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Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities

In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered...

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Autores principales: Schroeder, Joanna L, Lunn, Mary, Pinto, Ameet J, Raskin, Lutgarde, Sloan, William T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372544/
https://www.ncbi.nlm.nih.gov/pubmed/25803866
http://dx.doi.org/10.1371/journal.pone.0117221
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author Schroeder, Joanna L
Lunn, Mary
Pinto, Ameet J
Raskin, Lutgarde
Sloan, William T
author_facet Schroeder, Joanna L
Lunn, Mary
Pinto, Ameet J
Raskin, Lutgarde
Sloan, William T
author_sort Schroeder, Joanna L
collection PubMed
description In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported.
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spelling pubmed-43725442015-04-04 Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities Schroeder, Joanna L Lunn, Mary Pinto, Ameet J Raskin, Lutgarde Sloan, William T PLoS One Research Article In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported. Public Library of Science 2015-03-24 /pmc/articles/PMC4372544/ /pubmed/25803866 http://dx.doi.org/10.1371/journal.pone.0117221 Text en © 2015 Schroeder et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Schroeder, Joanna L
Lunn, Mary
Pinto, Ameet J
Raskin, Lutgarde
Sloan, William T
Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title_full Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title_fullStr Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title_full_unstemmed Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title_short Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities
title_sort probabilistic models to describe the dynamics of migrating microbial communities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372544/
https://www.ncbi.nlm.nih.gov/pubmed/25803866
http://dx.doi.org/10.1371/journal.pone.0117221
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