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

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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 solely on the nitrogen fixers behaviour. </font></p>
 
<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 solely on the nitrogen fixers behaviour. </font></p>
 
<p><font size="3">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. </font></p>
 
<p><font size="3">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. </font></p>
−
<p><font size="3">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  &Delta;<sub>t memory</sub> and compare it to the current concentration; C(t). If the value of C(t) - C(t – &Delta;<sub>t memory</sub>) 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. </font></p>
+
<p><font size="3">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  &Delta;<sub>t memory</sub> and compare it to the current concentration; C(t). If the value of C(t) - C(t – &Delta;<sub>t memory</sub>) 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 [1]. </font></p>
−
<p><font size="3">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. </font></p>
+
<p><font size="3">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 [1](Reaction 3). All data and sources are provided in Table 1. </font></p>
 
<p><font size="3">Above certain concentrations, naringenin kills bacteria. The thresholds we set for the bacteria species (excluding Pseudomonas) is based on the experiments we conducted in the biological laboratory and its value is 150μM.</font></p>
 
<p><font size="3">Above certain concentrations, naringenin kills bacteria. The thresholds we set for the bacteria species (excluding Pseudomonas) is based on the experiments we conducted in the biological laboratory and its value is 150μM.</font></p>
 
<p><font size="3">The model consists of three bacterial species (<i>Pseudomonas fluorescens</i>, <i>Herbaspirillum seropedicae</i>, and <i>Azospirillum brasilense</i>). <i>Pseudomonas</i> attaches to the top side of the modelled area which represents the rhizoplane. We described growth of nitrogen fixing bacteria using data from the laboratory and their morphology based on literature sources.</font></p>
 
<p><font size="3">The model consists of three bacterial species (<i>Pseudomonas fluorescens</i>, <i>Herbaspirillum seropedicae</i>, and <i>Azospirillum brasilense</i>). <i>Pseudomonas</i> attaches to the top side of the modelled area which represents the rhizoplane. We described growth of nitrogen fixing bacteria using data from the laboratory and their morphology based on literature sources.</font></p>
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         <tr>
 
         <tr>
 
           <td>Growth Rate <i>Herbaspirillum seropedicae</i></td>
 
           <td>Growth Rate <i>Herbaspirillum seropedicae</i></td>
−
           <td>x</td>
+
           <td>1.314*10<sup>-4</sup> cell per second</td>
 
           <td>growth curves (link)</td>
 
           <td>growth curves (link)</td>
 
            
 
            

Revision as of 18:55, 10 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 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 [1].

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 [1](Reaction 3). All data and sources are provided in Table 1.

Above certain concentrations, naringenin kills bacteria. The thresholds we set for the bacteria species (excluding Pseudomonas) is based on the experiments we conducted in the biological laboratory and its value is 150μM.

The model consists of three bacterial species (Pseudomonas fluorescens, Herbaspirillum seropedicae, and Azospirillum brasilense). Pseudomonas attaches to the top side of the modelled area which represents the rhizoplane. We described growth of nitrogen fixing bacteria using data from the laboratory and their morphology based on literature sources.

μ = Gr ± Gv
Reaction 1. first order kinetics

Ji→j = DcSij/dij(uj-ui)
Reaction 2. flux between neighbouring subdomains (finite volume method of Fick's Law)
Dc - diffusion coefficient, Sij - cross-section, dij distance between the centres of the two subdomains, uj and ui concentrations in the subdomains

A → ⌀ kA
Reaction 3: degradation of naringenin.

Parameter Value Source
Growth Rate Herbaspirillum seropedicae 1.314*10-4 cell per second growth curves (link)
Growth Rate Azospirillum brasilense x growth curves (link)
Naringenin concentration threshold 150 μM Experiment (link)
Diameter, length of Herbaspirillum seropedicae 0.7um, 1.5-5um [2]
Diameter, length of Azospirillum Brasilense 0.5um, 2.9 um [3]
Diameter, length of Pseudomonas fluorescens 0.5um, 1.5um [4]
Table 1: Table of the parameters used to create the soil community model and the references.





REFERENCES & Attributions

1. Naylor J, Fellermann H, Ding Y, Mohammed W, Jakubovics N, Mukherjee J, Biggs C, Wright P, Krasnogor N (2016) Simbiotics: A Multiscale Integrative Platform for 3D Modeling of Bacterial Populations. ACS Synthetic Biology 2016 DOI: 10.1021/acssynbio.6b00315 (link)

2. Baldani JI, Baldani VLD, Seldin L, Doebereiner J (1986) Characterization of Herbaspirillum seropedicae gen. nov., sp. nov., a Root-Associated Nitrogen-Fixing Bacterium International Journal of Systematic and Evolutionary Microbiology 36: 86-93, doi: 10.1099/00207713-36-1-86

3. Tarrand JJ, Kried NR, Doebereiner J (1978) A taxonomic study of the Spirillum lipoferum group, with descriptions of a new genus, Azospirillum gen. nov. and two species, Azospirillum lipoferum (Beijerinck) comb. nov. and Azospirillum brasilense sp. nov. Canadian Journal of Microbiology 24: 967-980

4. Rhodes ME (1959) The Characterization of Pseudomonas fluorescens Journal of general Microbiology 21: 221-263

Attributions: Patrycja Ubysz, Connor Trotter