A Comparison of Network Characteristics in Metabolic and Manufacturing Systems

  • Till Becker
  • Mirja Meyer
  • Moritz E. Beber
  • Katja Windt
  • Marc-Thorsten Hütt
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

Both metabolic and manufacturing systems face fluctuating environmental influences and thus share the common challenge to maintain a high level of efficiency for a variety of different conditions. Therefore, transferring methods used for analyzing one of the systems can lead to gaining new insights in the other. Following-up on previous findings on analogies in metabolic and manufacturing systems, our approach now is to analyze and compare complex network measures such as centrality or flow activity in both systems to identify quantified relations. The results show that both systems also display distinct statistical differences in addition to their various structural similarities.

Notes

Acknowledgments

We thank Nikolaus Sonnenschein (UC San Diego) for the simulation data of metabolic fluxes and for providing a curated metabolic network structure. The research of Katja Windt is supported by the Alfried Krupp Prize for Young University Teachers of the Krupp Foundation. Marc Hütt acknowledges support from Deutsche Forschungsgemeinschaft (grant HU-937/6).

References

  1. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97MATHCrossRefGoogle Scholar
  2. Albert R, Jeong H, Barabási A-L (1999) Diameter of the world-wide web. Nature 401:130–131CrossRefGoogle Scholar
  3. Armbruster D, Degond P, Ringhofer C (2005) Continuum models for interacting machines. In: Armbruster D, Kaneko K, Mikhailov A (eds) Networks of interacting machines. World Scientific Publishing, SingaporeGoogle Scholar
  4. Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512MathSciNetCrossRefGoogle Scholar
  5. Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113CrossRefGoogle Scholar
  6. Bebber DP, Hynes J, Darrah PR, Boddy L, Fricker M (2007) Biological solutions to transport network design. Proc R Soc B 274:2307–2315CrossRefGoogle Scholar
  7. Becker T, Beber ME, Windt K, Hütt M-T, Helbing D (2011) Flow control by periodic devices: a unifying language for the description of traffic, production, and metabolic systems. J Stat Mech: Theory Exp 2011:P05004CrossRefGoogle Scholar
  8. Bell JE, McMullen PR (2004) Ant colony optimization techniques for the vehicle routing problem. Adv Eng Inform 8:41–48CrossRefGoogle Scholar
  9. Costa LDF, Rodrigues FA, Travieso G, Boas PRV (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56:167–242CrossRefGoogle Scholar
  10. Erdös P, Renyi A (1959) On random graphs, Publicationes Mathematicae (Debrecen). 6:290—297Google Scholar
  11. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology 3 (June 2007)Google Scholar
  12. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41CrossRefGoogle Scholar
  13. Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, Lucau-Danila A, Anderson K, Andre B et al (2002) Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–391CrossRefGoogle Scholar
  14. Hammel C, Flemming A, Schulze F, Peters K (2008) Anwendung von Methoden aus der Theorie Komplexer Netzwerke für die Optimierung der Layouts von MFS, Technische Universität Chemitz (Hrsg.): 4. Fachkolloquium der WGTL, pp. 81–91Google Scholar
  15. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–C52CrossRefGoogle Scholar
  16. Helbing D, Deutsch A, Diez S, Peters K, Kalaidzidis Y, Padberg-Gehle K, Lämmer S, Johansson A, Breier G, Schulze F et al (2009) Biologistics and the struggle for efficiency: concepts and perspectives. Adv Complex Syst 12:533–548CrossRefGoogle Scholar
  17. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi A-L (2000) The large-scale organization of metabolic networks. Nature 407(6804):651–654 October 5, 2000CrossRefGoogle Scholar
  18. Ma H, Zeng A-P (2003) Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19(2):270–277 January 22, 2003CrossRefGoogle Scholar
  19. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298(5594):824–827 October 2002CrossRefGoogle Scholar
  20. Montañez R, Medina MA, Solé RV, Rodríguez-Caso C (2010) When metabolism meets topology: reconciling metabolite and reaction networks. BioEssays: News Rev Mol, Cell Dev Biol 32(3):246–256 March 2010CrossRefGoogle Scholar
  21. Newman MEJ, Park J (2003) Why social networks are different from other types of networks. Phys Rev E 68:036122-1–036122-8Google Scholar
  22. Papakostas N, Efthymiou K, Mourtzis D, Chryssolouris G (2009) Modelling the complexity of manufacturing systems using nonlinear dynamics approaches. CIRP Ann Manufact Technol 58:437–440CrossRefGoogle Scholar
  23. Papin JA, Palsson BO (2004) Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk. J Theor Biol 227:283–297CrossRefGoogle Scholar
  24. Papp B, Teusink B, Notebaart RA (2009) A critical view of metabolic network adaptations. HFSP J 3(1):24CrossRefGoogle Scholar
  25. Peters K, Seidel T, Lämmer S, Helbing D (2008) Logistics networks: coping with nonlinearity and complexity. In: Helbing D (ed.): Managing complexity: insights, concepts, applications, SpringerGoogle Scholar
  26. Ravasz E, Somera AL, Monaru DA, Oltvai ZN, Barabási A-L (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555 August 2002CrossRefGoogle Scholar
  27. Tero A, Takagi S, Saigusa T, Ito K, Bebber DP, Fricker MD, Yumiki K, Kobayashi R, Nakagaki T (2010) Rules for biologically inspired network design. Sciene 327:439–442MathSciNetMATHCrossRefGoogle Scholar
  28. Tharumarajah A, Wells AJ, Nemes L (1998) Comparison of emerging manufacturing concepts. In: SMC’98 Conference. Proceedings of 1998 IEEE international conference on systems, man, and cybernetics, pp. 325–331Google Scholar
  29. Ueda K, Vaario J, Ohkura K (1997) Modelling of biological manufacturing systems for dynamic reconfiguration. CIRP Ann Manufact Technol 46:343–346CrossRefGoogle Scholar
  30. Varma A, Palsson BØ (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotech 12(10):994–998 October 1994CrossRefGoogle Scholar
  31. Vrabič R, Butala P (2011) Computational mechanics approach to managing complexity in manufacturing systems. CIRP Ann Manufact Technol 60:503–506CrossRefGoogle Scholar
  32. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442CrossRefGoogle Scholar
  33. Ziv E, Koytcheff R, Middendorf M, Wiggins C (2005) Systematic identification of statistically significant network measures. Phys Rev 71:1–8Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Till Becker
    • 1
  • Mirja Meyer
    • 1
  • Moritz E. Beber
    • 1
  • Katja Windt
    • 1
  • Marc-Thorsten Hütt
    • 1
  1. 1.Jacobs UniversityBremenGermany

Personalised recommendations