Flux Balance Analysis
- We define Flux Balance Analysis and its importance and value added to our project.
- We performed a first analysis to determine the importance of tyrosine in the media.
- We analysed the distribution of growth at a fixed substrate production flux, which can be used to define optimal growth.
- We predicted the involvement of glycolysis in growth of our engineered strain and related it to the addition of tyrosine (a product precursor) in the medium.
What is Flux Balance Analysis?
The main aim of our project is to overproduce Hipposudoric Acid (HA) in recombinant Escherichia coli. For that we need to achieve optimal production of the HA’s precursor homogentisic acid (HGA). To predict HGA production and explore the metabolic constraints affecting flux towards our product we used Flux Balance Analysis (FBA).
FBA is a method for computing steady-state metabolic flux distributions via genome-scale metabolic models, which are built using known intracellular metabolic reactions and gene-protein-reaction associations (1).
The recently published E. coli genome-scale reconstruction iJO1366 was used in all simulations. (2, 3, 4) FBA was performed using the COBRA Toolbox v.3.0. in MATLAB.
First step: introducing the HGA synthesis pathway
As wild-type E. coli does not produce HGA natively, the first step with FBA is adding the HGA synthesis pathway (simplified to a one-step enzymatic reaction) to the wild-type model:
![FBA Reaction](https://static.igem.org/mediawiki/2020/7/7a/T--Manchester--FBA_reac.png)
Where 34hpp 3-(4-hydroxyphenyl)pyruvate, a metabolite natively present in E. coli, and HGA is homogentisic acid.
Media Comparison
We first examined and compared the effect of different nutrients (i.e. glucose minimal media only and glucose minimal media with tyrosine supplemented) on the growth of wild type E. coli and HGA-producing E. coli. HGA’s precursor is a tyrosine derivative, naturally we would expect an increased growth of the HGA-producing E. coli if tyrosine was added to the growth medium. To test this assumption we included a tyrosine uptake flux to the model. The tyrosine uptake rate was set to various levels, i.e. zero (glucose only), 20% and 100% of the glucose uptake rate, to investigate the impact on the growth of HGA-production strain. (Figure 1). FBA predicts that the tyrosine supplementation improves the growth of HGA-producing E. coli more significantly than that of the wild-type strain, which validates our assumption.
![Figure 1a](https://static.igem.org/mediawiki/2020/6/65/T--Manchester--FBA_1a.png)
![Figure 1b](https://static.igem.org/mediawiki/2020/d/d7/T--Manchester--FBA_1b.png)
![Figure 1c](https://static.igem.org/mediawiki/2020/a/a7/T--Manchester--FBA_1c.png)
Figure 1. Growth with fluxes at variable levels of Tyrosine in the media. Minimal media (MM), Tyrosine (tyr), Glucose (glc), Glucose uptake rate (GUR) (a) FBA prediction of maximum growth rate of wild-type and hga-producing E. coli grown on glucose minimal media with no tyrosine supplemented. (b) FBA prediction of maximum growth rate of wild-type and HGA-producing E. coli grown on glucose minimal media with medium-level tyrosine supplemented, the tyrosine uptake rate was set to 20% of the glucose uptake rate. (c) Prediction of maximum growth rate of wild-type and HGA-producing E. coli grown on glucose minimal media with high-level tyrosine supplemented, the tyrosine uptake rate was set to 100% of glucose uptake rate.
E. coli growth at fixed HGA production rate
The second test was designed to investigate the change in maximum growth rate at different glucose and oxygen uptake rate given a fixed HGA synthesis rate.This analysis predicts the optimal growth for every uptake value of glucose and oxygen, from 0 mmol/gdw/h to 20 mmol/gdw/h), at a fixed HGA production rate of 1 mmol/gdw/h (Figure 2).
![Figure 2](https://static.igem.org/mediawiki/2020/9/92/T--Manchester--FBA_4.png)
Figure 2. FBA prediction of the maximum growth rate at various glucose uptake rate (GUR) and oxygen uptake rate (OUR). HGA production rate was set to 1 mmol/gdw/h for all simulations.
Figure 2 can be useful for designing optimal growth conditions. For example, given a fixed GUR of 10 mmol/gdw/h, the optimal OUR that gives the highest growth rate might be around 16 mmol/gdw/h, or alternatively, given a fixed OUR we can infer that the optimal GUR might be 20 mmol/gdw/h or higher (Figure 3).
![FBA 5](https://static.igem.org/mediawiki/2020/e/e3/T--Manchester--FBA_5.png)
![FBA 6](https://static.igem.org/mediawiki/2020/a/a7/T--Manchester--FBA_6.png)
Figure 3. Projections of the growth distribution from Figure 2. In orange, growth rate against GUR for Constant OUR at 15 mmol/gdw/h. In blue, growth rate against growth rate against OUR for Constant GUR at 10 mmol/gdw/h.
Effect of tyrosine on Glycolysis and growth
The final test performed is the analysis of the metabolic flux carried by enolase (ENO), a key enzyme of glycolysis that catalyses the conversion of 2-phosphoglycerate (2pg) to phosphoenolpyruvate (pep) (5). We choose enolase not only because of its central role in the lower part of glycolysis, but also because the product of ENO, i.e. pep, is an important substrate of the Shikimate pathway, from which HGA derives.
![Figure 4a](https://static.igem.org/mediawiki/2020/e/e7/T--Manchester--FBA_4a.png)
![Figure 4b](https://static.igem.org/mediawiki/2020/a/ac/T--Manchester--FBA_4b.png)