Skip to main content

How Do Production Systems in Biological Cells Maintain Their Function in Changing Environments?

  • Conference paper
  • First Online:
Book cover Robust Manufacturing Control

Part of the book series: Lecture Notes in Production Engineering ((LNPE))

  • 1468 Accesses

Abstract

Metabolism is a fascinating natural production and distribution process. Metabolic systems can be represented as a layered network, where the input layer consists of all the nutrients in the environment (raw materials entering the production process in the cell), subsequently to be processed by a complex network of biochemical reactions (middle layer) and leading to a well-defined output pattern, optimizing, e.g., cell growth. Mathematical frameworks exploiting this layered-network representation of metabolism allow the prediction of metabolic fluxes (the cell’s ’material flow’) under diverse conditions. In combination with suitable minimal models it is possible to identify fundamental design principles and understand the efficiency and robustness of metabolic systems. Here, we summarize some design principles of metabolic systems from the perspective of production logistics and explore, how these principles can serve as templates for the design of robust manufacturing systems.

This contribution was previously published in Logistics Research (2012) pp. 79–87. DOI: 10.1007/s12159 012-0090-0.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Becker, T., Beber, M., Windt, K., Hütt, M., Helbing, D.: Flow control by periodic devices: a unifying language for the description of traffic, production, and metabolic systems. J. Stat. Mech. Theory Exp. 2011, P05004 (2011)

    Article  Google Scholar 

  2. Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)

    Article  Google Scholar 

  3. Palsson, B.: Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  4. Kholodenko, B.: Cell-signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol. 7, 165–176 (2006)

    Article  Google Scholar 

  5. Demeester, L., Eichler, K., Loch, C.H.: Organic production systems: what the biological cell can teach us about manufacturing. Manuf. Serv. Oper. Manage. 6, 115–132 (2004)

    Article  Google Scholar 

  6. Armbruster, D., Mikhailov, A.S., Kaneko, K.: Networks of Interacting Machines. World Scientific, Singapore (2005)

    Google Scholar 

  7. Helbing, D., Deutsch, A., Diez, S., Peters, K., Kalaidzidis, Y., Padberg-Gehle, K., Lämmer, S., Johansson, A., Breier, G., Schulze, F., et al.: Biologistics and the struggle for efficiency: concepts and perspectives. Adv. Complex Syst. 12, 533–548 (2009)

    Article  Google Scholar 

  8. Beber, M., Windt, K., Hütt, M.T.: Production research on metabolic systems. In: Spath, D., Ilg, R., Krause, T. (eds.) International Conference on Production Research (ICPR 21): Innovation in Product and Production 31 July–4 August 2011 in Stuttgart. Stuttgart, Germany, Fraunhofer-Verlag, Germany (2011)

    Google Scholar 

  9. Beber, M.E., Armbruster, D., Hütt, M.T.: Pattern complexity regulates modularity of flow networks. Phys. Rev. E (2012) (submitted)

    Google Scholar 

  10. Ueda, K., Vaario, J., Ohkura, K.: Modelling of biological manufacturing systems for dynamic reconfiguration. CIRP Ann. Manuf. Technol. 46, 343–346 (1997)

    Article  Google Scholar 

  11. Ueda, K., Kito, T., Fujii, N.: Modeling biological manufacturing systems with bounded-rational agents. CIRP Ann. Manuf. Technol. 55, 469–472 (2006)

    Article  Google Scholar 

  12. Ueda, K., Markus, A., Monostori, L., Kals, H.J.J., Arai, T.: Emergent synthesis methodologies for manufacturing. CIRP Ann. Manuf. Technol. 50, 535–551 (2001)

    Article  Google Scholar 

  13. Smith, J., Hütt, M.: Network dynamics as an interface between modeling and experiment in systems biology. In: Tretter, F., Gebicke-Haerter, P.J., Mendoza, E.R., Winterer, G. (eds.) Systems Biology in Psychiatric Research: From High-Throughput Data to Mathematical Modeling, pp. 234–276. Wiley-VCH(2010)

    Google Scholar 

  14. Varma, A., Palsson, B.O.: Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotech. 12, 994–998 (1994)

    Article  Google Scholar 

  15. Price, N.D., Reed, J.L., Palsson, B.Ø.: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol. 2, 886–897 (2004)

    Article  Google Scholar 

  16. Sonnenschein, N., Geertz, M., Muskhelishvili, G., Hütt, M.T.: Analog regulation of metabolic demand. BMC Syst. Biol. 5, 40 (2011)

    Article  Google Scholar 

  17. Barabási, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5, 101–113 (2004)

    Article  Google Scholar 

  18. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  19. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)

    Article  Google Scholar 

  20. Ma, H., Zeng, A.: The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics 19, 1423–1430 (2003)

    Article  Google Scholar 

  21. Arita, M.: The metabolic world of Escherichia coli is not small. Proc. Natl. Acad. Sci. USA 101, 1543–1547 (2004)

    Article  Google Scholar 

  22. Erdős, P., Rényi, A.: On random graphs i. Publ. Math. Debrecen 6, 290 (1959)

    Google Scholar 

  23. Becker, T., Beber, M.E., Meyer, M., Windt, K., Hütt, M.T.: A comparison of network characteristics in metabolic and manufacturing systems. In: 3rd International Conference on Dynamics in Logistics—LDIC 2012, Springer (2012)

    Google Scholar 

  24. Ravasz, E., Somera, A.L., Monaru, D.A., Oltvai, Z.N., Barabási, A.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)

    Article  Google Scholar 

  25. Beber, M., Fretter, C., Jain, S., Müller-Hannemann, M., Hütt, M.T.: Artefacts in statistical analyses of network motifs. Proc. Roy. Soc. Interface (2012) (submitted)

    Google Scholar 

  26. Papp, B., Teusink, B., Notebaart, R.A.: A critical view of metabolic network adaptations. HFSP J. 3, 24–35 (2009)

    Article  Google Scholar 

  27. Basler, G., Grimbs, S., Ebenhöh, O., Selbig, J., Nikoloski, Z.: Evolutionary significance of metabolic network properties. J. The Roy. Soc. Interface (2011)

    Google Scholar 

  28. Handorf, T., Ebenhoh, O., Heinrich, R.: Expanding metabolic networks: scopes of compounds, robustness, and evolution. J. Mol. Evol. 61, 498–512 (2005)

    Article  Google Scholar 

  29. Riehl, W.J., Krapivsky, P.L., Redner, S., Segrè, D.: Signatures of arithmetic simplicity in metabolic network architecture. PLoS Comput. Biol. 6, e1000725 (2010)

    Article  Google Scholar 

  30. Noor, E., Eden, E., Milo, R., Alon, U.: Central carbon metabolism as a minimal biochemical walk between precursors for biomass and energy. Mol. Cell 39, 809–820 (2010)

    Article  Google Scholar 

  31. Maslov, S., Krishna, S., Pang, T., Sneppen, K.: Toolbox model of evolution of prokaryotic metabolic networks and their regulation. Proc. Natl. Acad. Sci. 106, 9743 (2009)

    Article  Google Scholar 

  32. Zhu, Q., Qin, T., Jiang, Y.Y., Ji, C., Kong, D.X., Ma, B.G., Zhang, H.Y.: Chemical basis of metabolic network organization. PLoS Comput. Biol. 7, e1002214 (2011)

    Article  Google Scholar 

  33. Suthers, P.F., Zomorrodi, A., Maranas, C.D.: Genome-scale gene/reaction essentiality and synthetic lethality, analysis. 5 (Dec 2164) 1–17

    Google Scholar 

  34. Behre, J., Wilhelm, T., von Kamp, A., Ruppin, E., Schuster, S.: Structural robustness of metabolic networks with respect to multiple knockouts. J. Theoret. Biol. 252, 433–441 (2008)

    Article  Google Scholar 

  35. Marr, C., Müller-Linow, M., Hütt, M.T.: Regularizing capacity of metabolic networks. Phys. Rev. E. Stat. Nonlinear Soft Matter Phys. 75, 041917 (2007)

    Article  Google Scholar 

  36. Borenstein, E., Kupiec, M., Feldman, M.W., Ruppin, E.: Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc. Natl. Acad. Sci. 105, 14482–14487 (2008)

    Article  Google Scholar 

  37. Takemoto, K., Nacher, J.C., Akutsu, T.: Correlation between structure and temperature in prokaryotic metabolic networks. BMC Bioinf. 8, 303 (2007)

    Article  Google Scholar 

  38. Takemoto, K., Akutsu, T.: Origin of structural difference in metabolic networks with respect to temperature. BMC Syst. Biol. 2, 82 (2008)

    Article  Google Scholar 

  39. Basler, G., Ebenhöh, O., Selbig, J., Nikoloski, Z.: Mass-balanced randomization of metabolic networks. Bioinformatics 27, 1397–1403 (2011)

    Article  Google Scholar 

  40. Fong, S.S., Palsson, B.Ø.: Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet. 36, 1056–1058 (2004)

    Article  Google Scholar 

  41. Segrè, D., Vitkup, D., Church, G.: Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 99, 15112–15117 (2002)

    Article  Google Scholar 

  42. Motter, A.E., Gulbahce, N., Almaas, E., Barabási, A.L.: Predicting synthetic rescues in metabolic networks. Mol. Syst. Biol. 4, 1–10 (2008)

    Article  Google Scholar 

  43. Kim, D.H., Motter, A.E.: Slave nodes and the controllability of metabolic networks. New J. Phys. 11, 113047 (2009)

    Article  Google Scholar 

  44. Windt, K., Hütt, M., Meyer, M.: A modeling approach to analyze redundancy in manufacturing systems. In: ElMaraghy, H.A., (ed.) Enabling Manufacturing Competitiveness and Economic Sustainability: Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), pp. 493–498. Springer (2011)

    Google Scholar 

  45. Kaluza, P., Mikhailov, A.S.: Evolutionary design of functional networks robust against noise. Europhys. Lett. 79, 48001 (2007)

    Article  Google Scholar 

  46. Kaluza, P., Ipsen, M., Vingron, M., Mikhailov, A.: Design and statistical properties of robust functional networks: a model study of biological signal transduction. Phys. Rev. E 75, 15101 (2007)

    Article  Google Scholar 

  47. Kaluza, P., Vingron, M., Mikhailov, A.: Self-correcting networks: function, robustness, and motif distributions in biological signal processing. Chaos 18, 026113 (2008)

    Article  MathSciNet  Google Scholar 

  48. Famili, I., Forster, J., Nielsen, J., Palsson, B.Ø.: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. USA 100, 13134–13139 (2003)

    Article  Google Scholar 

  49. Nam, H., Conrad, T.M., Lewis, N.E.: The role of cellular objectives and selective pressures in metabolic pathway evolution. Curr. Opin. Biotechnol. 22, 1–6 (2011)

    Google Scholar 

  50. Eom, Y.H., Lee, S., Jeong, H.: Exploring local structural organization of metabolic networks using subgraph patterns. J. Theoret. Biol. 241, 823–829 (2006)

    Article  MathSciNet  Google Scholar 

  51. Nyhuis, P., Wiendahl, H.: Fundamentals of Production Logistics: Theory. Springer Verlag, Tools and Applications (2008)

    Google Scholar 

  52. Stange, P., Mikhailov, A.S., Hess, B.: Mutual synchronization of molecular turnover cycles in allosteric enzymes. The J. Physi. Chem. B 102, 6273–6289 (1998)

    Article  Google Scholar 

  53. Casagrande, V., Togashi, Y., Mikhailov, A.: Molecular synchronization waves in arrays of allosterically regulated enzymes. Phys. Rev. Lett. 99, 48301 (2007)

    Article  Google Scholar 

  54. Lämmer, S., Kori, H., Peters, K., Helbing, D.: Decentralised control of material or traffic flows in networks using phase-synchronisation. Physica A 363, 39–47 (2006)

    Article  Google Scholar 

  55. Lämmer, S., Helbing, D.: Self-control of traffic lights and vehicle flows in urban road networks. J. Stat. Mech. Theory Exp. (JSTAT) 2008, P04019 (2008)

    Article  Google Scholar 

  56. Fretter, C., Krumov, L., Weihe, K., Müller-Hannemann, M., Hütt, M.: Phase synchronization in railway timetables. Eur. Phys. J. B 77, 281–289 (2010)

    Article  Google Scholar 

  57. Sonnenschein, N., Marr, C., Hütt, M.T.: A topological characterization of medium-dependent essential metabolic reactions. Metabolites (2012) (submitted)

    Google Scholar 

  58. Marr, C., Theis, F., Liebovitch, L., Hütt, M.: Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli. PLoS Comput. Biol. 6, e1000836 (2010)

    Article  Google Scholar 

  59. Lorenz, J., Battiston, S., Schweitzer, F.: Systemic risk in a unifying framework for cascading processes on networks. Eur. Phys. J. B 71, 441–460 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  60. Alon, U.: Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007)

    Article  Google Scholar 

  61. Brandman, O., Meyer, T.: Feedback loops shape cellular signals in space and time. Science 322, 390–395 (2008)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

MEB is supported by a Deutsche Forschungsgemeinschaft grant to MTH (grant HU-937/6). We are indebted to Nikolaus Sonnenschein (San Diego, USA) for providing his expertise on flux-balance analysis. We gratefully acknowledge discussions and close collaboration with Katja Windt (Bremen, Germany) on the parallels of metabolism and manufacturing.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Moritz Emanuel Beber .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Beber, M.E., Hütt, MT. (2013). How Do Production Systems in Biological Cells Maintain Their Function in Changing Environments?. In: Windt, K. (eds) Robust Manufacturing Control. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30749-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30749-2_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30748-5

  • Online ISBN: 978-3-642-30749-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics