Strain Design as Multiobjective Network Interdiction Problem: A Preliminary Approach

  • Marina TorresEmail author
  • Shouyong Jiang
  • David Pelta
  • Marcus Kaiser
  • Natalio Krasnogor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11160)


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.


Strain design Network interdiction Metabolic networks Multiobjective bilevel optimisation 



DP acknowledges support through projects TIN2014-55024-P and TIN2017-86647-P from the Spanish Ministry of Economy and Competitiveness (including European Regional Development Funds). MT enjoys a Ph.D. research training staff grant associated with the project TIN2014-55024-P and co-funded by the European Social Fund.

SJ, MK, and NK acknowledge the EPSRC for funding project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marina Torres
    • 1
    Email author
  • Shouyong Jiang
    • 2
  • David Pelta
    • 1
  • Marcus Kaiser
    • 2
  • Natalio Krasnogor
    • 2
  1. 1.Models of Decision and Optimisation Research Group, Department of Computer Science and A.I.Universidad de GranadaGranadaSpain
  2. 2.School of ComputingNewcastle UniversityNewcastle upon TyneUK

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