Literature DB >> 27412586

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world.

Konstantinos P Koutsoumanis1, Zafiro Aspridou2.   

Abstract

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods. Copyright Â
© 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Complexity; Gene expression; Noise; Predictive Microbiology; Single cell; Time lapse fluorescence microscopy

Mesh:

Year:  2016        PMID: 27412586     DOI: 10.1016/j.ijfoodmicro.2016.06.021

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  5 in total

1.  Potential for Microbially Mediated Natural Attenuation of Diluted Bitumen on the Coast of British Columbia (Canada).

Authors:  Lars Schreiber; Nathalie Fortin; Julien Tremblay; Jessica Wasserscheid; Miria Elias; Jennifer Mason; Sylvie Sanschagrin; Susan Cobanli; Thomas King; Kenneth Lee; Charles W Greer
Journal:  Appl Environ Microbiol       Date:  2019-05-02       Impact factor: 4.792

2.  Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry.

Authors:  Míriam R García; José A Vázquez; Isabel G Teixeira; Antonio A Alonso
Journal:  Front Microbiol       Date:  2018-01-05       Impact factor: 5.640

3.  Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes.

Authors:  Patrick Murigu Kamau Njage; Pimlapas Leekitcharoenphon; Lisbeth Truelstrup Hansen; Rene S Hendriksen; Christel Faes; Marc Aerts; Tine Hald
Journal:  Microorganisms       Date:  2020-11-11

4.  Evaluation of water-assisted UV-C light and its additive effect with peracetic acid for the inactivation of Listeria monocytogenes, Salmonella enterica and murine norovirus on whole and fresh-cut strawberries during shelf-life.

Authors:  Jordi Ortiz-Solà; Antonio Valero; Maribel Abadias; Iolanda Nicolau-Lapeña; Inmaculada Viñas
Journal:  J Sci Food Agric       Date:  2022-05-10       Impact factor: 4.125

5.  The COM-Poisson Process for Stochastic Modeling of Osmotic Inactivation Dynamics of Listeria monocytogenes.

Authors:  Pierluigi Polese; Manuela Del Torre; Mara Lucia Stecchini
Journal:  Front Microbiol       Date:  2021-07-09       Impact factor: 5.640

  5 in total

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