Metabolic Modeling

In order to assess the real world viability of the BeeT we evaluated the proposed system of application by making a model of the entire system. To do this we used Flux Balance Analysis (FBA) to make model the base chassis. The chassis [tooltip: Chassis is the base organism that is modified] of BeeT is a variant of  Escherichia coli , for which it is known that it does not grow in sugar water, mainly due to high osmotic pressure ¹ . The question remained: Does it survive there, and if so, for how long?

Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks.

In-depth explanation

The main components of FBA are reactions and metabolites, which are derived from knowledge about genes and the enzymes they produce to facilitate the reactions between metabolites. Reactions are represented as rows in a matrix of numbers showing a positive number for production and a negative number for consumption, the numbers representing stoichiometrically balanced chemical equations.

Fluxes are represented as a vector of numbers representing the speed and direction of each reaction. The optimize function will find a parsimonious solution, given a certain objective or multiple objective reaction fluxes to maximize, or minimize if that is the case.

When using FBA the objective is usually the "Biomass reaction", which is a kind of resource sink that consumes all kinds of metabolites to represent growth and cell division, which makes sense given that most cells in the exponential phase will allocate as many resources as possible to cell division. An important set of reactions is the exchange reactions which represent whether the growth medium itself is either losing or gaining certain metabolites at a certain rate. This represents compounds accumulating in or getting taken up from the medium.

There is also an ATP Maintenance reaction, another resource sink, which represents all ATP required to maintain gene-regulation and regular functioning of a non-growing cell. Given the published version of the model, 3.15 mmol*gDW^-1*h^-1 was the parsimonious response when maximizing for biomass production whilst minimizing all other functions.

Every flux has certain bounds based on the medium the cell is in and what is known to be biologically possible for a specific organism to take up. These bounds have to be either experimentally determined, but in other cases are put at -1000 for the lower, and +1000 for the upper. The entire range of objective solutions can also be looked for, for every flux, this is known as Flux Variance Analysis, but in general a solution is preferred such that all other fluxes are as low as possible.

"Parsimonious Flux Balance Analysis” was born from the argument that less flux through a certain reaction means less effort the cell has to expend on the production of a certain protein and thereby will be more evolutionarily beneficial in the long term. It is to me the most sensible way to pick a specific solution from the allowed solution space created from a certain Metabolic Linear Programming Problem.

On the BIGG database we found a pre-existing well characterized metabolic model, named iJO1366 from the paper: “A comprehensive genome-scale reconstruction of Escherichia coli metabolism”. Orth JD1, Conrad TM, Na J, Lerman JA, Nam H, Feist AM and Palsson BØ. In order to work with the model in smbl format the python package CobraPy was downloaded to facilitate the manipulation and perturbation of the “in silico organism“. [ COBRApy: COnstraints-Based Reconstruction and Analysis for Python ]

The previously mentioned exchange reactions were modified by reading in the reaction ids and their upper and lower bounds. [Link to small page about file.]

Then we changed the objective from Biomass to ATP maintenance in order to represent the ‘starvation mode’ which we assume the cell to be in. And finally we loop through a range of water efflux rates from the cell and produce a value for the objective, given minimal other fluxes.

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Saving the Bees

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. iGEM team Wageningen 2016 has designed and created BeeT, a genetic system in Escherichia coli that detects the presence of Varroa. destructor in the bee hive and produces a combination of chitinases and Cry/Cyt/Mtx toxins to remove them! If you want to read more about the system and our work during the last six months, the iGEM team Wageningen 2016 invites you to continue surfing around our Wiki.

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Tiny Bee

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Tiny Bee

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MetabolicModeling

Metabolic Modeling

Saving the Bees

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References