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Open Access Methodology article

Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

Christian Jungreuthmayer12 and Jürgen Zanghellini12*

Author affiliations

1 Austrian Centre of Industrial Biotechnology, Vienna, Austria

2 Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria

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Citation and License

BMC Systems Biology 2012, 6:103  doi:10.1186/1752-0509-6-103

Published: 16 August 2012

Abstract

Background

Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.

Results

Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.

Conclusions

We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

Keywords:
Metabolic engineering; Elementary modes; Minimal cut sets; Integer programming; Strain optimization; Ethanol production; Minimal functionality; Gene regulation