Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design

Abstract

BackgroundMicrobial hosts offer a number of unique advantages when used as production systems for both native and heterologous small-molecules. These advantages include high selectivity and benign environmental impact; however, a principal drawback is low yield and/or productivity, which limits economic viability. Therefore a major challenge in developing a microbial production system is to maximize formation of a specific product while sustaining cell growth. Tools to rationally reconfigure microbial metabolism for these potentially conflicting objectives remain limited. Exhaustively exploring combinations of genetic modifications is both experimentally and computationally inefficient, and can become intractable when multiple gene deletions or insertions need to be considered. Alternatively, the search for desirable gene modifications may be solved heuristically as an evolutionary optimization problem. In this study, we combine a genetic algorithm and elementary mode analysis to develop an optimization framework for evolving metabolic networks with energetically favorable pathways for production of both biomass and a compound of interest.ResultsUtilization of thermodynamically-weighted elementary modes for flux reconstruction of E. coli central metabolism revealed two clusters of EMs with respect to their ΔGp°. For proof of principle testing, the algorithm was applied to ethanol and lycopene production in E. coli. The algorithm was used to optimize product formation, biomass formation, and product and biomass formation simultaneously. Predicted knockouts often matched those that have previously been implemented experimentally for improved product formation. The performance of a multi-objective genetic algorithm showed that it is better to couple the two objectives in a single objective genetic algorithm.ConclusionA computationally tractable framework is presented for the redesign of metabolic networks for maximal product formation combining elementary mode analysis (a form of convex analysis), pathway thermodynamics, and a genetic algorithm to optimize the production of two industrially-relevant products, ethanol and lycopene, from E. coli. The designed algorithm can be applied to any small-scale model of cellular metabolism theoretically utilizing any substrate and applied towards the production of any product.

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

“…By using weights in the objective function it is possible to account for experimental difficulties in the implementation of the strain. This allows to prioritize biologically feasible MCS over infeasible ones and – in contrast to other, optimized based methods [9] – does not effect the ability to calculate the complete set. Taking biological feasibility into account seems advantageous as in our example we have demonstrated that due to the lacking gene-enzyme-reaction mapping roughly 70% of the predicted solutions would require the deletion of at least one non-enzymatic reaction.…”

Thermodynamics-based design of microbial cell factories for anaerobic product formation

“…In this case thermodynamic constraints have played an important role narrowing the number of feasible pathways [55][56][57][58]. Although this approach is extremely useful, the main limitations are the relatively little thermodynamic information that is available for most pathway intermediates and the number of known biochemical pathways [57,59].…”

Quantification of Microbial Phenotypes

“…EFMs represent the network metabolic capabilities, however, the enumeration of EFMs is a cumbersome problem, thus, the reduction of EFMs to biologically relevant ones is an extremely useful approach. The first attempt of thermodynamic analysis of EFMs was realized to an E. coli metabolic network [48]. Later, a full NET analysis of EFMs was performed for a yeast model [49].…”