A Novel Class of Predictive Microbial Grown Models: Implementation in an Individual-Based Framework

Standaert, A.R.; Poschet, Filip; Geeraerd, Annemie; Uylbak, Fons V.; Kreft, Jan-Ulrich; Impe, Jan F. Van

doi:10.1016/s1474-6670(17)32580-6

Cited by 4 publications

(2 citation statements)

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“…Population dynamics are not modeled explicitly, but emerge from the behavior of the individuals and their interactions with the environment and each other. As suggested above, in predictive microbiological IbMs, the microbial cell is taken as the individual modeling unit (Ginovart et al, 2002 ; Standaert et al, 2004 ; Dens et al, 2005 ; Prats et al, 2006 ; Verhulst et al, 2011 ; Ferrier et al, 2013 ; Tack et al, 2014 , 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

et al. 2017

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Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i) biofilm growth on a substratum surface and (ii) submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental pH. As a result, cellular growth in the submerged colony is limited to the colony periphery, implying a linear increase of the colony radius over time. MICRODIMS has been successfully used to reproduce complex dynamics of clustered microbial communities.

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Section: Introductionmentioning

confidence: 99%

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

et al. 2017

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“…Agent-based Models or Individual-based Models (IbMs), in which individuals interact dynamically with each other as structural elements in the model world, exemplify this view of simulation modelling [2]. This type of modelling has become the sine qua non for understanding complex systems and has been used successfully in microbiology [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

INDISIM-YEAST: an individual-based simulator on a website for experimenting and investigating diverse dynamics of yeast populations in liquid media

Gisbert

Cañadas

2008

J Ind Microbiol Biotechnol

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Simulation modelling can be used to capture and mimic real-world microbial systems that, unlike the real-world, can then be experimented upon as a new kind of experimental milieu. Individual-based models, in which individuals interact dynamically with each other as structural elements in the model world, exemplify this view of simulation modelling. These models are more difficult to analyze, understand and communicate than traditional analytical models. It is good practice to provide executable versions that perform simulation results. INDISIM-YEAST, developed to deal with yeast populations in liquid media, models the evolution of a set of yeasts by setting up rules of behavior for each individual cell according to its own biological regulations and characteristics. The aim of this work is to develop and present a website from which INDISIM-YEAST is accessible, and how to carry out yeast simulations to further the skills associated with the use of this individual-based simulator. A good and useful way to analyze this yeast simulator is to experiment and explore the manner in which it reacts to changes in parameter values, initial conditions or assumptions. The application results in a very versatile program that could be used in controlled simulation experiments via the Internet.

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Predictive Microbiology

2014

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A Novel Class of Predictive Microbial Grown Models: Implementation in an Individual-Based Framework

Cited by 4 publications

References 10 publications

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information

INDISIM-YEAST: an individual-based simulator on a website for experimenting and investigating diverse dynamics of yeast populations in liquid media

Predictive Microbiology

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