Team:Wageningen UR/project/model overview

Modeling Overview

The performance of BananaGuard as biological control agent is defined bythree factors: its ability to destroy or inhibit Fusarium oxysporum upon detection, its ability to compete with other micro-organisms in the soil and the stability and performance of the genetic circuit. If the fungal growth inhibitors cannot destroy or inhibit F. oxysporum, the bananaplants will die. This aspect of BananaGuards performance is being tested in vitro. This leaves remaining two questions to be answered. The performance and stability of the genetic circuit is crucial for BananaGuard to work, even if the fungal growth inhibitors inhibit or destroy F. oxysporum they need to be produced when fungus is present. This requires two aspects of the system to function properly: the toggle-switch within the Kill-switch system needs to be stable and the fusaric acid dependent promoter needs to be able to activate the system in the presence of fusaric acid. Promoter design constitutes the first step assessing stability in the form of optimization. Different configurations of promoter elements on the promoters of the Kill-switch were modeled using statistical mechanics. Parameters such as repressor binding strengths and repressor production rates were changed every simulation. Scoring the bi-stability of the Kill-switch, the promoter element configuration with the most simulations showing bi-stability were considered to be most likely to work. Including the anti-gene transfer inhibition effect on cell growth rates and the fusaric acid dependent promoter, this optimized version of the BananaGuard system was subsequently modeled over a population of dividing cells in the final step, assessing both its stability and performance in the form of a stochastic model. Running thousands of simulations incorporating noise terms and promoter leakiness the stability of the BananaGuard system could be quantified. The included stochasticity caused every cell division cycle to have slightly different outputs, resulting in fractions of fast and slow growing cell populations with either stable or unstable Kill-switches. The same was done for the performance, upon induction by fusaric acid a portion of the cell population activated their Kill-switch or remained in the same state. In order to make the simulations as accurate as possible a parameter for the maximum growth rate of BananaGuard was required. This parameter was provided in the process of answering the competitiveness query. To ascertain our hosts competitiveness the rate of cell division needs to be investigated on a metabolic level. Cell growth rate is a key factor in the competition between our engineered Pseudomonas putida and the wildtype P. putida found in the rhizosphere of the banana plant roots [1,2]. The integrated genetic circuit uses metabolic resources that would otherwise be dedicated to cellular maintenance or growth. Should this divergence of resources be too large our host will be outcompeted. A genome scale metabolic model was extended, investigating and outlining the cost of introducing our synthetic genes on a metabolic level.

Key results

Promoter Design

Before the optimal configuration of repressor binding sites could be determined, the behaviour of the system had to be assigned scores representing a functioning or non-functioning Kill-switch. Clustered, three different behaviours could be identified:

In total eight different configurations of repressor binding sites were tested (Figure 2). Two, F and H, were found to be moderately robust towards variations in binding strengths and production rates. H was chosen as a base to design new promoters for the Kill-switch. Biologically it meant that there be two TetR repressor binding sites on the LacI promoter, two LacI and one CIλ repressor binding site on the TetR promoter and two TetR and one CIλ repressor binding site on the toxin promoter

More information and results of promoter design can be found here

Cost

To check if BananaGuard is still viable in its resting and active state (activated by fusaric acid), a comparison was made between BananaGuard and the wild type P. Putida. The results of this comparison indicates the impact of the synthetic pathway on its metabolism and the metabolic stress when anti-fungal production has been activated. To get the most realistic approach the carbon composition and ratio of the banana roots exudates are used. Figure 1 depicts how BananaGuard compares to the wild type P. Putida depending on the carbon uptake rate. For bacteria in the rhizosphere of the banana roots a carbon uptake rate of 4 mmol gDW^-1hr^-1 is expected. This shows a growth rate of >99% compared to the wild type for the resting state and still over 50% for the active state. This indicates that BananaGuard is not outcompeted by other rhizosphere-populating microorganisms and that metabolic stress is not a bottleneck for the production of anti-fungals in our activated system.

More information and results of the system cost can be found here

Performance

Two histogram plots (figure. 4) illustrate the key results of the stochastic model. The stability and distribution of populations with different basal production rates of CIλ and the activation of the system upon fusaric acid induction. Large distribution of division times can be seen in the population. These distributions can be attributed to the free toxins in the cell caused by stochastic fluctuations in the native toxin antitoxin system and a varying basal production rates of the kill-switch protein CIλ.

Figure 4.A indicates that a basal CIλ production rate of 50nM or higher will be devastating to the stability of the genetic circuit in a large portion of the population and shifts the average growth rate of the population. The long division times caused by the leaking toxins, which in turn is caused by the slow CIλ build-up, result in the kill-switch to change state. A low basal production rate of CIλ would result in faster growth and more stability.

Figure 4.B shows that slower growth rates activate the system more efficiently because CIλ is not diluted as much. Given the growth rate obtained from the metabolic model an activation efficiency of 98% is achieved. This means that the vast majority of our reengineered P. putida population will activate.

More information and results about the stability and performance of the system can be found here

Implications for the experimentalist and the system

Promoter Design: The statistical mechanics model has led to the experimentalists decision to opt for a new set of designed promoters and build two kill-switches in parallel. The model has predicted the newly designed promoters to have a higher stability. These needs to be verified experimentally.

Cost: To check if BananaGuard is still viable in its resting and active state (activated by fusaric acid), a comparison was made between BananaGuard and the wild type P. Putida. The results of this comparison indicates the impact of the synthetic pathway on its metabolism and the metabolic stress when anti-fungal production has been activated. The results for both the resting and active state show that BananaGuard is not outcompeted by other rhizosphere-populating microorganisms and that metabolic stress is not a bottleneck of the anti-fungal production relatively. This means that for our application it is not necessary to change the proteins in the kill switch or produce less or different anti-fungals.
The calculated growth rate for BananaGuard for a doubling time of 3 h^-1 for the wild type P. Putida is used to calculate the system performance.

Performance: The results have shown that leakiness of promoters on the input/output plasmid (CIλ and Zeta-toxin) can be detrimental to the performance of our reengineered P. putida. A point of attention when the entire system is tested.

The activation of our system is limited by the production of CIλ and not the influx of fusaric acid. The fusaric acid detection experiments has shown the fusaric acid dependent promoter to be activated with a limited amount of fusaric acid present. The activation occurs more efficiently if the cell growth rate is low. This means that it is highly probable that the majority of BananaGuards population will activate given a generally slower growth rate in stressful environments