The ecFactory method is a series of sequential steps for identification of metabolic engineering gene targets. These targets show which genes should be subject to overexpression, modulated expression (knock-down) or deletion (knock-out), with the objective of increasing production of a given metabolite. This method was developed by combining the principles of the FSEOF algorithm (flux scanning with enforced objective function) together with the features of GECKO enzyme-constrained metabolic models (ecModels), which incorporate enzymes as part of genome-scale metabolic networks.
- A functional Matlab installation (MATLAB 7.3 or higher).
- The RAVEN toolbox for MATLAB.
- Gurobi Optimizer for any simulations.
- Git
Clone this repository into an accesible directory in your computer. No further steps are needed.
A case study for prediction of metabolic engineering targets for increased production of 2-phenylethanol in S. cerevisiae cells using ecYeastGEM and the ecFactory method is explained in detail in a MATLAB live script. To run this example, open the live script in MATLAB and run it! with this, you will see the outputs of the method scripts in real time.
- An additional case study for prediction of gene targets for enhanced heme production in S. cerevisiae has been added. Validation of a subset of the predicted gene targets can be seen in this publication.
All the relevant outputs of the method are stored in the tutorials/results folder in this repository.
Last update: 2022-09-17
This repository is administered by Iván Domenzain, Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology.