An optimal regulation of fluxes dictates microbial growth in and out of steady state
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eLife assessment
This valuable study provides a synthesis of sector models for cellular resource partitioning in microbes, and shows how a simple flux balance model can quantitatively explain growth phenomena from numerous published experimental datasets. The study is overall convincing, although there are a few incomplete points regarding parameter values (justification and discussion of robustness). This work should be of interest to the microbial physiology community.
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Abstract
Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with ≈ 60 data sets from Escherichia coli establishes this regulatory mechanism’s biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments.
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eLife assessment
This valuable study provides a synthesis of sector models for cellular resource partitioning in microbes, and shows how a simple flux balance model can quantitatively explain growth phenomena from numerous published experimental datasets. The study is overall convincing, although there are a few incomplete points regarding parameter values (justification and discussion of robustness). This work should be of interest to the microbial physiology community.
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Reviewer #1 (Public Review):
In this manuscript, Chure and Cremer first provide a broad panorama of the different sector models for resource allocation in biosynthesis and how they provide an explanation of cellular growth physiology; then they formalise how optimal flux balance (flux parity) can reproduce many different physiological observables in a quantitative manner.
The first part of this study comprises a valuable synthesis of many literature results, which are here gathered together and clearly reformulated. The authors also assembled a rich and impressive collection of experimental published datasets in E. coli from several sources, which are then extensively compared with the outcomes of the models. In my view, these points are the main strengths of the manuscript.
The flux-parity regulation introduced in the second part …
Reviewer #1 (Public Review):
In this manuscript, Chure and Cremer first provide a broad panorama of the different sector models for resource allocation in biosynthesis and how they provide an explanation of cellular growth physiology; then they formalise how optimal flux balance (flux parity) can reproduce many different physiological observables in a quantitative manner.
The first part of this study comprises a valuable synthesis of many literature results, which are here gathered together and clearly reformulated. The authors also assembled a rich and impressive collection of experimental published datasets in E. coli from several sources, which are then extensively compared with the outcomes of the models. In my view, these points are the main strengths of the manuscript.
The flux-parity regulation introduced in the second part emerges from the balance of metabolic and biosynthesis fluxes, which have to be mutually optimised in the authors' framework. Those ingredients are often found in the literature, and the reader has sometimes the impression that novelty is lacking. Although flux balance and optimisation are often assumed in modelling resource allocation, the authors have the merit of formalising the approach in a clearer way than was done before, making an extensive comparison with data.
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Reviewer #2 (Public Review):
The authors propose a proteome allocation model which includes a ribosomal and metabolic sector (and an additional sector in the case of nutrient upshift or downshift), and they consider the effect of tRNA charging on translation. It appears that the rate of protein generation via translation by ribosomes and the rate of tRNA charging via metabolic proteins are mutually maximized (the so-called "flux-parity regulation"). Based on this principle, one can reproduce many aspects of bacterial growth both in and out of a steady state, without having to consider other processes.
A major strength of this article is that the authors include many different E. coli datasets. From the figures presented, the model appears to agree well with the data. If the model can indeed predict bacterial growth out of a steady …
Reviewer #2 (Public Review):
The authors propose a proteome allocation model which includes a ribosomal and metabolic sector (and an additional sector in the case of nutrient upshift or downshift), and they consider the effect of tRNA charging on translation. It appears that the rate of protein generation via translation by ribosomes and the rate of tRNA charging via metabolic proteins are mutually maximized (the so-called "flux-parity regulation"). Based on this principle, one can reproduce many aspects of bacterial growth both in and out of a steady state, without having to consider other processes.
A major strength of this article is that the authors include many different E. coli datasets. From the figures presented, the model appears to agree well with the data. If the model can indeed predict bacterial growth out of a steady state, then it will be useful in understanding how tRNA charging affects the bacterial response to environmental fluctuations.
To improve the manuscript, units and typical values in E. coli should be provided in the main text as parameters are introduced, to give the reader some benchmark numbers and physical intuition. Furthermore, how proteins are assigned to metabolic, ribosomal, or other proteome sectors can be better explained in the main text, i.e. based on the dependence of their respective abundances on the growth rate. It would also help the reader to explicitly state which parameters are being adjusted and which are fixed (four are mentioned in Section 8 of the appendix but there are many others defined in the text). Finally, whether v_max (max metabolic rate) and tau (uncharged-to-charged tRNA ratio) take on physically reasonable values is not clear, e.g. values for v_max span 4 orders of magnitude. These are essential parameters to the model, and without a sense of how they compare to real values, it is difficult to judge the robustness of the results.
Some specific questions follow:
- Are there experimental data to verify the charging sensitivity parameter tau?
- Which molecules, other than charged tRNAs, are considered 'precursors', and are these neglected or accounted for in the model? For example, the other components of the ternary complex, e.g. GTP and EF-Tu, are not mentioned.
- What is the yield coefficient Y in Eqs. 10, 55, Fig. S2,A(iii)? No value appears in the text or supplemental tables.
- Why is the inactive fraction of ribosomes considered a puzzle? Bremer & Dennis and Metzl-Raz et al. have provided polysomal profiling data in E. coli and in S. cerevisiae, respectively. In E. coli it is ~85% but can be considerably lower in S. cerevisiae. Furthermore, it seems unphysical that 100% of ribosomes would be active at all times; it takes time for a ribosome to find and bind to mRNA.
- (p)ppGpp binds to molecules other than tRNAs, e.g. RNA polymerase. Shouldn't this be accounted for in, e.g., Eq. 3? -
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