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Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 125769

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Special Issue Editor

Dr. Hyun-Seob Song

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Guest Editor

Department of Biological Systems Engineering, Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, 1400 R St, Lincoln, NE 68588, USA
Interests: microbial community modeling; metabolic modeling; metabolic network analysis; system optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interest in engineering microbial communities for application in biotechnology and biomedical science has rapidly grown over the last decade. The design and control of microbial communities still remains a grand challenge, particularly due to the complexity of interspecies interactions that require mathematical modeling and computational analysis as essential tools.

This Special Issue calls for contributions across a broad range of areas that address recent computational and modeling developments for predicting species interactions and community dynamics and functions. Modeling frameworks of interest include metabolic network analysis, flux balance analysis, trait-based modeling, Lotka-Volterra modeling, evolutionary game theory, the cybernetic approach, functional gene-based modeling, thermodynamically-based modeling, individual-based modeling, integrative multiscale modeling, and other relevant approaches. We also welcome papers on data-driven inference of species interaction networks or gene co-expression networks in microbial communities.

Dr. Hyun-Seob Song
Guest Editor

Review related to the special issue： Song, H.-S.; Cannon, W.R.; Beliaev, A.S.; Konopka, A. Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes 2014, 2, 711-752. (https://www.mdpi.com/2227-9717/2/4/711)

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

microbial community dynamics
microbial interaction networks
mathematical modeling
data-driven inference

Published Papers (14 papers)

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Editorial

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3 pages, 157 KiB

Open AccessEditorial

Special Issue: Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics

by Hyun-Seob Song

Processes 2018, 6(5), 41; https://doi.org/10.3390/pr6050041 - 24 Apr 2018

Cited by 1 | Viewed by 3489

Abstract

n/a Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

Research

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14 pages, 1148 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi

by St. Elmo Wilken, Mohan Saxena, Linda R. Petzold and Michelle A. O’Malley

Processes 2018, 6(1), 7; https://doi.org/10.3390/pr6010007 - 15 Jan 2018

Cited by 15 | Viewed by 6840

Abstract

Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitability. Microbial growth simulations reveal that the fungus Anaeromyces robustus is most suited to pair with either the bacterium Clostridia ljungdahlii or the methanogen Methanosarcina barkeri—both organisms also found in the rumen microbiome. By capitalizing on simulations to screen six alternative organisms, valuable experimental time is saved towards identifying stable consortium members. This approach is also readily generalizable to larger systems and allows one to rationally select partner microbes for formation of stable consortia with non-model microbes like anaerobic fungi. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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21 pages, 726 KiB

Open AccessArticle

Individual-Based Modelling of Invasion in Bioaugmented Sand Filter Communities

by Aisling J. Daly, Jan M. Baetens, Johanna Vandermaesen, Nico Boon, Dirk Springael and Bernard De Baets

Processes 2018, 6(1), 2; https://doi.org/10.3390/pr6010002 - 01 Jan 2018

Cited by 6 | Viewed by 5132

Abstract

Using experimental data obtained from in vitro bioaugmentation studies of a sand filter community of 13 bacterial species, we develop an individual-based model representing the in silico counterpart of this synthetic microbial community. We assess the inter-species interactions, first by identifying strain identity effects in the data then by synthesizing these effects into a competition structure for our model. Pairwise competition outcomes are determined based on interaction effects in terms of functionality. We also consider non-deterministic competition, where winning probabilities are assigned based on the relative intrinsic competitiveness of each strain. Our model is able to reproduce the key qualitative dynamics observed in in vitro experiments with similar synthetic sand filter communities. Simulation outcomes can be explained based on the underlying competition structures and the resulting spatial dynamics. Our results highlight the importance of community diversity and in particular evenness in stabilizing the community dynamics, allowing us to study the establishment and development of these communities, and thereby illustrate the potential of the individual-based modelling approach for addressing microbial ecological theories related to synthetic communities. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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2724 KiB

Open AccessArticle

Dynamics of the Bacterial Community Associated with Phaeodactylum tricornutum Cultures

by Fiona Wanjiku Moejes, Antonella Succurro, Ovidiu Popa, Julie Maguire and Oliver Ebenhöh

Processes 2017, 5(4), 77; https://doi.org/10.3390/pr5040077 - 07 Dec 2017

Cited by 16 | Viewed by 8553

Abstract

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating that our hypotheses regarding the molecular interactions are in agreement with experimental data. Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to develop quantitative and predictive mathematical models, which may guide experimental efforts to synthetically construct and monitor stable communities required for robust upscaling strategies. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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2848 KiB

Open AccessArticle

An Integrated Mathematical Model of Microbial Fuel Cell Processes: Bioelectrochemical and Microbiologic Aspects

by Andrea G. Capodaglio, Daniele Cecconet and Daniele Molognoni

Processes 2017, 5(4), 73; https://doi.org/10.3390/pr5040073 - 20 Nov 2017

Cited by 34 | Viewed by 7256

Abstract

Microbial Fuel Cells (MFCs) represent a still relatively new technology for liquid organic waste treatment and simultaneous recovery of energy and resources. Although the technology is quite appealing due its potential benefits, its practical application is still hampered by several drawbacks, such as systems instability (especially when attempting to scale-up reactors from laboratory prototypes), internally competing microbial reactions, and limited power generation. This paper is an attempt to address some of the issues related to MFC application in wastewater treatment with a simulation model. Reactor configuration, operational schemes, electrochemical and microbiological characterization, optimization methods and modelling strategies were reviewed and have been included in a mathematical simulation model written with a multidisciplinary, multi-perspective approach, considering the possibility of feeding real substrates to an MFC system while dealing with a complex microbiological population. The conclusions drawn herein can be of practical interest for all MFC researchers dealing with domestic or industrial wastewater treatment. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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3848 KiB

Open AccessFeature PaperArticle

Dispersal-Based Microbial Community Assembly Decreases Biogeochemical Function

by Emily B. Graham and James C. Stegen

Processes 2017, 5(4), 65; https://doi.org/10.3390/pr5040065 - 01 Nov 2017

Cited by 91 | Viewed by 9998

Abstract

Ecological mechanisms influence relationships among microbial communities, which in turn impact biogeochemistry. In particular, microbial communities are assembled by deterministic (e.g., selection) and stochastic (e.g., dispersal) processes, and the relative balance of these two process types is hypothesized to alter the influence of microbial communities over biogeochemical function. We used an ecological simulation model to evaluate this hypothesis, defining biogeochemical function generically to represent any biogeochemical reaction of interest. We assembled receiving communities under different levels of dispersal from a source community that was assembled purely by selection. The dispersal scenarios ranged from no dispersal (i.e., selection-only) to dispersal rates high enough to overwhelm selection (i.e., homogenizing dispersal). We used an aggregate measure of community fitness to infer a given community’s biogeochemical function relative to other communities. We also used ecological null models to further link the relative influence of deterministic assembly to function. We found that increasing rates of dispersal decrease biogeochemical function by increasing the proportion of maladapted taxa in a local community. Niche breadth was also a key determinant of biogeochemical function, suggesting a tradeoff between the function of generalist and specialist species. Finally, we show that microbial assembly processes exert greater influence over biogeochemical function when there is variation in the relative contributions of dispersal and selection among communities. Taken together, our results highlight the influence of spatial processes on biogeochemical function and indicate the need to account for such effects in models that aim to predict biogeochemical function under future environmental scenarios. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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6397 KiB

Open AccessArticle

Stoichiometric Network Analysis of Cyanobacterial Acclimation to Photosynthesis-Associated Stresses Identifies Heterotrophic Niches

by Ashley E. Beck, Hans C. Bernstein and Ross P. Carlson

Processes 2017, 5(2), 32; https://doi.org/10.3390/pr5020032 - 19 Jun 2017

Cited by 13 | Viewed by 8577

Abstract

Metabolic acclimation to photosynthesis-associated stresses was examined in the thermophilic cyanobacterium Thermosynechococcus elongatus BP-1 using integrated computational and photobioreactor analyses. A genome-enabled metabolic model, complete with measured biomass composition, was analyzed using ecological resource allocation theory to predict and interpret metabolic acclimation to irradiance, O₂, and nutrient stresses. Reduced growth efficiency, shifts in photosystem utilization, changes in photorespiration strategies, and differing byproduct secretion patterns were predicted to occur along culturing stress gradients. These predictions were compared with photobioreactor physiological data and previously published transcriptomic data and found to be highly consistent with observations, providing a systems-based rationale for the culture phenotypes. The analysis also indicated that cyanobacterial stress acclimation strategies created niches for heterotrophic organisms and that heterotrophic activity could enhance cyanobacterial stress tolerance by removing inhibitory metabolic byproducts. This study provides mechanistic insight into stress acclimation strategies in photoautotrophs and establishes a framework for predicting, designing, and engineering both axenic and photoautotrophic-heterotrophic systems as a function of controllable parameters. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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2300 KiB

Open AccessFeature PaperArticle

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

by Michael A. Henson and Poonam Phalak

Processes 2017, 5(1), 13; https://doi.org/10.3390/pr5010013 - 18 Mar 2017

Cited by 25 | Viewed by 9406

Abstract

The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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858 KiB

Open AccessEditor’s ChoiceArticle

Photorespiration and Rate Synchronization in a Phototroph-Heterotroph Microbial Consortium

by Fadoua El Moustaid, Ross P. Carlson, Federica Villa and Isaac Klapper

Processes 2017, 5(1), 11; https://doi.org/10.3390/pr5010011 - 02 Mar 2017

Cited by 5 | Viewed by 7181

Abstract

The process of oxygenic photosynthesis is robust and ubiquitous, relying centrally on input of light, carbon dioxide, and water, which in many environments are all abundantly available, and from which are produced, principally, oxygen and reduced organic carbon. However, photosynthetic machinery can be conflicted by the simultaneous presence of carbon dioxide and oxygen through a process sometimes called photorespiration. We present here a model of phototrophy, including competition for RuBisCO binding sites between oxygen and carbon dioxide, in a chemostat-based microbial population. The model connects to the idea of metabolic pathways to track carbon and degree of reduction through the system. We find decomposition of kinetics into elementary flux modes a mathematically natural way to study synchronization of mismatched rates of photon input and chemostat turnover. In the single species case, though total biomass is reduced by photorespiration, protection from excess light exposures and its consequences (oxidative and redox stress) may result. We also find the possibility that a consortium of phototrophs with heterotrophs can recycle photorespiration byproduct into increased biomass at the cost of increase in oxidative product (here, oxygen). Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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393 KiB

Open AccessArticle

Modeling Biofilms: From Genes to Communities

by Tianyu Zhang

Processes 2017, 5(1), 5; https://doi.org/10.3390/pr5010005 - 23 Jan 2017

Cited by 8 | Viewed by 6933

Abstract

Biofilms are spatially-structured communities of different microbes, which have a huge impact on both ecosystems and human life. Mathematical models are powerful tools for understanding the function and evolution of biofilms as diverse communities. In this article, we give a review of some recently-developed models focusing on the interactions of different species within a biofilm, the evolution of biofilm due to genetic and environmental causes and factors that affect the structure of a biofilm. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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2129 KiB

Open AccessArticle

Species Coexistence in Nitrifying Chemostats: A Model of Microbial Interactions

by Maxime Dumont, Jean-Jacques Godon and Jérôme Harmand

Processes 2016, 4(4), 51; https://doi.org/10.3390/pr4040051 - 14 Dec 2016

Cited by 5 | Viewed by 5509

Abstract

In a previous study, the two nitrifying functions (ammonia oxidizing bacteria (AOB) or nitrite-oxidizing bacteria (NOB)) of a nitrification reactor—operated continuously over 525 days with varying inputs—were assigned using a mathematical modeling approach together with the monitoring of bacterial phylotypes. Based on these theoretical identifications, we develop here a chemostat model that does not explicitly include only the resources’ dynamics (different forms of soluble nitrogen) but also explicitly takes into account microbial inter- and intra-species interactions for the four dominant phylotypes detected in the chemostat. A comparison of the models obtained with and without interactions has shown that such interactions permit the coexistence of two competing ammonium-oxidizing bacteria and two competing nitrite-oxidizing bacteria in competition for ammonium and nitrite, respectively. These interactions are analyzed and discussed. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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Review

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9136 KiB

Open AccessFeature PaperReview

Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

by Hyun-Seob Song, William R. Cannon, Alexander S. Beliaev and Allan Konopka

Processes 2014, 2(4), 711-752; https://doi.org/10.3390/pr2040711 - 17 Oct 2014

Cited by 123 | Viewed by 31186 | Correction

Abstract

Microorganisms in nature form diverse communities that dynamically change in structure and function in response to environmental variations. As a complex adaptive system, microbial communities show higher-order properties that are not present in individual microbes, but arise from their interactions. Predictive mathematical models not only help to understand the underlying principles of the dynamics and emergent properties of natural and synthetic microbial communities, but also provide key knowledge required for engineering them. In this article, we provide an overview of mathematical tools that include not only current mainstream approaches, but also less traditional approaches that, in our opinion, can be potentially useful. We discuss a broad range of methods ranging from low-resolution supra-organismal to high-resolution individual-based modeling. Particularly, we highlight the integrative approaches that synergistically combine disparate methods. In conclusion, we provide our outlook for the key aspects that should be further developed to move microbial community modeling towards greater predictive power. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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Other

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265 KiB

Open AccessFeature PaperOpinion

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

by Marco Zaccaria, Sandra Dedrick and Babak Momeni

Processes 2017, 5(4), 53; https://doi.org/10.3390/pr5040053 - 25 Sep 2017

Cited by 16 | Viewed by 8922

Abstract

With our growing understanding of the impact of microbial communities, understanding how such communities function has become a priority. The influence of microbial communities is widespread. Human-associated microbiota impacts health, environmental microbes determine ecosystem sustainability, and microbe-driven industrial processes are expanding. This broad range of applications has led to a wide range of approaches to analyze and describe microbial communities. In particular, theoretical work based on mathematical modeling has been a steady source of inspiration for explaining and predicting microbial community processes. Here, we survey some of the modeling approaches used in different contexts. We promote classifying different approaches using a unified platform, and encourage cataloging the findings in a database. We believe that the synergy emerging from a coherent collection facilitates a better understanding of important processes that determine microbial community functions. We emphasize the importance of close collaboration between theoreticians and experimentalists in formulating, classifying, and improving models of microbial communities. Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

101 KiB

Open AccessCorrection

Song, H.-S., et al. Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes 2014, 2, 711–752

by Hyun-Seob Song, William R. Cannon, Alexander S. Beliaev and Allan Konopka

Processes 2015, 3(3), 699-700; https://doi.org/10.3390/pr3030699 - 14 Sep 2015

Cited by 4 | Viewed by 5235

Abstract

The authors wish to make the following correction to this paper [1]. Due to mislabeling, replace: [...] Full article

(This article belongs to the Special Issue Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics)

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