Team:Newcastle/Results/Chemotaxis/AgarBehaviour
Rationale/h1>
In order to understand chemotaxis on agar, it was important to understand how the selected bacteria (Azospirillum brasilense, Azorhizobium caulinodans and Herbaspirillum seropedicae) grow on solid media in standard laboratory conditions. This measure enabled the comparison of colony morphology from experimental plates against a primary control. Plates were compared to these controls if there was concern that a colony had grown in an unpredictable manner. The results also allowed a quality-control for if contamination was to arise as we had familarised ourselves with the bacteria
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.
Dc - diffusion coefficient, Sij - cross-section, dij distance between the centres of the two subdomains, uj and ui concentrations in the subdomains A → ⌀ kA
| Parameter | Value | Source |
|---|---|---|
| Growth Rate Herbaspirillum seropedicae | 4*10-4 cell per second | growth curves (link) |
| Growth Rate Azospirillum brasilense | 1.314*10-4 cell per second | 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] |
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