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. 2012;7(2):e32003.
doi: 10.1371/journal.pone.0032003. Epub 2012 Feb 14.

Universality of thermodynamic constants governing biological growth rates

Affiliations

Universality of thermodynamic constants governing biological growth rates

Ross Corkrey et al. PLoS One. 2012.

Abstract

Background: Mathematical models exist that quantify the effect of temperature on poikilotherm growth rate. One family of such models assumes a single rate-limiting 'master reaction' using terms describing the temperature-dependent denaturation of the reaction's enzyme. We consider whether such a model can describe growth in each domain of life.

Methodology/principal findings: A new model based on this assumption and using a hierarchical Bayesian approach fits simultaneously 95 data sets for temperature-related growth rates of diverse microorganisms from all three domains of life, Bacteria, Archaea and Eukarya. Remarkably, the model produces credible estimates of fundamental thermodynamic parameters describing protein thermal stability predicted over 20 years ago.

Conclusions/significance: The analysis lends support to the concept of universal thermodynamic limits to microbial growth rate dictated by protein thermal stability that in turn govern biological rates. This suggests that the thermal stability of proteins is a unifying property in the evolution and adaptation of life on earth. The fundamental nature of this conclusion has importance for many fields of study including microbiology, protein chemistry, thermal biology, and ecological theory including, for example, the influence of the vast microbial biomass and activity in the biosphere that is poorly described in current climate models.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Observed and predicted growth rates by strain.
Observed square root growth rate data are shown as circles and are standardized by dividing by the maximum for each strain. Fitted curves are shown as lines. Strain numbering is given in Table S1. Bacterial strains are shown as green circles, archaeal strains as blue squares, and eukaryote strains as red diamonds.
Figure 2
Figure 2. Posterior statistics for selected parameters.
Posterior means and 95% HPDI are shown for formula image, formula image, formula image, formula image, and formula image. Bacterial strains are shown as circles, archaeal strains as squares, and eukaryote strains as diamonds. Posterior domain distributions are shown on the right margin. For both symbols and marginal distributions domains are colored green for Bacteria, blue for Archaea, and red for Eukarya. The strains are arranged so that those belonging to the same species are grouped contiguously. We show this by vertical gray and white shading that indicate when the strain species change; for example, strains 6—18 are all E. coli.
Figure 3
Figure 3. Fitted curves for growth rate by strain.
Shown are the mean predicted growth curves plotted on a vertical log scale against the reciprocal of temperature. The colored portions indicate the observed temperature ranges for each strain, and the dashed portions are extrapolations outside these ranges. The curves are labeled with strain codes and domain. The domains are colored green for Bacteria, blue for Archaea, red for Eukarya.
Figure 4
Figure 4. The probability of an enzyme being in its native state.
Shown is the mean curve for each strain. The colored portions indicate the observed temperature ranges for each strain, and the dashed portions are extrapolations outside these ranges in order to show their shapes. The curves are labeled with strain codes and domain. The domains are colored green for Bacteria, blue for Archaea, red for Eukarya.

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