Team:Wageningen UR/Model
Modeling
Overview
Every biological phenomenon involves numerous components and exists in a complex environment. While certain aspects can be investigated in the lab, we still cannot physically identify, understand, list or represent all factors of influence on Cattlelyst’s systems. In order to grasp the essence of events, mathematical models have become a widely accepted and employed tool in biology [1]. Modeling requires to omit, simplify and conceptualize complexity irrelevant to the research question. Therefore, the validity and applicability of a model depends on the assumptions made by the modeler, as described by statistician George Box All models are wrong, some are useful
[2].
Cattlelyst is developed to reduce the ecological footprint of the livestock sector. The biofilter combines synthetic methane oxidation, nitrification and denitrification for which it relies on genetically modified bacteria. Another key aspect of our project is to preserve the local environment, for which we should prevent the escape of our genetically modified Cattlelyst bacteria. To develop Cattlelyst, we created a conceptual understanding by modeling three key aspects of our project with individual mathematical models:
- Metabolic scale:
- Microbial scale:
- Biofilter scale:
Nitrogen metabolism of combined nitrification and denitrification
The dynamics of our methane-based biosafety circuit
Biofilter size and performance
This page gives an overview of the three different models; the models themselves can be found on Github.
Conventional biological NH3 -removal consists of two distinct steps: nitrification by autotrophs under aerobic conditions, and denitrification by heterotrophs under anaerobic conditions [3]. Nitrifiers and denitrifiers require different growth conditions, which makes the overall conversion of NH3 to nitrogen gas N2 a complex procedure.
Recently, microbes e.g. Pseudomonas spp. have been discovered coupling heterotrophic nitrification to aerobic denitrification (HNAD). These bacteria can complete the conversion of NH3 to N2. As of today, the biological mechanisms behind this conversion have not been untangled completely. To further our understanding of microbial nitrogen conversion pathways, a dynamic model based on a system of ordinary differential equations was built (Figure 1).
With these features implemented, relative dynamics of the system could be studied, pathway bottlenecks could be identified, and hypotheses about HNAD could be generated. Moreover, only minor manipulations of the model are required to qualitatively describe HNAD data from a further Pseudomonas species P. putida ZN1 [4]. These findings demonstrate the model’s predictive power. Moreover, this model can predict nitrogen dynamics for both ammonia-dependent growth and nitrate-dependent growth. This allowed us to forecast nitrogen conversion rates and efficiencies for Pseudomonas cells exposed to a combination of nitrogen sources. A conceptual understanding of microbial nitrogen dynamics laid the foundation for our experimental design to synthetically engineer ammonia conversion in Pseudomonas putida. Specifically, engineering nitrification, synthetic denitrification and reducing nitrous oxide emissions built upon the premises of this model.
Curious? Click here to read more about this dynamic model
It is crucial to prevent the spread of GMO’s into nature, therefore, we designed a biosafety circuit which aims to kill the cells outside of the biofilter. Inside the biofilter, the relatively high methane concentration is converted to formaldehyde [5]. This is sensed by the FrmR protein which is a transcriptional repressor that dissociates from the Pfrm promoter when it binds to formaldehyde [6]. The FrmR protein is coupled to a hok-sok toxin-antitoxin system to kill the cells (see Figure 2) [7].
An ODE model was made to investigate whether the designed biosafety circuit was sensitive to the small differences in formaldehyde concentrations in the cell in high methane conditions (inside the biofilter) versus low methane conditions (outside the biofilter). The circuit needs to be able to only increase the hok/sok ratio enough in low formaldehyde conditions to kill the cell. The formaldehyde sensitivity of the original designed biosafety circuit was too low. To improve the system, ten extensions on the circuit were tested.
To increase the hok/sok ratio in low formaldehyde conditions, a positive feedback loop on toxin mRNA production was shown to be effective. The formaldehyde sensitivity of the biosafety circuit was improved by adding a competitor protein that is expressed under the control of the Pfrm promoter. This hypothetical protein similarly binds to FrmR as formaldehyde does. However, such a protein is not yet found in nature.
This improved biosafety circuit showed formaldehyde sensitivity between 15-30 hours after the system starts. After this time, the hok/sok ratio in high formaldehyde conditions increased which will kill the cells. We learned from the model that the methane based safety circuit is not sensitive enough to only kill the cells that escape from the biofilter. Therefore, in the laboratory experiments, we aimed to put toxin production under control of a hybrid promoter which is activated upon low cell densities, outside of the biofilter and repressed by LacI [9].
Curious? Click here to read more about the biosafety model
Although biofilters can be an effective and economical tool to clean air with both pollutants, biofiltration of ammonia and methane emissions have mainly been investigated separately [10]–[14]. To investigate the combined reduction of methane and ammonia emissions, a biofilter model was developed that determines the reactor size and performance for the microbial culture options we have considered for Cattlelyst. The model combines metabolic and reactor modeling with three different scales based on mass balances: single cell, single particle, and reactor scale. The chosen parameters describe the average conditions in a cow stall with 100 milking cows. Both natural and synthetic methanotrophs and nitrification-aerobic denitrification performing bacteria were compared across a range of ammonia and methane conversion efficiencies.
The model simulations showed that the methane and ammonia efficiencies are limited by methane diffusion in the biofilm. Nevertheless, the biofilter can reach similar methane and ammonia conversion efficiencies as biofiltration units designed for methane abatement with similar dilution rates (gas flow rate divided by reactor size) [14]–[16]. However, a relatively large reactor size (103−104 m3)is needed to accommodate this with the low pollutant concentrations and high gas flow rates. Synthetically engineering methane uptake only slightly improved the biofilter: the increase in performance at the same conversion efficiencies of methane and ammonia was low in comparison to normal methane uptake. The biofilter bottleneck lays in the transport of methane into the biofilter; this should be improved to truly see the benefits of increased methane uptake. Nevertheless, decreasing the gas flow rate also decreases the reactor size, thus, a hood system greatly improves the system. Although the biofilter still needs improvement, it does prove simultaneous reduction of methane and ammonia emissions is possible with wild type and synthetic biofilms.
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References
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