light hits the organisms. This light switch is coupled to a toxin/anti-toxin system, when the light switch is triggered it inhibits the production of the anti-toxin. With the anti-toxin production inhibited, the constitutive production of toxin kills BeeT. This system was also modeled using dynamic modelling, this was to ensure that the system only kills BeeT in the presence of blue light and it survives when no blue light is present.

The final BeeT product will be applied using sugar water which is transported into the hive by worker bees. This means that our chassis, Escherichia coli is exposed to high osmotic stress. When we started our project it was unclear whether E. coli is capable of surviving in sugar water for long periods of time. If it unable to do so we need to chose an alternative application method. T

Metabolic Modeling

In order to assess the real world viability of the BeeT we evaluated the proposed system of application by making a model of the entire system. To do this we used Flux Balance Analysis (FBA) to make model the base chassis. The chassis The chassis is the base organism that is modified of BeeT is a variant of ​ Escherichia coli, for which it is known that it does not grow in sugar water, mainly due to high osmotic pressure. ² The question remained: Does it survive there, and if so, for how long?

What is Flux Balanace Analysis

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks.

Key Results

The relationship between maximum ATP available for survival and water efflux is shown in Figure 1, it demonstrates that there is a linear relation. This implies that if no water is available for ATP used for maintenance outside cell growth, the cell will die. When the model is run without any modification, ie in an environment where it is in the exponential growth phase an ATP Maintenance flux of 3.15 mmol*gram Dry Weight^-1*hour^-1 is given as output by the model.

We do not know the amount needed in sugar water conditions, but because of these results we can start looking at the relationship between survival time and water efflux.

In Figure 2 we can see not only the relationship of survival time against max ATP available for survival, but also how different thresholds of minimal cell-water tolerance would affect this relationship. The minimal cell-water tolerance threshold gives the value at which percentage of the remaining cell-water the point of no return for the cell had been reached. Which has a drastic effect on survival time, changing 20 minutes of maximum survival time to a mere ~90 seconds in the worst case scenario.

References

1. Eri Nasuno, Nobutada Kimura, Masaki J. Fujita, Cindy H. Nakatsu, Yoichi Kamagata, and Satoshi Hanada (2012). Phylogenetically Novel LuxI/LuxR-Type Quorum Sensing Systems Isolated Using a Metagenomic Approach Vol, 78, number 22. ↩

2. Cheng, Y. L., Hwang, J., & Liu, L. (2011). The Effect of Sucrose-induced Osmotic Stress on the Intracellular Level of cAMP in Escherichia coli using Lac Operon as an Indicator. Journal of Experimental Microbiology and Immunology (JEMI) Vol, 15, 15-21. ↩

2. Neidhardt F.C. Escherichia coli and Salmonella: Cellular and Molecular Biology. Vol 1. pp. 15, ASM Press 1996 ↩