Literature DB >> 18683253

A hidden square-root boundary between growth rate and biomass yield.

Wilson W Wong1, Linh M Tran, James C Liao.   

Abstract

Although the theoretical value of biomass yield can be calculated from metabolic network stoichiometry, the growth rate is difficult to predict. Since the rate and yield can vary independently, no simple relationship has been discovered between these two variables. In this work, we analyzed the well-accepted enzyme kinetics and uncovered a hidden boundary for growth rate, which is determined by the square-root of three physiological parameters: biomass yield, the substrate turnover number, and the maximum synthesis rate of the turnover enzyme. Cells cannot grow faster than the square-root of the product of these parameters. This analysis is supported by experimental data and involves essentially no assumptions except (i) the cell is not undergoing a downshift transition, (ii) substrate uptake enzyme activity is proportional to its copy number. This simple boundary (not correlation) has escaped notice for many decades and suggests that the yield calculation does not predict the growth rate, but gives an upper limit for the growth rate. The relationship also explains how growth rate is affected by the yield and sheds lights on strain design for product formation.

Mesh:

Year:  2009        PMID: 18683253     DOI: 10.1002/bit.22046

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  6 in total

1.  Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection.

Authors:  Michael Manhart; Bharat V Adkar; Eugene I Shakhnovich
Journal:  Proc Biol Sci       Date:  2018-02-14       Impact factor: 5.349

2.  Phenomenological model for predicting the catabolic potential of an arbitrary nutrient.

Authors:  Samuel M D Seaver; Marta Sales-Pardo; Roger Guimerà; Luís A Nunes Amaral
Journal:  PLoS Comput Biol       Date:  2012-11-01       Impact factor: 4.475

3.  Trait variation in yeast is defined by population history.

Authors:  Jonas Warringer; Enikö Zörgö; Francisco A Cubillos; Amin Zia; Arne Gjuvsland; Jared T Simpson; Annabelle Forsmark; Richard Durbin; Stig W Omholt; Edward J Louis; Gianni Liti; Alan Moses; Anders Blomberg
Journal:  PLoS Genet       Date:  2011-06-16       Impact factor: 5.917

4.  The complex relationship between microbial growth rate and yield and its implications for ecosystem processes.

Authors:  David A Lipson
Journal:  Front Microbiol       Date:  2015-06-16       Impact factor: 5.640

5.  Analytical solution for a hybrid Logistic-Monod cell growth model in batch and continuous stirred tank reactor culture.

Authors:  Peng Xu
Journal:  Biotechnol Bioeng       Date:  2019-12-02       Impact factor: 4.530

6.  Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

Authors:  Roi Adadi; Benjamin Volkmer; Ron Milo; Matthias Heinemann; Tomer Shlomi
Journal:  PLoS Comput Biol       Date:  2012-07-05       Impact factor: 4.475

  6 in total

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