Strain design as multiobjective network interdiction problem: A preliminary approach

Torres, M., Jiang, Shouyong, Pelta, D. , Kaiser, M. and Krasnogor, N. (2018) Strain design as multiobjective network interdiction problem: A preliminary approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11160 . pp. 273-282. ISSN 0302-9743

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Computer-aided techniques have been widely applied to analyse the biological circuits of microorganisms and facilitate rational modification of metabolic networks for strain design in order to maximise the production of desired biochemicals for metabolic engineering. Most existing computational methods for strain design formulate the network redesign as a bilevel optimisation problem. While such methods have shown great promise for strain design, this paper employs the idea of network interdiction to fulfil the task. Strain design as a Multiobjective Network Interdiction Problem (MO-NIP) is proposed for which two objectives are optimised (biomass and bioengineering product) simultaneously in addition to the minimisation of the costs of genetic perturbations (design costs). An initial approach to solve the MO-NIP consists on a Nondominated Sorting Genetic Algorithm (NSGA-II). The shown examples demonstrate the usefulness of the proposed formulation for the MO-NIP and the feasibility of the NSGA-II as a problem solver. © Springer Nature Switzerland AG 2018.

Additional Information:cited By 0; Conference of 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018 ; Conference Date: 23 October 2018 Through 26 October 2018; Conference Code:219759
Keywords:Artificial intelligence, Genetic algorithms, Metabolic engineering, Metabolism, Bilevel, Computer aided technique, Metabolic network, Network interdiction, Network interdiction problems, Non dominated sorting genetic algorithm (NSGA II), Optimisation problems, Preliminary approach, Product design
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35662
Deposited On:07 May 2019 09:27

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