Team:UGA-Georgia/Modeling

Our Model

Idea:

To increase geraniol yield by mapping the entire lipid biosynthesis pathway in M. maripaludis, performing a genome-scale flux balance analysis and developing strategies for the over production of isoprenoid compounds.

Results:

We designed a metabolic model (fig. 1) for isoprenoid biosynthesis based upon the complete pathway, including stoichiometric values of acetyl-CoA, ATP, and other compounds required for cell growth. The Biocyc, KEGG, IMG, and SEED databases were used to identify candidate genes/enzymes responsible for each reaction.

In Progress:

To run computational analysis on our models we use Systems Biology Markup Language (SBML) formatted files. SBML files contain all information regarding reactions/metabolites. Below (table 1) are a few examples of reaction information from the M. maripaludis SBML file, the entire model contains 619 reactions and 604 metabolites. SBML files are used for In-silico metabolic engineering software, the particular ones we are using are OptFlux™ and MATLAB® in the COBRA toolbox. The goal of using these programs is to complete a genome-scale flux balance analysis and develop strategies for the overproduction of isoprenoid compounds. These software are capable of running simulations given any mutation, environmental condition, up/down-regulation, gene insertion/deletion, etc. After constructing the model, we may then insert new reactions of interest (e.g. geraniol synthase) and optimize specific biomass towards overproduction of isoprenoid compounds. These software will effectively calculate the most efficient ‘strain’ for our biomass query including knock-outs of unnecessary interfering genes, optimal growth conditions, and up/down regulation of particular genes. Figure 2 is an excerpt from OptFlux™ software exemplifying how a user may calculate critical genes/reactions for their biomass flux of interest. Figure 3 is an excerpt from MATLAB® software demonstrating how a user may run optimization simulations by defining various parameters.

Outlook:

Build a regulatory model for isoprenoid biosynthesis in M. maripaludis, allowing further model optimization. Using primary literature, we will identify enzyme kinetics and effectors, missing genes/enzymes and regulation upon each individual reaction.

Discussion:

Optimization of biomass production in M. maripaludis will lead us to results that demand the effective up-regulation and down-regulation of certain genes in the pathway that act upon the flux of biomass. For this to become a practical solution, one must establish a library of regulators that can provide variable levels of expression. This led us to our next project – Establishing and characterizing the first Ribosome Binding Site (RBS) library in methanogens.

Table 1

References

[1] OptFlux™ “OptFlux 3 Beginner’s Tutorial.” Web. 29 Apr. 2014. http://darwin.di.uminho.pt/optflux/tutorial/TutorialOptFlux3.pdf

[2] "Optimization App." MathWorks®, n.d. Web. 17 Oct. 2014. http://www.mathworks.com/help/optim/ug/graphical-optimization-tool.html

Fig. 1 This metabolic model of the isoprenoid biosynthesis pathway was compiled that includes stoichiometric values of acetyl-CoA, ATP and other compounds required for cell growth. The Biocyc, KEGG, IMG, and SEED databases were used to identify candidate genes/enzymes responsible for each reaction.

[1] Fig. 2 An excerpt from OptFlux™ software exemplifying how a user may calculate critical genes/reactions for their biomass flux of interest.

[2] Fig. 3 An excerpt from MATLAB® software demonstrating how a user may run optimization simulations by defining various parameters.