Team:Newcastle/Modelling/Community
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 is 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 [1], 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 biofilm formation process.
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 solely on the nitrogen fixers behaviour.
The other bacteria growth is described by the first order kinetics (Reaction 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. Cells perform run and tumble, sample the chemoattractant concentration in periods of time Δt memory and compare it to the current concentration; C(t). If the value of C(t) - C(t – Δt memory) 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.
Naringenin forms the gradient according to the finite volume method of Fick’s law. The simulation domain is divided into nonoverlapping subdomains and the flux between them is calculated with the equation shown in Reaction 2. The chemical is degraded with rate kA (Reaction 3). All data and sources are provided in Table 1.
Table 1.
| Parameter | Value | Source |
|---|---|---|
| Growth Rate Herbaspirillum seropedicae | x | growth curves (link) |
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REFERENCES & Attributions
1. Naylor J, Fellermann H, Ding Y, Mohammed W, Jakubovics N, Mukherjee J, Biggs C, Wright P, Krasnogor N. Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations. ACS Synthetic Biology 2016. DOI: 10.1021/acssynbio.6b00315 (link)
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Attributions: Patrycja Ubysz, Connor Trotter