Sources, propagation and consequences of stochasticity in cellular growth

Thomas, Philipp; Terradot, Guillaume; Danos, Vincent; Weiße, Andrea Y.

doi:10.1038/s41467-018-06912-9

Cited by 96 publications

(82 citation statements)

References 68 publications

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“…Based on these findings, they proposed a phenomenological model of proteome allocation that extended the first growth law to these orthogonal types of growth rate modulation (Scott et al 2010). More mechanistic coarse-grained models predicting both the growth rate and the coarsegrained proteome as a function of growth conditions have also been proposed confirming and extending this finding (Marr 1991;Molenaar et al 2009;Weisse et al 2015;Maitra & Dill 2015;Bosdriesz et al 2015;Maitra & Dill 2016;Pandey & Jain 2016;Giordano et al 2016;Liao et al 2017; Thomas et al 2018;Sharma et al 2018;Pandey et al 2018).…”

Section: Introductionmentioning

confidence: 60%

A bacterial size law revealed by a coarse-grained model of cell physiology

Bertaux

Kügelgen

Marguerat

et al. 2016

Preprint

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Microbial cells are able to reach balanced exponential growth in a variety of environmental conditions. At the single-cell level, this implies coordination between the processes governing cellular growth, cell division and gene expression. A fundamental goal of microbiology is to build a quantitative and predictive understanding of how such coordination is achieved. It is known since long that the size of E. coli cells grown in media of different nutrient quality increases with growth rate. Recent data, however, shows that this relationship is of another nature when growth is modulated by other types of limitations, such as translation inhibition or forced expression of useless proteins. Here, we present a single-cell coarse-grained model of bacterial physiology that unifies previous efforts (i.e. the proteome allocation theory and the structural model of division control) into a simple and low-parametric model. We show that this model quantitatively explains the observed relationship between cell size and growth rate for various types of growth limitations with a single free parameter. In addition, predicted proteome fractions agree with observations, and when noise is included in the model, the recently discovered 'adder' principle of cell size homeostasis is correctly predicted. Thus, our minimalistic coarse-grained model successfully recapitulates the most fundamental aspects of bacterial physiology, and could serve as a guide for the development of more detailed and mechanistic whole-cell models.

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Section: Introductionmentioning

confidence: 60%

A bacterial size law revealed by a coarse-grained model of cell physiology

Bertaux

Kügelgen

Marguerat

et al. 2016

Preprint

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“…For example, metabolic rate or internal pH could lead to the alteration of cytoplasm properties, e.g. its fluidity 25,26 , and many intracellular processes, including DNA replication and cell division, are highly dependent on the growth rate 60,61 . It is, therefore, important to consider and characterise potential effects of the immobilisation method on physiology of the studied bacteria.…”

Section: Discussionmentioning

confidence: 99%

Comparison ofEscherichia colisurface attachment methods for single-cell,in vivomicroscopy

Wang¹,

Krasnopeeva

Lin³

et al. 2019

Preprint

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For in vivo, single-cell imaging bacterial cells are commonly immobilised via physical confinement or surface attachment. Different surface attachment methods have been used both for atomic force and optical microscopy (including super resolution), and some have been reported to affect bacterial physiology. However, a systematic comparison of the effects these attachment methods have on the bacterial physiology is lacking. Here we present such a comparison for bacterium Escherichia coli, and assess the growth rate, size and intracellular pH of cells growing attached to different, commonly used, surfaces. We demonstrate that E. coli grow at the same rate, length and internal pH on all the tested surfaces when in the same growth medium. The result suggests that tested attachment methods can be used interchangeably when studying E. coli physiology.

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“…Therefore, after substituting (19) and eq. (15) computed for i = 1 in the third constraint of (14), by suitably exploiting the saturating function (3), one has the following further relationship between x 0 and x p :…”

Section: First-order Moment Equationsmentioning

confidence: 99%

“…Fluctuations in growth rate are known to be responsible for phenotypic heterogeneity, although the mechanisms behind them are still matter of investigation [19], [20]. A large effort has been spent to investigate such phenotypic heterogeneity since it is supposed to be involved in cellular growth control and cancer initiation (see [12] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Noise propagation in metabolic pathways: the role of growth-mediated feedback

Borri

Palumbo

Singh

2020

Preprint

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Metabolic networks are known to deal with the chemical reactions responsible to fuel cellular activities with energy and carbon source and, as a matter of fact, to set the growth rate of the cell. To this end, feedback and regulatory networks play a crucial role to handle adaptation to external perturbations and internal noise. In this work, a cellular resource is assumed to be activated at the end of a metabolic pathway, by means of a cascade of transformations. Such a cascade is triggered by the catalytic action of an enzyme that promotes the first transformation. The final product is responsible for the cellular growth rate modulation. This mechanism acts in feedback at the enzymatic level, since the enzyme (as well as all species) is subject to dilution, with the dilution rate set by growth. Enzymatic production is modeled by the occurrence of noisy bursts: a Stochastic Hybrid System is exploited to model the network and to investigate how such noise propagates on growth fluctuations. A major biological finding is that, differently from other models of metabolic pathways disregarding growth-mediated feedback, fluctuations in enzyme levels do not produce only local effects, but propagate up to the final product (hence to the growth rate). Furthermore, the delay provided by the cascade length helps in reducing the impact of enzymatic noise on to growth fluctuations. Analytical results are supported by Monte Carlo simulations.

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Sources, propagation and consequences of stochasticity in cellular growth

Cited by 96 publications

References 68 publications

A bacterial size law revealed by a coarse-grained model of cell physiology

A bacterial size law revealed by a coarse-grained model of cell physiology

Comparison ofEscherichia colisurface attachment methods for single-cell,in vivomicroscopy

Noise propagation in metabolic pathways: the role of growth-mediated feedback

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