Unification of cell division control strategies through continuous rate models

Nieto, César; Arias-Castro, Juan Carlos; Sánchez, Carlos Martínez; Vargas-García, César Augusto; Pedraza, Juan Manuel

doi:10.1103/physreve.101.022401

Cited by 44 publications

(140 citation statements)

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“…A linear growth regime is obtained by setting a = 0, while, for a = 1, cells grow exponentially. A general a > 0 can be used to explore other division mechanisms [12].…”

Section: Methodsmentioning

confidence: 99%

“…The Stochastic Simulation Algorithm is a numerical approach for generating sample paths of a continuous-time Markov process whose probability distribution evolves through transition rates between its states. For each cell i in the population, the algorithm first computes P i (t), the probability that cell i divides at time t, by integrating the division rate δ over time (see explanation in Supplementary Information) [12]. Then, these P i (t) are compared to uniformly generated random numbers r i , and the next division is implemented only for cell j, with P j > r j .…”

Section: Methodsmentioning

confidence: 99%

“…For each scenario, the parameter values for g and k shown in table I were chosen so that the mean doubling time τ satisfies τ = 1, and s b , defined the mean size at birth, satisfies s b = 1. This s b , introduced in previous studies [12] (see Supplementary Information), corresponds to the size that is perfectly doubled when division occurs. Importantly, specific values for both τ and s b can be determined experimentally, so that experimental data, once normalized to these values, can be compared to model predictions.…”

Section: A Biological Scenariosmentioning

confidence: 99%

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Cell size statistics in cell lineages and population snapshots with different growth regimes and division strategies

Totis

Nieto

Vargas-García

et al. 2020

Preprint

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Growing populations of bacteria control their growth and division reaching narrow distributions of cellsizes. In this paper we explored how different combinations of growth regimes and division mechanisms lead to different cell-size statistics in these populations. Deterministic and stochastic modeling were used to describe the size distribution of a population of cells that is observed from two different perspectives: as single cell lineages, i.e. random paths in the lineage tree, or as snapshots, at given times, of a population in which all descendants of a single ancestor cell are observed. Our time-dependent approaches allowed us to obtain both the transient dynamics and the steady state values for the main statistical moments of the cell-size distribution. Also, we established mathematical relationships among the statistics in the two considered perspectives, thus improving our knowledge of how cells control their growth and proliferation.

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“…A linear growth regime is obtained by setting a = 0, while, for a = 1, cells grow exponentially. A general a > 0 can be used to explore other division mechanisms [12].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Biological Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Cell size statistics in cell lineages and population snapshots with different growth regimes and division strategies

Totis

Nieto

Vargas-García

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…These observations reflected questions raised about bacterial size regulation, which has been a subject of intense debate (16)(17)(18). Several reports using single-cell quantitative approach have demonstrated that several bacterial species grow a fixed cell volume per cell cycle during steady-state growth, regardless of cell size at birth (19)(20)(21)(22)(23). The addition of a constant cell volume, described by the adder model (19)(20)(21)(22)(23), results in less cell size variation over cell cycles.…”

Section: Growth and Division Of Single Cell Of Z Mobilis Strain Zm6mentioning

confidence: 96%

“…Several reports using single-cell quantitative approach have demonstrated that several bacterial species grow a fixed cell volume per cell cycle during steady-state growth, regardless of cell size at birth (19)(20)(21)(22)(23). The addition of a constant cell volume, described by the adder model (19)(20)(21)(22)(23), results in less cell size variation over cell cycles. However, the rule was not applied in E. coli under slow growth condition (22,24) or in Mycobacterium species that grow asymmetrically by an apical growth (25).…”

Section: Growth and Division Of Single Cell Of Z Mobilis Strain Zm6mentioning

confidence: 99%

Cell biological studies of ethanologenic bacteriumZymomonas mobilis

Fuchino

Chan

Hwang

et al. 2020

Preprint

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Alphaproteobacterium Zymomonas mobilis exhibits extreme ethanologenic physiology, making this species a promising biofuel producer. Numerous studies have investigated its biology relevant to industrial applications and mostly at the population level. However, the organization of single cells in this industrially important, polyploid species has been largely uncharacterized.In the present study, we characterized basic cellular behaviour of Z. mobilis strain Zm6 at a single cell level. We observed that growing Z. mobilis cells often divided at non mid-cell position, which contributed to variant cell size at birth. Yet, the cell size variance was regulated by a modulation of cell cycle span, mediated by a correlation of bacterial tubulin homologue FtsZ-ring accumulation with cell growth. The Z. mobilis culture also exhibited heterogeneous cellular DNA contents among individual cells, which might have been caused by asynchronous replication of chromosome that was not coordinated to cell growth. Furthermore, slightly angled divisions might have rendered temporary curvatures of attached Z. mobilis cells. Overall, the presented study uncovered a novel bacterial cell organization in Z. mobilis, the metabolism of which is not favoured for biosynthesis to build biomass.ImportanceWith increasing environmental concerns about the exhausting use of fossil fuels, a development of sustainable biofuel production platform has been attracting significant public attention. Ethanologenic Z. mobilis species are endowed with an efficient ethanol-fermentation capacity that surpass, in several aspects, that of the baker’s yeast Saccharomyces cerevisiae, the most used microorganism for ethanol productions. For a development of Z. mobilis culture-based biorefinery, an investigation of its uncharacterized cell biology is important, because bacterial cellular organization and metabolism are closely associated with each other in a single cell compartment.In addition, the current work highlights that polyploid bacterium Z. mobilis exhibits a distinctive mode of bacterial cell organization, reflecting its unique metabolism that do not prioritize incorporation of nutrients to cell growth. Thus, another significance of presented work is to advance our general understanding in the diversity of bacterial cell architecture.

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