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

  • Georgi Yordanov GeorgievEmail author
  • Tommi Aho
  • Juha Kesseli
  • Olli Yli-Harja
  • Stuart A. Kauffman
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


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.


Constraint-based modeling Metabolism Microorganism Principle of least action Action efficiency 



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.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Georgi Yordanov Georgiev
    • 1
    • 2
    • 3
    Email author
  • Tommi Aho
    • 4
  • Juha Kesseli
    • 5
  • Olli Yli-Harja
    • 4
  • Stuart A. Kauffman
    • 4
    • 6
  1. 1.Department of PhysicsWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Department of PhysicsAssumption CollegeWorcesterUSA
  3. 3.Department of PhysicsTufts UniversityMedfordUSA
  4. 4.Department of Signal ProcessingTampere University of TechnologyTampereFinland
  5. 5.BioMediTech Institute and Faculty of Medicine and Life SciencesUniversity of TampereTampereFinland
  6. 6.Complex Systems CenterUniversity of VermontBurlingtonUSA

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