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An evolutionary procedure for inferring MP systems regulation functions of biological networks

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Abstract

Metabolic P systems are a modeling framework for metabolic, regulatory and signaling processes. The key point of MP systems are flux regulation functions, which determine the evolution of a system from a given initial state. This paper presents important improvements to a technique, based on genetic algorithms and multiple linear regression, for inferring regulation functions that reproduce observed behaviors (time series datasets). An accurate analysis of three case studies, namely the mitotic oscillator in early amphibian embryos, the Lodka–Volterra predator-prey model and the chaotic logistic map show that this methodology can provide, from observed data, significant knowledge about the regulation mechanisms underlying biological processes.

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References

  • Alon U (2006) An introduction to systems biology: design principles of biological circuits. 1st edn, Chapman and Hall/CRC, Boca Raton

    Google Scholar 

  • Besozzi D, Cazzaniga P, Mauri G, Pescini D, Vanneschi L (2009) A comparison of genetic algorithms and particle swarm optimization for parameter estimation in stochastic biochemical systems. In: Onwubolu C (ed) 7th European conference, EvoBIO 2009. Lecture notes in computer science 5483, Springer, London, pp 116–127

    Google Scholar 

  • Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP (2009) A yeast synthetic network for in vivo assessment of Reverse-Engineering and modeling approaches. Cell 137(1):172–181

    Article  Google Scholar 

  • Cao H, Romero-Campero FJ, Heeb S, Cámara M, Krasnogor N (2010) Evolving cell models for systems and synthetic biology. Systems and synthetic biology 4(1):55–84

    Article  Google Scholar 

  • Castellini A, Franco G, Pagliarini R (2011) Data analysis pipeline from laboratory to MP models. Nat Comp 10(1):55–76

    Article  MathSciNet  Google Scholar 

  • Castellini A, Manca V (2009) MetaPlab: a computational framework for metabolic P systems. In: Pérez-Jiménez MJ (ed) 9th international workshop on membrane computing. Lecture notes in computer science 5391, Springer, New York, pp 157–168

    Google Scholar 

  • Castellini A, Manca V, Zucchelli M (2012) Towards an evolutionary procedure for reverse-engineering biological networks. In: Coello CA (ed) Artificial Immune systems—11th international conference, ICARIS 2012. Lecture notes in computer science 7597, Springer, New York, pp 271–285

    Google Scholar 

  • Castellini A, Zucchelli M, Busato M, Manca V (2013) From time series to biological network regulations: an evolutionary approach. Mol Biosys 9:225–233

    Article  Google Scholar 

  • Di Ventura B, Lemerle C, Michalodimitrakis K, Serrano L (October 2006) From in vivo to in silico biology and back. Nature 443(7111):527–533

    Article  Google Scholar 

  • Fontana F, Manca V (2008) Predator-prey dynamics in P systems ruled by metabolic algorithm. Biosystems 91(3):545–557

    Article  Google Scholar 

  • Garcia-Martinez C, Lima C, Twycross J, Lozano M, Krasnogor N (2010) P system model optimisation by means of evolutionary based search algorithms. In: Proceedings of the 12th annual conference on genetic and evolutionary computation, GECCO ’10, Portland, ACM Publisher, pp 545–557

  • Goldbeter A (1991) A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. PNAS 88(20):9107–9111

    Article  Google Scholar 

  • Manca V (2009) Fundamentals of metabolic P systems. In Handbook of Membrane Computing, chapter 19. Oxford University Press, Oxford

    Google Scholar 

  • Manca V (2013) Infobiotics: information in biotic systems. Springer, New York

    Book  Google Scholar 

  • Manca V, Bianco L (2008) Biological networks in metabolic P systems. Biosystems 91(3):489–498

    Article  Google Scholar 

  • Manca V, Marchetti L (2012) Solving dynamical inverse problems by means of metabolic P systems. Biosystems 109:78–86

    Article  Google Scholar 

  • Manca V, Marchetti L (2013) An algebraic formulation of the inverse problems in MP dynamics. Int J Comp Math. doi:10.1080/00207160.2012.735362

    MathSciNet  Google Scholar 

  • Manca V, Marchetti L, Pagliarini R (2011) MP modelling of glucose-insulin interactions in the intravenous glucose tolerance test. Int J Nat Comp Res 2(3):13–24

    Article  Google Scholar 

  • May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261:459–467

    Article  Google Scholar 

  • Mitchell M (1998) An introduction to genetic algorithms. MIT Press, London

    MATH  Google Scholar 

  • Nobile MS, Besozzi D, Cazzaniga P, Mauri G, Pescini D (2012) A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Willam S (ed) EvoBIO 2012. Lecture notes in computer science 7246, Springer, London, pp 74–85

    Google Scholar 

  • Pérez-Jiménez MJ, Romero-Campero FJ (2006) P systems: a new computational modelling tool for systems biology. Trans Comp Sys Biol VI 4220:176–197

    Article  Google Scholar 

  • Pescini D, Besozzi D, Mauri G, Zandron C (2006) Dynamical probabilistic P systems. Int J Found Comp Sci 17(1):183–204

    Article  MathSciNet  MATH  Google Scholar 

  • Priami C (2009) Algorithmic systems biology. Commun ACM 52(5):80–88

    Article  Google Scholar 

  • Păun G (2002) Membrane computing. An introduction. Springer, Berlin

    Book  Google Scholar 

  • Păun G, Pérez-Jiménez MJ (2006) Membrane computing: brief introduction, recent results and applications. Biosystems 85:11–22

    Article  Google Scholar 

  • Păun G, Rozenberg G, Salomaa A (2010) The oxford handbook of membrane computing. Oxford University Press Inc, New York

    Book  MATH  Google Scholar 

  • Romero-Campero FJ, Cao H, Camera M, Krasnogor N (2008) Structure and parameter estimation for cell systems biology models. In: Proceedings of GECCO, ACM Publisher, Atlanta, pp 331–338

  • Suzuki Y, Tsumoto S, Tanaka H (1996) Analysis of cycles in symbolic chemical system based on abstract rewriting system on multisets. In: Proceedings of the international conference on artificial life V, MIT press, London, pp 482–489

  • Szallasi Z, Stelling J, Periwal V (eds) (2006) System modeling in cellular biology: from concepts to nuts and bolts. The MIT Press, London, 1 edition

    Book  Google Scholar 

  • Tudose C, Lefticaru R, Ipate F (2011) Using genetic algorithms and model checking for P systems automatic design. In: Rong H (ed) NICSO, volume 387 of studies in computational intelligence, Springer, London, pp 285–302

    Google Scholar 

  • Voit E, Neves AR, Santos H (2006) The intricate side of systems biology. PNAS 103(25):9452–9457

    Article  Google Scholar 

  • von Bertalanffy L (1967) General systems theory: foundations, developments, applications. George Braziller Inc., New York

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Verona University, Strada Le Grazie 15, 37134, Verona, Italy

    Alberto Castellini, Vincenzo Manca & Mauro Zucchelli

Authors
  1. Alberto Castellini
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  2. Vincenzo Manca
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  3. Mauro Zucchelli
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Correspondence to Alberto Castellini.

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Castellini, A., Manca, V. & Zucchelli, M. An evolutionary procedure for inferring MP systems regulation functions of biological networks. Nat Comput 14, 375–391 (2015). https://doi.org/10.1007/s11047-014-9421-1

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  • DOI: https://doi.org/10.1007/s11047-014-9421-1

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