ReviewFrontiers in microbiology2017
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling.
Review in Frontiers in microbiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Gentamicin and d-serine selective pressure to improve the homogeneity of cells expressing target proteins.Synthetic and systems biotechnology · 2027Article
- Submicron infrared spectroscopy assessment of single-cell phenotypic diversity in microbial lipid production.Microbial cell factories · 2025Article
- Whole Cells of Microorganisms-A Powerful Bioanalytical Tool for Measuring Integral Parameters of Pollution: A Review.Biosensors · 2025Review
- Photodynamic inactivation and its effects on the heterogeneity of bacterial resistance.Scientific reports · 2024Article
- Isolation and characterization of Saccharomyces cerevisiae mutants with increased cell wall chitin using fluorescence-activated cell sorting.FEMS yeast research · 2024Article
- Article
- Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC.Nature microbiology · 2023Article
- Diverse mechanisms of bioproduction heterogeneity in fermentation and their control strategies.Journal of industrial microbiology & biotechnology · 2023Review
- Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information.iScience · 2022Article
- Advances in automated real-time flow cytometry for monitoring of bioreactor processes.Engineering in life sciences · 2022Review
- The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art.Foods (Basel, Switzerland) · 2021Review
- Microbial single-cell omics: the crux of the matter.Applied microbiology and biotechnology · 2020Review
- INDISIM-Denitrification, an individual-based model for study the denitrification process.Journal of industrial microbiology & biotechnology · 2020Article
- Toward Engineering Biosystems With Emergent Collective Functions.Frontiers in bioengineering and biotechnology · 2020Article
- Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities.Applied and environmental microbiology · 2019Article
- Maximum entropy and population heterogeneity in continuous cell cultures.PLoS computational biology · 2019Article
- Quantitative Flow Cytometry to Understand Population Heterogeneity in Response to Changes in Substrate Availability inFrontiers in bioengineering and biotechnology · 2019Article
- Defining Cell Identity with Single-Cell Omics.Proteomics · 2018Review
- Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling.Frontiers in microbiology · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.