A universal trade-off between growth and lag in fluctuating environments

Basan, Markus; Honda, Tomoya; Christodoulou, Dimitris; Hörl, Manuel; Chang, Ying-Hsu; Leoncini, Emanuele; Mukherjee, Avik; Okano, Hiroyuki; Taylor, Brian; Silverman, Josh M.; Sánchez, Carlos Martínez; Williamson, James R.; Paulsson, Johan; Hwa, Terence; Sauer, Uwe

doi:10.1038/s41586-020-2505-4

Cited by 180 publications

(273 citation statements)

References 51 publications

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“…Our independent studies Kotte et al () and Basan et al () appear at first glance somewhat contradictory, because we observed very different phenomenologies, regarding population‐level heterogeneity and also the extent of observed lag times. While Kotte et al () found distinct subpopulations of cells, with some cells that grew immediately and others that never resumed growth, Basan et al () found a unimodal distribution of single cell lag times and the vast majority of cells eventually resumed growth. The resulting lag times from Kotte et al () were also much longer than those observed in Basan et al ().…”

contrasting

confidence: 78%

“…Independent of the physiological starting point, cells in both experiments had to overcome the problem of depletion of key glycolytic metabolites that result from the sudden flux reversal when switching from glycolytic to gluconeogenic growth. This imposes a trade‐off between rapid growth and adaptability that was also observed in Kotte et al (), caused by sequential flux limitation in gluconeogenesis (Basan et al , ). Imposing additional stress during the transition phase can drive the population into subpopulation heterogeneity, which can be observed as an increased length of the lag phase (Kotte et al , ).…”

mentioning

confidence: 77%

“…While Kotte et al () found distinct subpopulations of cells, with some cells that grew immediately and others that never resumed growth, Basan et al () found a unimodal distribution of single cell lag times and the vast majority of cells eventually resumed growth. The resulting lag times from Kotte et al () were also much longer than those observed in Basan et al ().…”

mentioning

confidence: 99%

“…These differences are largely the result of different experimental protocols for performing the metabolic shifts. In Basan et al (), bacteria were washed twice on a filter with warmed post‐shift medium and then gently resuspended with a pipette in warmed medium, a procedures accomplished in 2–3 min. Thus, cells were instantaneously exposed to the new substrate and lag phases were only a few hours, closely resembling the apparent phases of no growth during the diauxic shifts in batch experiments (Erickson et al , ; Basan et al , ).…”

mentioning

confidence: 99%

“…In Basan et al (), bacteria were washed twice on a filter with warmed post‐shift medium and then gently resuspended with a pipette in warmed medium, a procedures accomplished in 2–3 min. Thus, cells were instantaneously exposed to the new substrate and lag phases were only a few hours, closely resembling the apparent phases of no growth during the diauxic shifts in batch experiments (Erickson et al , ; Basan et al , ). In contrast, the protocol of Kotte et al () included several rounds of centrifugation and washing with ice‐cold, substrate‐free medium.…”

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confidence: 99%

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Implications of initial physiological conditions for bacterial adaptation to changing environments

Heinemann

Basan

Sauer

2020

Molecular Systems Biology

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confidence: 78%

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confidence: 77%

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confidence: 99%

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confidence: 99%

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Implications of initial physiological conditions for bacterial adaptation to changing environments

Heinemann

Basan

Sauer

2020

Molecular Systems Biology

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Trade‐offs between the instantaneous growth rate and long‐term fitness: Consequences for microbial physiology and predictive computational models

Bruggeman¹,

Teusink²,

Steuer

2023

BioEssays

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Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade‐offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long‐term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

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Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth

Bate,

Amekan,

Pushkin

et al. 2023

Bull Math Biol

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Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose–lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

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A universal trade-off between growth and lag in fluctuating environments

Cited by 180 publications

References 51 publications

Implications of initial physiological conditions for bacterial adaptation to changing environments

Implications of initial physiological conditions for bacterial adaptation to changing environments

Trade‐offs between the instantaneous growth rate and long‐term fitness: Consequences for microbial physiology and predictive computational models

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth

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