Skip to main content

Action and Power Efficiency in Self-Organization: The Case for Growth Efficiency as a Cellular Objective in Escherichia coli

  • Conference paper
  • First Online:
Evolution, Development and Complexity

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

  • 1058 Accesses

Abstract

Complex systems of different nature self-organize using common mechanisms. One of those is increase of their efficiency. The level of organization of complex systems of different nature can be measured as increased efficiency of the product of time and energy for an event, which is the amount of physical action consumed by it. Here we apply a method developed in physics to study the efficiency of biological systems. The identification of cellular objectives is one of the central topics in the research of microbial metabolic networks. In particular, the information about a cellular objective is needed in flux balance analysis which is a commonly used constrained-based metabolic network analysis method for the prediction of cellular phenotypes. The cellular objective may vary depending on the organism and its growth conditions. It is probable that nutritionally scarce conditions are very common in the nature, and, in order to survive in those conditions, cells exhibit various highly efficient nutrient-processing systems like enzymes. In this study, we explore the efficiency of a metabolic network in transformation of substrates to new biomass, and we introduce a new objective function simulating growth efficiency. We are searching for general principles of self-organization across systems of different nature. The objective of increasing efficiency of physical action has been identified previously as driving systems toward higher levels of self-organization. The flow agents in those networks are driven toward their natural state of motion, which is governed by the principle of least action in physics. We connect this to a power efficiency principle. Systems structure themselves in a way to decrease the average amount of action or power per one event in the system. In this particular example, action efficiency is examined in the case of growth efficiency of E. coli. We derive the expression for growth efficiency as a special case of action (power) efficiency to justify it through first principles in physics. Growth efficiency as a cellular objective of E. coli coincides with previous research on complex systems and is justified by first principles in physics. It is expected and confirmed outcome of this work. We examined the properties of growth efficiency using a metabolic model for Escherichia coli. We found that the maximal growth efficiency is obtained at a finite nutrient uptake rate. The rate is substrate dependent and it typically does not exceed 20 mmol/h/gDW. We further examined whether the maximal growth efficiency could serve as a cellular objective function in metabolic network analysis and found that cellular growth in batch cultivation can be predicted reasonably well under this assumption. The fit to experimental data was found slightly better than with the commonly used objective function of maximal growth rate. Based on our results, we suggest that the maximal growth efficiency can be considered a plausible optimization criterion in metabolic modeling for E. coli. In the future, it would be interesting to study growth efficiency as an objective also in other cellular systems and under different cultivation conditions.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
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

  • T. Baba, T. Ara, M. Hasegawa, Y. Takai, Y. Okumura, M. Baba, K.A. Datsenko, M. Tomita, B.L. Wanner, H. Mori, Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection, Mol. Syst. Biol. 2 (2006) 2006.0008.

    Article  Google Scholar 

  • S.A. Becker, A.M. Feist, M.L. Mo, G. Hannum, B.Ø. Palsson, M.J. Herrgard, Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox, Nat. Protocols 2 (2007) 727–738.

    Article  Google Scholar 

  • L.M. Blank, L. Kuepfer, U. Sauer, Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast, Genome Biol. 6 (2005) R49.

    Article  Google Scholar 

  • H.P.J. Bonarius, V. Hatzimanikatis, K.P.H. Meesters, C.D. de Gooijer, G. Schmid, J. Tramper, Metabolic flux analysis of hybridoma cells in different culture media using mass balances, Biotechnol. Bioeng. 50 (1996) 299–318.

    Article  Google Scholar 

  • M. Dauner, U. Sauer, Stoichiometric growth model for riboflavin-producing Bacillus subtilis, Biotechnol. Bioeng. 76 (2001) 132–143.

    Article  Google Scholar 

  • O. Ebenhoh, R. Heinrich, Evolutionary optimization of metabolic pathways. Theoretical reconstruction of the stoichiometry of ATP and NADH producing systems, Bull. Math. Biol. 63 (2001) 21–55.

    Article  Google Scholar 

  • A.M. Feist, C.S. Henry, J.L. Reed, M. Krummenacker, A.R. Joyce, P.D. Karp, L.J. Broadbelt, V. Hatzimanikatis, B.Ø. Palsson, A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information, Mol. Syst. Biol. 3 (2007) 121.

    Article  Google Scholar 

  • A.M. Feist, B.Ø. Palsson, The biomass objective function, Curr. Opin. Microbiol., 13 (2010) 344–349.

    Article  Google Scholar 

  • D.A. Fell, J.R. Small, Fat synthesis in adipose tissue. An examination of stoichiometric constraints, Biochem J 238 (1986) 781–786.

    Article  Google Scholar 

  • J.D. Glasner, P. Liss, G. Plunkett 3rd, A. Darling, T. Prasad, M. Rusch, A. Byrnes, M. Gilson, B. Biehl, F.R. Blattner, N.T. Perna, ASAP, a systematic annotation package for community analysis of genomes, Nucleic Acids Res. 31 (2003) 147–151.

    Article  Google Scholar 

  • R. Heinrich, F. Montero, E. Klipp, T.G. Waddell, E. Melendez-Hevia, Theoretical approaches to the evolutionary optimization of glycolysis: thermodynamic and kinetic constraints, Eur. J. Biochem. 243 (1997) 191–201.

    Article  Google Scholar 

  • A.L. Knorr, R. Jain, R. Srivastava, Bayesian-based selection of metabolic objective functions, Bioinformatics 23 (2007) 351–357.

    Article  Google Scholar 

  • E. Meléndez-Hevia, A. Isidoro, The game of the pentose phosphate cycle, J. Theor. Biol. 117 (1985) 251–263.

    Article  Google Scholar 

  • J. Monod, Chance and Necessity: An Essay on the Natural Philosophy of Modern Biology, first ed., Knopf, New York, 1971.

    Google Scholar 

  • A.P. Oliveira, J. Nielsen, J. Forster, Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol 5 (2005) 39.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • R. Ramakrishna, J.S. Edwards, A. McCulloch, B.Ø. Palsson, Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints, Am. J. Physiol. Regul. Integr. Comp. Physiol. 280 (2001) R695–R704.

    Article  Google Scholar 

  • R. Schuetz, L. Kuepfer, U. Sauer, Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli, Mol. Syst. Biol. 3 (2007) 119.

    Article  Google Scholar 

  • W.M. van Gulik, J.J. Heijnen, A metabolic network stoichiometry analysis of microbial growth and production formation, Biotech. Bioeng. 48 (1995) 681–698.

    Article  Google Scholar 

  • K. Valgepea, K. Adamberg, R. Nahku, P.-J. Lahtvee, L. Arike, R. Vilu, Systems biology approach reveals that overflow metabolism of acetate in Escherichia coli is triggered by carbon catabolite repression of acetyl-CoA synthetase, BMC Syst. Biol. 4 (2010) 166.

    Article  Google Scholar 

  • A. Varma, B.Ø. Palsson, Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110, Appl. Environ. Microbiol. 60 (1994) 3724–3731.

    Google Scholar 

  • H. Goldstein, Classical Mechanics, (2nd ed., Addison Wesley, 1980).

    Google Scholar 

  • Pierre de Maupertuis, Essai de cosmologie, (1750).

    Google Scholar 

  • Georgiev, G.Y., and Georgiev, I., 2002. The Least Action and the Metric of an Organized System. Open Systems & Information Dynamics 9: 371–380.

    Article  Google Scholar 

  • Georgiev, G.Y., Daly, M., Gombos, E., Vinod, A., and Hoonjan, G., 2012. Increase of Organization in Complex Systems. World Academy of Science, Engineering and Technology 71. preprint arXiv:1301.6288.

    Google Scholar 

  • Georgiev, G.Y., Quantitative Measure, Mechanism and Attractor for Self-Organization in Networked Complex Systems. Self-Organizing Systems LNCS 7166: 90–95.2012.

    Article  Google Scholar 

  • Georgiev G.Y., Henry K., Bates T., Gombos E., Casey A., Lee H., Daly M., and Vinod A., “Mechanism of organization increase in complex systems”, Complexity, 21(2), 18–28, DOI: https://doi.org/10.1002/cplx.21574 7/25 (2015).

  • Georgi Yordanov Georgiev, Atanu Chatterjee “The road to a measurable quantitative understanding of self-organization and evolution” Ch. 15. In Evolution and Transitions in Complexity, Eds. Gerard Jagers op Akkerhuis, Springer International Publishing, (2016). p. 223–230.

    Google Scholar 

  • Georgi Yordanov Georgiev, Erin Gombos, Timothy Bates, Kaitlin Henry, Alexander Casey, Michael Daly “Free Energy Rate Density and Self-organization in Complex Systems” Ch. 27. p. 321–327. In Springer Proceedings in Complexity Proceedings of ECCS 2014: European Conference on Complex Systems, Eds. S. De Pellegrini, G. Caldarelli, E. Merelli, Springer International Publishing, ISBN 3319292269, (2016a)

    Google Scholar 

  • Georgiev G.Y., Chatterjee A., Iannacchione G.S. “Exponential Self-Organization and Moore’s Law: Measures and Mechanisms” Complexity, 2016b. Article ID 8170632

    Google Scholar 

  • Chatterjee, A., 2012. Action, an Extensive Property of Self–Organizing Systems. International Journal of Basic and Applied Sciences 1(4): 584–593.

    Google Scholar 

  • Chatterjee, A., 2013. Principle of least action and convergence of systems towards state of closure. International Journal of Physical Research 1(1): 21–27.

    Article  Google Scholar 

  • Annila, A. and Salthe, S., 2010. Physical foundations of evolutionary theory. Journal of Non-Equilibrium Thermodynamics 353: 301–321.

    ADS  MATH  Google Scholar 

  • Annila, A., 2010. All in Action. Entropy. 1211: 2333–2358.

    Google Scholar 

  • Kauffman S. A., “The origins of order: Self organization and selection in evolution” Oxford University press (1993)

    Google Scholar 

  • Giovanni P. and Levin M. “Top-down models in biology: explanation and control of complex living systems above the molecular level.” Journal of The Royal Society Interface 13.124 (2016): 20160555.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Academy of Finland (Finnish Programme for Centres of Excellence in Research 2006–2011) and the FiDiPro programme of Finnish Funding Agency for Technology and Innovation. GG thanks Assumption College for a Faculty Development Grant and financial support from the Department of Natural Sciences at Assumption College.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Georgi Yordanov Georgiev .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Georgiev, G.Y., Aho, T., Kesseli, J., Yli-Harja, O., Kauffman, S.A. (2019). Action and Power Efficiency in Self-Organization: The Case for Growth Efficiency as a Cellular Objective in Escherichia coli . In: Georgiev, G., Smart, J., Flores Martinez, C., Price, M. (eds) Evolution, Development and Complexity. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-00075-2_8

Download citation

Publish with us

Policies and ethics