Model
Aim
Our model aims to reconstruct the metabolic pathways of Azotobacter vinelandii, and use flux balance analysis (FBA) to determine possible thresholds for alginate production and sucrose intake. To that end, an existing FBA model of Escherichia coli was adapted and modified for our needs.
Experiments
Different measurements of our bacteria to quantify our model were made. We need to know which carbon sources are best for our bacteria. How much it takes (sucrose) and how much it gives (produced alginate) related to the available resources.
To generate the data for our model we inoculated three 150 mL cultures of A. vinelandii and took samples to measure the dry cell mass, the optical density, the sucrose content in the medium and the alginate production over a time period of 10 days.
To find a correlation between dry cell mass and OD at 620 nm (see fig. 1), The OD was measured at 620 nm, as this was the preferred wavelength for bacterial measurement in our “host lab”, the dry cell mass was determined by weighing the cell pellets with a frame precision wage.
Fig. 1 Correlation of OD620 and dry cell mass [mg] over a testing time of 10 days
For the dry cell mass the standard deviation is 0. On day 6 no dry cell mass could be measured, because the samples were useless.
The sucrose intake was measured using the sucrose assay kit from abcam (ab83387). Utilizing the invertase enzyme, sucrose is split into glucose, which reacts with the OxiRed agent provided by the kit. The reaction results in an orange dye that fluoresces at a wavelength of 587 nm.
Fig. 2 correlation of OD570 and anthrone-glucose complexes in different concentration from abcam83387 protocol booklet page 10
The fluorescence is proportional to the present, therefore the sucrose is split by the enzyme invertase to glucose which then reacts with OxiRed (Highly sensitive, stable peroxidase fluorogenic substrate) that can be measured at OD570. The kit can detect sucrose amounts from the range of 0.0002-10 mM. To make sure to stay in this range the sucrose concentration from the Burks medium (20 g/l) 1 in which A. vinelandii grew was used as a benchmark for the sample dilution
The testing kit (abcam83387) was kindly offered to us by our host lab, but our first measurement did not lead to usable values, because we tested too many samples at once and exceeded critical incubation times. We tried to order a new kit to repeat the measuring with optimized testing conditions, but the delivery time of the kit exceeded the iGEM wiki freeze, so that no more measurements could be done.
Alginate is measured using a protocol proposed by A. Beck 2, which measures the total carbohydrate amount of cell-pellets.The principle of the method is based on a colorimetric OD measurement of sugar-complexes, which form by mixing the cell-pellet with a buffer containing 75% sulfuric acid and anthrone. Sulfuric acid hydrolyzes polysaccharides into glucose, which then binds anthrone forming blue-green complexe. That complex can be measured at an OD of 625 nm. Before measuring the samples a standard curve with defined glucose concentration was created, which made it possible to conduct the glucose concentration in our samples (see fig. 3).
Fig.3 Glucose concentration after complexation with anthrone measured at OD625.
The standard curve was used to calculate the glucose-concentration in the samples and the glucose concentration can be equated with the total carbohydrate amount.
Between the sample-taking from the growth curve and performance of the experiments, we lost a lot of time because we struggled a bit with finding a good method to measure the amount of alginate, as it is not an ordinary test. With the help of Dr. St. Elmo Wilken (make a link to integrated human practices) we finally found the paper with the testing method we performed then. Because of this loss of time we got contaminations in our sucrose-, and alginate samples. We still measured the parameters to get an idea of the implementability of the testing methods, and also to put some data in our “model-code” to see if it works.
As a conclusion we can resume, that the testing methods are totally feasible for iGEM teams.
Model structure
Use E.coli model and adjust what A vinelandii do for alginate production and add it
It was planned to use the data from the performed experiments to evaluate how much sucrose intake leads to how much alginate production and dry cell mass. In further experiments, which we were not able to perform, we wanted to use other carbon sources to see if they can increase the amount of produced alginate. We also thought about growing A. vinelandii without any carbon sources, as it can fix atmospheric nitrogen, to see if this an option, as it would reduce the components of the medium. Furthermore we wanted to perform all of these experiments again, when we have introduced the alg8 gene and see how much the amount of alginate was increased.
Contribution to future iGEM teams
Future iGEM teams could benefit from the planning of our model, as they can easily find out which experiments have to be performed, and which methods they have to use therefore.
Creating the code
We also researched and developed a first pass of the model analysis. To analyse the model data, we used the programming language julia and the COBREXA.jl library. This library provides constraint-based reconstruction and analysis tools for exa-scale metabolic modeling.
Here is a simple example for future iGEM teams as a starting point:
using COBREXA # loads the package
using Tulip # loads the optimization solver
# open the SBML file and load the contents
model = load_model("/path/to/your_model.xml")
# now the model is loaded and you can perform any task on the data
# run a FBA
fluxes = flux_balance_analysis_dict(model, Tulip.Optimizer)
You can perform any task on the data with this example, in this case we performed a FBA analysis task on the data. Further examples and tutorials can be found in this documentation.
- P. Dos Sanatos et.al ,2011, Molecular Biology and Genetic Engineering in Nitrogen Fixation, DOI: 10.1007/978-1-61779-194-9_6
- A.E. Beck et.al, 2018, Measuring Cellular Biomass Composition for Computational Biology Applications, DOI: 10.3390/pr6050038