Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs
One of use cases for metabolic network optimisation of biotechnologically applied microorganisms is the in silico design of new strains with an improved distribution of metabolic fluxes. Global stochastic optimisation methods (genetic algorithms, evolutionary programing, particle swarm and others) c...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| In: |
Mathematical biosciences
Year: 2018, Jahrgang: 307, Pages: 25-32 |
| ISSN: | 1879-3134 |
| DOI: | 10.1016/j.mbs.2018.11.002 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1016/j.mbs.2018.11.002 Verlag: http://www.sciencedirect.com/science/article/pii/S0025556416303376 
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| Verfasserangaben: | Egils Stalidzans, Katrina Landmane, Jurijs Sulins, Sven Sahle |
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| 520 | |a One of use cases for metabolic network optimisation of biotechnologically applied microorganisms is the in silico design of new strains with an improved distribution of metabolic fluxes. Global stochastic optimisation methods (genetic algorithms, evolutionary programing, particle swarm and others) can optimise complicated nonlinear kinetic models and are friendly for unexperienced user: they can return optimisation results with default method settings (population size, number of generations and others) and without adaptation of the model. Drawbacks of these methods (stochastic behaviour, undefined duration of optimisation, possible stagnation and no guaranty of reaching optima) cause optimisation result misinterpretation risks considering the very diverse educational background of the systems biology and synthetic biology research community. Different methods implemented in the COPASI software package are tested in this study to determine their ability to find feasible solutions and assess the convergence speed to the best value of the objective function. Special attention is paid to the potential misinterpretation of results. Optimisation methods are tested with additional constraints that can be introduced to ensure the biological feasibility of the resulting optimised design: (1) total enzyme activity constraint (called also amino acid pool constraint) to limit the sum of enzyme concentrations and (2) homeostatic constraint limiting steady state metabolite concentration corridor around the steady state concentrations of metabolites in the original model. Impact of additional constraints on the performance of optimisation methods and misinterpretation risks is analysed. | ||
| 534 | |c 2018 | ||
| 650 | 4 | |a Homeostatic constraint | |
| 650 | 4 | |a Metabolic engineering | |
| 650 | 4 | |a Optimisation | |
| 650 | 4 | |a Parallel optimisation runs | |
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