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Natural Computing

, Volume 13, Issue 3, pp 321–331 | Cite as

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

  • Chiara DamianiEmail author
  • Dario Pescini
  • Riccardo Colombo
  • Sara Molinari
  • Lilia Alberghina
  • Marco VanoniEmail author
  • Giancarlo Mauri
Article

Abstract

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O2 consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.

Keywords

Flux balance analysis Constraint-based modeling Genetic algorithm Metabolic network modeling Crabtree effect Randomized sampling 

Notes

Acknowledgments

This work has been supported by grants from Regione Lombardia (NEDD) to LA, GM and MV and and FP7 (Unicellsys) to LA and MV and from project SysBioNet, Italian Roadmap Research Infrastructures 2012 to LA. We warmly thank Prof. Hans Westerhoff for constructive discussions and criticism.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Chiara Damiani
    • 1
    • 2
    Email author
  • Dario Pescini
    • 1
    • 3
  • Riccardo Colombo
    • 1
    • 2
  • Sara Molinari
    • 1
    • 4
  • Lilia Alberghina
    • 1
    • 4
  • Marco Vanoni
    • 1
    • 4
    Email author
  • Giancarlo Mauri
    • 1
    • 2
  1. 1.SYSBIO - Centre for Systems BiologyMilanItaly
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanItaly
  3. 3.Dipartimento di Statistica e Metodi QuantitativiUniversità degli Studi di Milano-BicoccaMilanItaly
  4. 4.Dipartimento di Biotecnologie e BioscienzeUniversità degli Studi di Milano-BicoccaMilanItaly

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