1
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Pasqualini J, Facchin S, Rinaldo A, Maritan A, Savarino E, Suweis S. Emergent ecological patterns and modelling of gut microbiomes in health and in disease. PLoS Comput Biol 2024; 20:e1012482. [PMID: 39331660 DOI: 10.1371/journal.pcbi.1012482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 09/11/2024] [Indexed: 09/29/2024] Open
Abstract
Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiome's influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been recently proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, and addressing the consequences of sampling effects and statistical errors on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different statistical generative models, which incorporate sampling and population dynamics, to describe such patterns and distinguish which are expected by chance, versus those that are potentially informative about disease states or other biological drivers. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics.
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Affiliation(s)
- Jacopo Pasqualini
- Dipartimento di Fisica "G. Galilei" e INFN sezione di Padova, University of Padova, Padova, Italy
| | - Sonia Facchin
- Dipartimento di Scienze Chirurgiche, Oncologiche e Gastroenterologiche (DiSCOG), University of Padova, Padova, Italy
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile, Edile e Ambientale (ICEA), University of Padova, Padova, Italy
- Laboratory of Ecohydrology, École Polytechnique Fédérale Lausanne, Lausanne, Switzerland
| | - Amos Maritan
- Dipartimento di Fisica "G. Galilei" e INFN sezione di Padova, University of Padova, Padova, Italy
| | - Edoardo Savarino
- Dipartimento di Scienze Chirurgiche, Oncologiche e Gastroenterologiche (DiSCOG), University of Padova, Padova, Italy
| | - Samir Suweis
- Dipartimento di Fisica "G. Galilei" e INFN sezione di Padova, University of Padova, Padova, Italy
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2
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Lemos-Costa P, Miller ZR, Allesina S. Phylogeny structures species' interactions in experimental ecological communities. Ecol Lett 2024; 27:e14490. [PMID: 39152685 DOI: 10.1111/ele.14490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 06/24/2024] [Accepted: 07/11/2024] [Indexed: 08/19/2024]
Abstract
Species' traits and interactions are products of evolutionary history. Despite the long-standing hypothesis that closely related species possess similar traits, and thus experience stronger competition, measuring the effect of evolutionary history on the ecology of natural communities remains challenging. We propose a novel framework to test whether phylogeny influences patterns of coexistence and abundance of species assemblages. In our approach, phylogenetic trees are used to parameterize species' interactions, which in turn determine the abundance of species in a given assemblage. We use likelihoods to score models parameterized with a given phylogeny, and contrast them with models built using random trees, allowing us to test whether phylogenetic information helps to predict species' abundances. Our statistical framework reveals that interactions are indeed structured by phylogeny in a large set of experimental plant communities. Our results confirm that evolutionary history can help predict, and potentially manage or conserve, the structure and function of complex ecological communities.
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Affiliation(s)
- Paula Lemos-Costa
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Zachary R Miller
- Department of Earth and Planetary Sciences, Yale University, New Haven, Connecticut, USA
| | - Stefano Allesina
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, USA
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3
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Mallmin E, Traulsen A, De Monte S. Chaotic turnover of rare and abundant species in a strongly interacting model community. Proc Natl Acad Sci U S A 2024; 121:e2312822121. [PMID: 38437535 PMCID: PMC10945849 DOI: 10.1073/pnas.2312822121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/02/2024] [Indexed: 03/06/2024] Open
Abstract
The composition of ecological communities varies not only between different locations but also in time. Understanding the fundamental processes that drive species toward rarity or abundance is crucial to assessing ecosystem resilience and adaptation to changing environmental conditions. In plankton communities in particular, large temporal fluctuations in species abundances have been associated with chaotic dynamics. On the other hand, microbial diversity is overwhelmingly sustained by a "rare biosphere" of species with very low abundances. We consider here the possibility that interactions within a species-rich community can relate both phenomena. We use a Lotka-Volterra model with weak immigration and strong, disordered, and mostly competitive interactions between hundreds of species to bridge single-species temporal fluctuations and abundance distribution patterns. We highlight a generic chaotic regime where a few species at a time achieve dominance but are continuously overturned by the invasion of formerly rare species. We derive a focal-species model that captures the intermittent boom-and-bust dynamics that every species undergoes. Although species cannot be treated as effectively uncorrelated in their abundances, the community's effect on a focal species can nonetheless be described by a time-correlated noise characterized by a few effective parameters that can be estimated from time series. The model predicts a nonunitary exponent of the power-law abundance decay, which varies weakly with ecological parameters, consistent with observation in marine protist communities. The chaotic turnover regime is thus poised to capture relevant ecological features of species-rich microbial communities.
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Affiliation(s)
- Emil Mallmin
- Max Planck Institute for Evolutionary Biology, Department of Theoretical Biology, Plön24306, Germany
| | - Arne Traulsen
- Max Planck Institute for Evolutionary Biology, Department of Theoretical Biology, Plön24306, Germany
| | - Silvia De Monte
- Max Planck Institute for Evolutionary Biology, Department of Theoretical Biology, Plön24306, Germany
- Institut de Biologie de l’ENS, Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université Paris Science & Lettres, Paris75005, France
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4
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Ishizawa H, Tashiro Y, Inoue D, Ike M, Futamata H. Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure. Proc Natl Acad Sci U S A 2024; 121:e2312396121. [PMID: 38315845 PMCID: PMC10873592 DOI: 10.1073/pnas.2312396121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/20/2023] [Indexed: 02/07/2024] Open
Abstract
Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.
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Affiliation(s)
- Hidehiro Ishizawa
- Department of Applied Chemistry, Graduate School of Engineering, University of Hyogo, Himeji671-2280, Japan
- Research Institute of Green Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
| | - Yosuke Tashiro
- Department of Engineering, Graduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
- Graduate School of Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
| | - Daisuke Inoue
- Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Suita565-0821, Japan
| | - Michihiko Ike
- Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Suita565-0821, Japan
| | - Hiroyuki Futamata
- Research Institute of Green Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
- Department of Engineering, Graduate School of Integrated Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
- Graduate School of Science and Technology, Shizuoka University, Hamamatsu432-8561, Japan
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5
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Baig Y, Ma HR, Xu H, You L. Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics. Nat Commun 2023; 14:7937. [PMID: 38049401 PMCID: PMC10696002 DOI: 10.1038/s41467-023-43455-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 11/09/2023] [Indexed: 12/06/2023] Open
Abstract
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.
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Affiliation(s)
- Yasa Baig
- Department of Physics, Duke University, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Helena R Ma
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA
| | - Helen Xu
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
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Lässig M, Mustonen V, Nourmohammad A. Steering and controlling evolution - from bioengineering to fighting pathogens. Nat Rev Genet 2023; 24:851-867. [PMID: 37400577 PMCID: PMC11137064 DOI: 10.1038/s41576-023-00623-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
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Affiliation(s)
- Michael Lässig
- Institute for Biological Physics, University of Cologne, Cologne, Germany.
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
| | - Armita Nourmohammad
- Department of Physics, University of Washington, Seattle, WA, USA.
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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7
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Arya S, George AB, O’Dwyer JP. Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes. Proc Natl Acad Sci U S A 2023; 120:e2307313120. [PMID: 37991947 PMCID: PMC10691334 DOI: 10.1073/pnas.2307313120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/16/2023] [Indexed: 11/24/2023] Open
Abstract
Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.
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Affiliation(s)
- Shreya Arya
- Department of Physics, University of Illinois, Urbana-Champaign, Urbana, IL61801
| | - Ashish B. George
- Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL61801
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA0214
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL61801
| | - James P. O’Dwyer
- Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL61801
- Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL61801
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8
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Brunner JD, Gallegos-Graves LA, Kroeger ME. Inferring microbial interactions with their environment from genomic and metagenomic data. PLoS Comput Biol 2023; 19:e1011661. [PMID: 37956203 PMCID: PMC10681327 DOI: 10.1371/journal.pcbi.1011661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/27/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.
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Affiliation(s)
- James D. Brunner
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Marie E. Kroeger
- Biosciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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9
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Weiss AS, Niedermeier LS, von Strempel A, Burrichter AG, Ring D, Meng C, Kleigrewe K, Lincetto C, Hübner J, Stecher B. Nutritional and host environments determine community ecology and keystone species in a synthetic gut bacterial community. Nat Commun 2023; 14:4780. [PMID: 37553336 PMCID: PMC10409746 DOI: 10.1038/s41467-023-40372-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
A challenging task to understand health and disease-related microbiome signatures is to move beyond descriptive community-level profiling towards disentangling microbial interaction networks. Using a synthetic gut bacterial community, we aimed to study the role of individual members in community assembly, identify putative keystone species and test their influence across different environments. Single-species dropout experiments reveal that bacterial strain relationships strongly vary not only in different regions of the murine gut, but also across several standard culture media. Mechanisms involved in environment-dependent keystone functions in vitro include exclusive access to polysaccharides as well as bacteriocin production. Further, Bacteroides caecimuris and Blautia coccoides are found to play keystone roles in gnotobiotic mice by impacting community composition, the metabolic landscape and inflammatory responses. In summary, the presented study highlights the strong interdependency between bacterial community ecology and the biotic and abiotic environment. These results question the concept of universally valid keystone species in the gastrointestinal ecosystem and underline the context-dependency of both, keystone functions and bacterial interaction networks.
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Affiliation(s)
- Anna S Weiss
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Lisa S Niedermeier
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Alexandra von Strempel
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Anna G Burrichter
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Diana Ring
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Chen Meng
- Bavarian Center for Biomolecular Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Karin Kleigrewe
- Bavarian Center for Biomolecular Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Chiara Lincetto
- Division of Paediatric Infectious Diseases, Dr. von Hauner Children's Hospital, Ludwig Maximilians University, Munich, Germany
| | - Johannes Hübner
- Division of Paediatric Infectious Diseases, Dr. von Hauner Children's Hospital, Ludwig Maximilians University, Munich, Germany
| | - Bärbel Stecher
- Max von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany.
- German Center for Infection Research (DZIF), partner site LMU Munich, Munich, Germany.
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10
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Liu YY. Controlling the human microbiome. Cell Syst 2023; 14:135-159. [PMID: 36796332 PMCID: PMC9942095 DOI: 10.1016/j.cels.2022.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 02/17/2023]
Abstract
We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.
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Affiliation(s)
- Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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11
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Skwara A, Lemos‐Costa P, Miller ZR, Allesina S. Modelling ecological communities when composition is manipulated experimentally. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- Abigail Skwara
- Department of Ecology & Evolution University of Chicago Chicago Illinois USA
- Department of Ecology & Evolutionary Biology Yale University New Haven Connecticut USA
| | - Paula Lemos‐Costa
- Department of Ecology & Evolution University of Chicago Chicago Illinois USA
| | - Zachary R. Miller
- Department of Ecology & Evolution University of Chicago Chicago Illinois USA
| | - Stefano Allesina
- Department of Ecology & Evolution University of Chicago Chicago Illinois USA
- Northwestern Institute for Complex Systems Northwestern University Evanston Illinois USA
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12
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Liu Y, Xu P. Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia. Curr Opin Biotechnol 2022; 76:102754. [PMID: 35809433 DOI: 10.1016/j.copbio.2022.102754] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 12/27/2022]
Abstract
Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.
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Affiliation(s)
- Yugeng Liu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China
| | - Peng Xu
- Department of Chemical Engineering, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China; Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong 515063, China.
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13
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Ji B, Herrgård MJ, Nielsen J. Microbial community dynamics revisited. NATURE COMPUTATIONAL SCIENCE 2021; 1:640-641. [PMID: 38217193 DOI: 10.1038/s43588-021-00144-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Boyang Ji
- BioInnovation Institute, Copenhagen, Denmark.
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