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Revision as of 17:16, 8 September 2015
Modeling
Content
Metabolic networks
As said before, the aim of our project is to create a biological system able to produce two molecules: butyric acid and formic acid.
To achieve this, we need to modify the existing balance between the metabolic pathways present in E. Coli.
Indeed, we want to optimize butyrate and formate productions in our bacterium
by adjusting environmental conditions in order to obtain the desired concentrations of the associated acids.
The following metabolic network represents all of the known metabolites and metabolic pathways for Escherichia coli K12 MG1655 (best known model) as of today.
It was obtained from the KEGG database.
Our first step was to identify the pathways in which our molecules take part, in order to have a clear understanding of their role and effect.
Figure 1:Kegg Metabolic pathways - Escherichia coli K-12 MG1655
Formate network
Formate is naturally produced by E.coli but at a level that is quite low. Our project requires that Apicoli produces more. Hence we had to optimize its biosynthesis by studying the genes coding for the enzymes involved in the pathway. We decided to focus our efforts on the Pyruvate Formate Lyase (PFL), the enzyme that causes degradation of pyruvate, thus yielding formate.
Figure 2: Reaction catalyzed by PFL
The subnetwork presented below was obtained from MetExplore platform[2] and presents all reactions from the KEGG and ByoCyc databases involved in the production or consumption of formate. This map will help us predict the likely consequences of a PFL-induced formate overproduction in Apicoli. In fact, formate is harmful to our bacterium and is normally metabolized to other products. We thus have to find the balance between producing enough formate to kill the varroa without killing Apicoli.
Figure 3: Metabolic network of all reaction involving formate happening in E.coli
Butyrate network
Contrary to formate, butyrate is not naturally produced by E. Coli. E.coli possesses an enzyme called (Butyryl-coA transferase (or Acetate-coA transferase) that yields butyrate, but this reaction cannot happen spontaneously in the organism due to the lack of Butanoyl-coA, its substrate. Indeed, study of the biosynthesis pathway shows that the enzymes responsible for Butanoyl-coA production (EC.2.1.3.19 phosphate butyryltransferase , EC.1.3.1.44 trans-2-enoyl-CoA reductase (NAD+) , etc.) cannot be found in our strain. Hence, in order to obtain butyrate, we chose to introduce a complete production pathway relying on genes coming from different organisms in Apicoli (see Attract).
Flux Balance Analysis (FBA)
Presentation
To go further in the development of our project, we decided to use a method called Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA).
It is based on the model EC_iJO1366 [1]. This is the most recent model concerning E.coli K12 MG1655.
It is a stoichiometric model defining all metabolic ways known to this day in this particular strain. It has been adapted to better suit our own bacterial system. Here you will find the associated XML file.
The modifications we made are such that the described strain is now capable of producing butyrate.
Our modeling aims at determining the maximum butyrate and formate quantities our system would be able to produce, depending on two initial conditions: oxygen and glucose flux. We set them as follows:
- Maximal oxygen entry flux: 5 mmol.gDW-1.h-1
- Maximal glucose entry flux: 0.3998 mmol.gDW-1.h-1
It is interesting to note that FBA provides the produced and consumed metabolites in a quantitative way.
The setpoint for oxygen flux is rather low in order to simulate microaerobic conditions. The glucose flux setpoint was chosen according to the results of our tests with the Biosilta kit (see “Preliminary part”).
This kit was used to ensure a stable availability of glucose over time for our bacteria. Indeed the medium contains enzymes capable of catabolizing starch, thus gradually releasing glucose in the culture.
For known initial quantities of starch and enzymes, we are able to deduce the glucose release flux. Thus, we chose the appropriate enzyme concentration and polymer quantity in order to have a glucose rate of 0.3998 mmol.gDW-1.h-1.
Annexes
References
- [1] KEGG Metabolic pathways - Escherichia coli K-12 MG1655
- [2] Le Conte Y, Arnold G, Trouiller J, Masson C, Chappe B, Ourisson G. 1989. Attraction of the parasitic mite varroa to the drone larvae of honey bees by simple aliphatic esters. Science 245:638–639.
- [3] Methods for attracting honey bee parasitic mites. [accessed 2015 Jul 24].
- [4] Louis P, Flint HJ. 2009. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol. Lett. 294:1–8. [5] Atsumi S, Cann AF, Connor MR, Shen CR, Smith KM, Brynildsen MP, Chou KJY, Hanai T, Liao JC. 2008. Metabolic engineering of Escherichia coli for 1-butanol production. Metabolic Engineering 10:305–311.
- [6] Wallace KK, Bao Z-Y, Dai H, Digate R, Schuler G, Speedie MK, Reynolds KA. 1995. Purification of Crotonyl-CoA Reductase from Streptomyces collinus and Cloning, Sequencing and Expression of the Corresponding Gene in Escherichia coli. European Journal of Biochemistry 233:954–962.