Difference between revisions of "Team:Newcastle/Modelling/Community"

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                   <p><font size="3">The method of modelling we have chosen is an agent-based model that allows us to see how changes in the rate of naringenin production influences the behaviour of the whole nitrogen fixing bacteria community. The software we used is SimBiotics, the agent-based modelling tool developed at Newcastle University. SimBiotics presents a way to visualise our stochastic simulations via real-time animations. Supported by the data from our chemotaxis experiments and growth curves (link), the model can accurately predict the ratio between bacteria populations forming the biofilm on a root.</font></p>
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<p><font size="3">Due to a lack of time and computational resources, we have excluded competition factor from the model assuming an infinite amount of resources. To make the model even simpler we have set the Pseudomonas layer to be steady. As no Pseudomonas growth is observed so we can focus on the nitrogen fixers behaviour. </font></p>
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<p><font size="3">The other bacteria grow with first order kinetics (Figure 1). To obtain understanding of bacterial growth, we monitored the change in absorbance (600nm) of our 3 nitrogen fixing bacteria grown at 30˚c for 72 hours. This data was then converted into cell density after experiments to identify cell count at specific optical densities. Through doing this, we obtained a conversion ratio. This allowed us to understand growth rate in a way that could be accurately incorporated into the model. </font></p>
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<p><font size="3">The bacteria’s chemotactic movement is modelled with a modified version of micromotility and tumble run [2]. Cells perform run and tumble, sample the chemoattractant concentration in periods of time dtmemory and compare it to the current concentration; C(t). If the value of C(t) - C(t – dtmemory) is lower than one, the cell is more likely to tumble. Otherwise, a probability to tumble decreases with increasing gradient and the bacterium is less likely to stop running [2]. </font></p>
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<p><font size="3">Naringenin forms the gradient according to the finite volume method of Fick’s law [2]. The simulation domain is divided into nonoverlapping subdomains and the flux between them is calculated with the equation shown as Figure 2. The chemical is degraded with rate kA (Figure 3). All data and sources are provided in Table 1. </font></p>
  
  

Revision as of 15:49, 4 October 2018

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Model

Alternative Roots

Microbial Community

Introduction Model Design

Introduction

One of the applications for root-colonising Pseudomonas fluorescens (CT 364) as a chassis organism proposed was to produce a naturally occurring chemical – naringenin. The substance, as demonstrated in our laboratory (link), attracts free-living nitrogen fixing bacteria. Under the right conditions, this would benefit the plant’s nitrogen nourishment and possibly reduce synthetic nitrogen fertilizers usage. Although we already transformed Pseudomonas fluorescens with an operon with genes for naringenin biosynthesis, there is still a long way to test the system on plants. Plants need a lot of time to grow compared to microorganisms. Understanding how the root-colonising bacteria and the nitrogen fixers behave in the soil would be time intensive. To have an early insight and provide visualisations for the public, we developed the microbial community modelling to imitate what’s happening in the soil around the inoculated root.

Model Design

The method of modelling we have chosen is an agent-based model that allows us to see how changes in the rate of naringenin production influences the behaviour of the whole nitrogen fixing bacteria community. The software we used is SimBiotics, the agent-based modelling tool developed at Newcastle University. SimBiotics presents a way to visualise our stochastic simulations via real-time animations. Supported by the data from our chemotaxis experiments and growth curves (link), the model can accurately predict the ratio between bacteria populations forming the biofilm on a root.

Due to a lack of time and computational resources, we have excluded competition factor from the model assuming an infinite amount of resources. To make the model even simpler we have set the Pseudomonas layer to be steady. As no Pseudomonas growth is observed so we can focus on the nitrogen fixers behaviour.

The other bacteria grow with first order kinetics (Figure 1). To obtain understanding of bacterial growth, we monitored the change in absorbance (600nm) of our 3 nitrogen fixing bacteria grown at 30˚c for 72 hours. This data was then converted into cell density after experiments to identify cell count at specific optical densities. Through doing this, we obtained a conversion ratio. This allowed us to understand growth rate in a way that could be accurately incorporated into the model.

The bacteria’s chemotactic movement is modelled with a modified version of micromotility and tumble run [2]. Cells perform run and tumble, sample the chemoattractant concentration in periods of time dtmemory and compare it to the current concentration; C(t). If the value of C(t) - C(t – dtmemory) is lower than one, the cell is more likely to tumble. Otherwise, a probability to tumble decreases with increasing gradient and the bacterium is less likely to stop running [2].

Naringenin forms the gradient according to the finite volume method of Fick’s law [2]. The simulation domain is divided into nonoverlapping subdomains and the flux between them is calculated with the equation shown as Figure 2. The chemical is degraded with rate kA (Figure 3). All data and sources are provided in Table 1.





REFERENCES & Attributions

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Attributions: Patrycja Ubysz, Connor Trotter