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

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<p><font size="3">One proposed application of the root-colonising <i>Pseudomonas</i> sp. endophyte chassis is to produce a flavonoid, naringenin, in and around plant roots. The substance, as demonstrated in our experimental work (link), attracts free-living nitrogen-fixing bacteria. Under the right conditions, this would benefit the plant by increasing nitrogen availability, and possibly reduce the need for synthetic nitrogen fertiliser use. </font></p>
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<p><font size="3">In the lab, we demonstrated that <i>Pseudomonas</i> sp. was a genetically tractable chassis organism, and that it could be used to colonise Arabidopsis roots. Based on this evidence, we propose that plant roots, colonised with <i>Pseudomonas</i> sp. expressing an operon with genes for naringenin biosynthesis, would create a naringenin concentration gradient in the surrounding soil environment. To provide an early insight into the effect that naringenin production would have on the surrounding microbial community, and to provide visualisations for the public, we developed the microbial community modelling to imitate what is happening in the soil around the colonised root.</font></p>
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In the lab, we demonstrated that Pseudomonas sp. was a genetically tractable chassis organism (link), and that it can colonise Arabidopsis roots (link). One proposed application of for our root-colonising Pseudomonas endophyte chassis is to produce the chemoattractant naringenin. The substance, as demonstrated in our experimental work (link), attracts free-living nitrogen fixing bacteria. Under the right conditions, this could benefit the plant by increasing nitrogen availability, and reduce use of synthetic nitrogen fertilisers.  
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We therefore propose that plant roots, colonised with our Pseudomonas sp. and carrying an operon containing the four genes encoding naringenin biosynthesis enzymes, would create a naringenin concentration gradient in the surrounding soil environment. To provide insight into the effect that naringenin production would have on the surrounding microbial community, and to provide visualisations for ourselves and those we have engaged with, we developed a microbial community model to simulate what is happening in the soil around a Pseudomonas colonised root.
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                   <p><font size="3">The method of choice is an agent-based model that allows us to see the behaviour of the whole nitrogen-fixing bacterial community under the influence of a chemoattractant – naringenin. The software used is SimBiotics [1], the agent-based modelling tool developed at Newcastle University. SimBiotics provides a way to visualise stochastic simulations via real-time animations. Supported by data from our chemotaxis experiments and growth curves (link), the model was able to accurately predict the microbes' behaviour.</font></p>
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<p><font size="3">The model assumes infinite resources, hence no competition between the species as that scenario would be the most probable in the soil. The model consists of three bacterial species (<i>Pseudomonas</i> sp., <i>Herbaspirillum seropedicae</i>, and <i>Azospirillum brasilense</i>). To focus on the formation of the biofilm itself, we have set the <i>Pseudomonas</i> sp. layer to be steady. There are 30 <i>Pseudomonas</i> sp. cells distributed and attached to the top side of the modelled area representing the rhizosphere and 100 cells of initial populations of each nitrogen-fixing species performing chemotaxis. </font></p>
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The method of choice is an agent-based model that allows us to see the behaviour of the whole nitrogen fixing bacterial community under the influence of a chemoattractant – naringenin. The software used is SimBiotics [1], the agent-based modelling tool developed at Newcastle University. SimBiotics provides a way to visualise stochastic simulations via real-time animations. Supported by data from our chemotaxis experiments and growth curves (link), the model was able to predict the microbial behaviour.  
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<p><font size="3">The growth of the nitrogen-fixing bacteria is described by the first order kinetics (Reaction 1). To obtain understanding of bacterial growth, we monitored the change in absorbance (600 nm) of our nitrogen-fixing bacteria grown at 30 ˚C for 72 hours. These data were 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 rates in a way that could be accurately incorporated into the model. As soon as the bacteria reaches the length equal twice of the initial length (Table 1) it divides into two identical cells. </font></p>
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</font></p>
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<p><font size="3">The model assumes infinite resources, i.e. no competition between the species. The bacterial species present are Azospirillum brasilense, Herbaspirillum seropedicae and engineered Pseudomonas sp. There are 30 Pseudomonas cells placed on the top side of the modelled area representing the rhizosphere and 100 cells of initial populations of each nitrogen fixing species capable of demonstrating a chemotactic response distributed randomly in the simulated space (Figure 1).</font></p>
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<p><font size="3">
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The growth of the nitrogen fixing bacteria is described by the first order kinetics (Reaction 1). To obtain understanding of bacterial growth, we monitored the change in absorbance (600 nm) of our nitrogen fixing bacteria grown at 30 ˚C for 72 hours. These data were then converted into cell density after experiments to identify cell count at specific optical densities. By doing this, we obtained a conversion ratio. This allowed us to understand growth rates in a way that could be accurately incorporated into the model. As soon as the bacteria reaches the size approaching double its starting size (Table 1) it divides into two cells of the same length.  
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</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">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 non-overlapping 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).</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 non-overlapping 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).</font></p>
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<p><font size="3">Above certain concentrations, naringenin kills bacteria. The threshold we set for the bacterial species (excluding <i>Pseudomonas</i> sp.) is based on the experiments we conducted in the biological laboratory where a concentration of 150 μM was found to be toxic.</font></p>
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<p><font size="3">Our lab work identified that above certain concentrations, naringenin kills bacteria. The threshold we set for the bacterial species (excluding <I>Pseudomonas sp.</I>) is based on the experiments we conducted in the biological laboratory where a concentration of 150 μM was found to be toxic. </font></p>
  
 
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<button class="collapsible"><font size="5">Reactions</font></button>

Revision as of 12:13, 15 October 2018

Model

Alternative Roots

Microbial Community

Introduction Model Design

Introduction

In the lab, we demonstrated that Pseudomonas sp. was a genetically tractable chassis organism (link), and that it can colonise Arabidopsis roots (link). One proposed application of for our root-colonising Pseudomonas endophyte chassis is to produce the chemoattractant naringenin. The substance, as demonstrated in our experimental work (link), attracts free-living nitrogen fixing bacteria. Under the right conditions, this could benefit the plant by increasing nitrogen availability, and reduce use of synthetic nitrogen fertilisers.

We therefore propose that plant roots, colonised with our Pseudomonas sp. and carrying an operon containing the four genes encoding naringenin biosynthesis enzymes, would create a naringenin concentration gradient in the surrounding soil environment. To provide insight into the effect that naringenin production would have on the surrounding microbial community, and to provide visualisations for ourselves and those we have engaged with, we developed a microbial community model to simulate what is happening in the soil around a Pseudomonas colonised root.

Model Design

The method of choice is an agent-based model that allows us to see the behaviour of the whole nitrogen fixing bacterial community under the influence of a chemoattractant – naringenin. The software used is SimBiotics [1], the agent-based modelling tool developed at Newcastle University. SimBiotics provides a way to visualise stochastic simulations via real-time animations. Supported by data from our chemotaxis experiments and growth curves (link), the model was able to predict the microbial behaviour.

The model assumes infinite resources, i.e. no competition between the species. The bacterial species present are Azospirillum brasilense, Herbaspirillum seropedicae and engineered Pseudomonas sp. There are 30 Pseudomonas cells placed on the top side of the modelled area representing the rhizosphere and 100 cells of initial populations of each nitrogen fixing species capable of demonstrating a chemotactic response distributed randomly in the simulated space (Figure 1).

The growth of the nitrogen fixing bacteria is described by the first order kinetics (Reaction 1). To obtain understanding of bacterial growth, we monitored the change in absorbance (600 nm) of our nitrogen fixing bacteria grown at 30 ˚C for 72 hours. These data were then converted into cell density after experiments to identify cell count at specific optical densities. By doing this, we obtained a conversion ratio. This allowed us to understand growth rates in a way that could be accurately incorporated into the model. As soon as the bacteria reaches the size approaching double its starting size (Table 1) it divides into two cells of the same length.

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 non-overlapping 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).

Our lab work identified that above certain concentrations, naringenin kills bacteria. The threshold we set for the bacterial species (excluding Pseudomonas sp.) is based on the experiments we conducted in the biological laboratory where a concentration of 150 μM was found to be toxic.

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 4*10-4 fg per second growth curves (link)
Growth Rate Azospirillum brasilense 1.314*10-4 fg per second growth curves (link)
Naringenin concentration threshold 150 μM Experiment (link)
Diameter, initial length of Herbaspirillum seropedicae 0.7μm, 1.5μm [2]
Diameter, initial length of Azospirillum brasilense 0.5μm, 2.9 μm [3]
Diameter, initial length of Pseudomonas sp. 0.5μm, 1.5μm [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