Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Model Checking Approach to the Parameter Estimation of Biochemical Pathways
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Bioinformatics
Analysis and Optimization of C3 Photosynthetic Carbon Metabolism
BIBE '10 Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering
Design of robust metabolic pathways
Proceedings of the 48th Design Automation Conference
Bootstrapping parameter estimation in dynamic systems
DS'11 Proceedings of the 14th international conference on Discovery science
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout, which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organisms to maximise the production of key intermediate metabolites such as succinate and acetate. We obtain a vast set of Pareto optimal solutions; each of them represents an organism strain. For each solution, we evaluate the fragility by calculating three robustness indexes and by exploring reactions and metabolite interactions. Finally, we perform the Sensitivity Analysis of the metabolic model, which finds the inputs with the highest influence on the outputs of the model. We show that our methodology provides effective vision of the achievable synthetic strain landscape and a powerful design pipeline.