ArticleFEMS yeast research2025
Assessing methods for estimating microbial lag phase duration: a comparative analysis using Saccharomyces cerevisiae empirical and simulated data.
Article in FEMS yeast research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Plasma-Treated Water (PTW) Delaying the Lag Phase of Food-Related Pathogens.Microorganisms · 2026Article
- Fitness costs and persistence of plasmid-mediated cephalosporin resistance inFrontiers in microbiology · 2026Review
- Elucidation of the growth phenotypes of 82 strains from theFrontiers in bioengineering and biotechnology · 2026Article
- The diverse phenotypic and mutational landscape induced by fluoroquinolone treatment.mSystems · 2025Article
- Article
- Bioenergetics of aerobic and anaerobic growth ofFrontiers in microbiology · 2023Article
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Authors and funding
3 authors.
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Abstract
The lag phase is a temporary, nonreplicative period observed when a microbial population is introduced to a new, nutrient-rich environment. Although the theoretical concept of growth phases is clear, the practical application of methods for estimating lag lengths is often challenging. In fact, there are two distinct assumptions: (i) that cells do not divide at all during the lag phase or (ii) that they divide but at a suboptimal rate. Therefore, the choice of method should consider not only technical limitations but also consistency with the biological context. Here, we investigate the performance of the most common lag estimation methods, using empirical and simulated datasets. We apply different biological scenarios and simulate curves with varying parameters (i.e. growth rate, noise level, and frequency of measurements) to test their impact on the estimated lag phase duration. Our validation shows that infrequent measurements, low growth rate, longer lag phases, or higher level of noise in the measurements result in higher bias and higher variance of lag estimation. Additionally, in case of noisy data, the methods relying on model fitting perform best.
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