Flux Balance Analysis

Our synthetic pathway produces Hydrogen. Our chassis fixes Nitrogen, which is influenced by Hydrogen availability. Therefore, we were keenly interested in finding out how our synthetic pathway would interact with the host metabolism. After some exploratory consultation, we determined Flux Balance Analysis was an effective tool for this.

Introduction

Flux Balance Analysis (FBA) can serve to explore the fluxes of a given metabolic reconstruction. In this case, we wanted to determine the level and extent of interaction of our added pathway with the core metabolism. Since our chassis, R.etli, has two "flavors" (free living organism & plant symbiont), we were curious as to weather our transgenic system would remain functional under symbiont form. Moreover, since a key aspect of the project was Nitrogen fixation, we wanted to ensure said pathway was as functional as it could be.

Furthermore, key researchers at our University warned us against the project due to the widespread presence of endogenous hydrogenases (the word "impossible" was often used, which only got us rebelliously stuck on proving them wrong). These native enzymes play the opposite role of the activity we were interested in, they consume molecular hydrogen instead of producing it. Apparently, since Nitrogen fixation produces molecular hydrogen, some Rhizobial species developed capture hydrogenases to recycle the protons into the all-important proton gradient that feeds cellular machinery. However, our particular strain, CFN42, doesn't have any capture hydrogenase. Nonetheless, since nature developed pathways to do the exact opposite of what we're doing, we were interested in finding out any toxic or deleterious effects our system might have. Therefore, out goals can be stated as being:

1. Under symbiont form, our transgenic chassis is capable of Hydrogen production?
2. To what extent does the Hydrogen production affect core metabolism, and Nitrogen fixation?
3. Is there a toxic effect derived from Hydrogen production?

Theoretical Background

FBA is part of a larger set of models known as the Constraint Based Analysis (CBA). Since cells may adopt an astronomical level of possible solutions for a given set of conditions, CBM models create constrains on the solution space to limit the complexity of the solution and render it easier to compute, interpret, and/or understand.

FBA starts at the assumption that pathways strive towards homeostasis. Thus, it assumes the cellular chemoton will adjust whatever it has to do in order to maintain chemical stability. In other words, this model does not require pesky kinetic constants, something that made our lives easier. For some light reading on this, you can consult [http://dx.doi.org/10.2277/0521859034 "Systems Biology: Properties of Reconstructed Networks", by Bernhard Palsson]. Or you can always go ask the All-Knowing-Oracle [http://en.wikipedia.org/wiki/Flux_balance_analysis here].

The Simulation

A researcher at our institute had already done a [http://dx.doi.org/10.1371/journal.pcbi.0030192 metabolic reconstruction] for the core metabolism of our chassis under symbiont form. The proteins in this reconstruction were chosen based on microArray data, and the pathways were reconstructed using the then-available literature.

The Toolbox

We performed the FBA calculations using a particular toolbox in iGEM's favorite program: MATLAB. In particular, we used the [http://opencobra.sourceforge.net/openCOBRA/Welcome.html COnstrained Based Reconstruction & Analysis toolbox], originally by the Palsson Lab. I have to confess, the poor documentation (version 3.1.2) made our life difficult. It appears the toolbox has a lot of functions for various CBA, though a simple tutorial would have been quite welcome...

Parsing v1

At first we decided to parse the data we had into The Stochiometric Matrix, and we did it, with a combination of PERL, R, & MATLAB scripts. We had a lot of trouble with that parser particularly because we didn't know to what structure it had to conform (i.e. what are the fields of "rev", or "c", or "b"...). After finding [http://systemsbiology.ucsd.edu/Downloads/E_coli_Core/ this] sample file to use and the [http://dx.doi.org/10.1038/nbt.1614 Nature Method Tutorial]), we eventually got it working. Once the thing was partially functional, we got to play around with COBRA's functions, and we found the elusive createModel function.

Parsing v2

With the createModel function, we re-made the COBRA structure and got it fully functional with this. It was then we discovered the data we had been working with was painfully out-of-date. Searching in vain for a magical function that would consult databases and update reactions, we realized manual curation was necessary. For every one of the 385 reactions we had.

Parsing v3

We used the [http://www.brenda-enzymes.org/ E-C Numbers] classification to check the known reactions, we connected isolated dots with the [http://www.genome.jp/kegg/pathway.html Kyoto Encyclopedia of Genes and Genomes], and we finished the holes with [http://scholar.google.com/ other searches]... Once the data felt up-to-date, we set to re-analyze and tweak the metabolic reconstruction to our heart's content. We made several models to explore how the system reacted:

We do realize there's an option to knock-out a gene, and then perform the analysis, but this approached yielded easier-to-interpret results (in our humble opinion). We kept the Objective Function (OF) of the old data since it made sense. After running all the models, we realized that our OF was maximized for the Main Transgenic Model.

Parsing v4

The Results

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References

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