Stochasticity of cellular growth: sources, propagation and consequences

Thomas, Philipp; Terradot, Guillaume; Danos,; Weiße, AY

doi:10.1101/267658

Cited by 7 publications

(8 citation statements)

References 57 publications

(77 reference statements)

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“…Cells must continuously synthesize molecules to grow and divide. At a singlecell level, gene expression and cell size are coordinated but heterogeneous which can drive phenotypic variability and decision making in cell populations [1][2][3][4][5]. The interplay between these sources of cell-to-cell variability is not well understood since they have traditionally been studied separately.…”

Section: Introductionmentioning

confidence: 99%

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Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

Thomas

Shahrezaei

2021

J. R. Soc. Interface.

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The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis , a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

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Section: Introductionmentioning

confidence: 99%

“…polymerases or ribosomes that approximately double over the division cycle [3,16,17]. Cell size fluctuates in single cells, however, providing a source of extrinsic noise in reaction rates that can be identified via noise decompositions [18,19].…”

Section: Introductionmentioning

confidence: 99%

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

Thomas

Shahrezaei

2021

J. R. Soc. Interface.

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“…To aid interpretation, the cross-correlations can be decomposed into four noise modes, as indicated in equations (16) and (17).…”

Section: Expression-growth Correlations In a Two-protein Toy Modelmentioning

confidence: 99%

“…Yet, the composition of individual bacteria grown in a constant environment is known to fluctuate vigorously, in part due to the stochastic nature of gene expression [2][3][4][5]. Many experimental and theoretical studies have shed light on the origins, characteristics and consequences of this "noisy" expression [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Still, it remains unknown to what extent, and by what routes, noise in gene expression propagates through the cell and affects the rate of growth [5,18,19], which is often considered a proxy for its fitness [18,20].…”

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confidence: 99%

Noise propagation in an integrated model of bacterial gene expression and growth

Kleijn

Krah

Hermsen

2018

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In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple enzymes that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections with metabolic control analysis. We then present a many-protein model parameterized by previously measured protein abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

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“…The causes of this individuality have been proposed to include genetic variation, micro-environmental heterogeneity, regulatory differences, and the stochasticity of metabolic reactions that overlay a conserved genome 11–14 . This last possibility has garnered support from the repeated studies of clonal laboratory populations that present varied values for continuous microbial traits, referred to as ‘phenotypic noise’ 11, 12, 15–17 . The advantage, or consequence, of this variability may lie in bet-hedging or facilitating the development of microbial interactions within the population to allow for a division of labor 11, 14, 18, 19 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the stoichiometric trait distributions of cultured bacterial populations and uncultured microbial communities

Manzella

Geiss

Hall

2019

Preprint

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Originality StatementThe ecological stoichiometry of microbial biomass has most often focused on the ratio of the biologically-important elements carbon (C), nitrogen (N), and phosphorus (P) and has primarily been examined at a resolution where the contribution of the individual is masked by the reported population or community average. However, reporting population or community averages makes it difficult to assess phenotypic plasticity and stochasticity and mask important information required to understand both the drivers and implications of microbial biomass stoichiometry in nature. One way to assess the diversity of individual microbial phenotypes is through the use of single-cell techniques such as energy dispersive spectroscopy (EDS). EDS reports cellular quotas for the majority of elements composing microbial biomass including C, N, and P. In this study, by measuring individual cells within a microbial community or population, we describe for the first time the stoichiometry of microbial biomass as a distribution instead of an average. Exploration of stoichiometric trait distributions (as presented here) has the potential to improve our understanding of how nutrients interact with individual microorganisms to structure the elemental content of bacterial biomass and better describe how bacterial community biomass affects the ecosystems within which these organisms exist.SummaryTo assess the potential for EDS to describe the stoichiometric variance within populations and communities we measured the stoichiometric trait distribution of cultured freshwater bacterial populations under different resource conditions and compared them to natural microbial communities sampled from three lakes. Mean biomass C:N:P values obtained by EDS matched closely to those obtained by bulk measures using traditional analytical techniques for each freshwater isolate. However, we observed pronounced differences in the stoichiometric trait distributions of freshwater bacterial isolates compared to the stoichiometric trait distributions of natural communities. The stoichiometric trait distribution of the environmental isolates changed with P availability, growth phase, and genotype, with P availability having the strongest effect. The distribution of biomass ratios within each isolate growth experiment were the most constrained during stages of rapid growth and commonly had unimodal distributions. In contrast to the population distributions, the distribution of N:P and C:P for a similar number of cells from each of the mixed lake communities had narrower stoichiometric distributions and more commonly exhibited multiple modes.

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Stochasticity of cellular growth: sources, propagation and consequences

Cited by 7 publications

References 57 publications

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

Noise propagation in an integrated model of bacterial gene expression and growth

Evaluating the stoichiometric trait distributions of cultured bacterial populations and uncultured microbial communities

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