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Article ; Online: Assembly archetypes in ecological communities.

Flores-Arguedas, Hugo / Antolin-Camarena, Omar / Saavedra, Serguei / Angulo, Marco Tulio

2023 Volume 20, Issue 208, Page(s) 20230349

Abstract: An instrumental discovery in comparative and developmental biology is the existence of assembly archetypes that synthesize the vast diversity of organisms' body plans-from legs and wings to human arms-into simple, interpretable and general design ... ...

Abstract	An instrumental discovery in comparative and developmental biology is the existence of assembly archetypes that synthesize the vast diversity of organisms' body plans-from legs and wings to human arms-into simple, interpretable and general design principles. Here, we combine a novel mathematical formalism based on category theory with experimental data to show that similar 'assembly archetypes' exist at the larger organization scale of ecological communities when assembling a species pool across diverse environmental contexts, particularly when species interactions are highly structured. We applied our formalism to clinical data discovering two assembly archetypes that differentiate between healthy and unhealthy human gut microbiota. The concept of assembly archetypes and the methods to synthesize them can pave the way to discovering the general assembly principles of the ecological communities we observe in nature.
MeSH term(s)	Animals ; Humans ; Biota ; Gastrointestinal Microbiome
Language	English
Publishing date	2023-11-29
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2023.0349
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Generalizing game-changing species across microbial communities.

Deng, Jie / Angulo, Marco Tulio / Saavedra, Serguei

ISME communications

2021 Volume 1, Issue 1, Page(s) 22

Abstract: Microbes form multispecies communities that play essential roles in our environment and health. Not surprisingly, there is an increasing need for understanding if certain invader species will modify a given microbial community, producing either a desired ...

Abstract	Microbes form multispecies communities that play essential roles in our environment and health. Not surprisingly, there is an increasing need for understanding if certain invader species will modify a given microbial community, producing either a desired or undesired change in the observed collection of resident species. However, the complex interactions that species can establish between each other and the diverse external factors underlying their dynamics have made constructing such understanding context-specific. Here we integrate tractable theoretical systems with tractable experimental systems to find general conditions under which non-resident species can change the collection of resident communities-game-changing species. We show that non-resident colonizers are more likely to be game-changers than transients, whereas game-changers are more likely to suppress than to promote resident species. Importantly, we find general heuristic rules for game-changers under controlled environments by integrating mutual invasibility theory with in vitro experimental systems, and general heuristic rules under changing environments by integrating structuralist theory with in vivo experimental systems. Despite the strong context-dependency of microbial communities, our work shows that under an appropriate integration of tractable theoretical and experimental systems, it is possible to unveil regularities that can then be potentially extended to understand the behavior of complex natural communities.
Language	English
Publishing date	2021-06-08
Publishing country	England
Document type	Journal Article
ISSN	2730-6151
ISSN (online)	2730-6151
DOI	10.1038/s43705-021-00022-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting microbiome compositions from species assemblages through deep learning.

Michel-Mata, Sebastian / Wang, Xu-Wen / Liu, Yang-Yu / Angulo, Marco Tulio

iMeta

2022 Volume 1, Issue 1

Abstract: ... a deep learning framework that automatically learns the map between species assemblages and community ... validate our framework using synthetic data generated by classical population dynamics models ... Then, we apply our framework to data from ...

Abstract	Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from
Language	English
Publishing date	2022-03-01
Publishing country	Australia
Document type	Journal Article
ISSN	2770-596X
ISSN (online)	2770-596X
DOI	10.1002/imt2.3
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Article ; Online: A theoretical framework for controlling complex microbial communities.

Angulo, Marco Tulio / Moog, Claude H / Liu, Yang-Yu

Nature communications

2019 Volume 10, Issue 1, Page(s) 1045

Abstract: ... control framework has limited our ability to manipulate these microbial communities. Here we fill this gap ... by developing a control framework based on the new notion of structural accessibility. Our framework uses ... of which allows controlling the whole community. We numerically validate our control framework on large ...

Abstract	Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros. Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states.
MeSH term(s)	Animals ; Clostridioides difficile/physiology ; Ecosystem ; Gastrointestinal Microbiome/physiology ; Germ-Free Life ; Host Microbial Interactions/physiology ; Mice ; Models, Biological ; Porifera/microbiology ; Porifera/physiology
Language	English
Publishing date	2019-03-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-019-08890-y
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Article ; Online: Revealing Complex Ecological Dynamics via Symbolic Regression.

Chen, Yize / Angulo, Marco Tulio / Liu, Yang-Yu

BioEssays : news and reviews in molecular, cellular and developmental biology

2019 Volume 41, Issue 12, Page(s) e1900069

Abstract: Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this ... ...

Abstract	Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.
MeSH term(s)	Ecology ; Ecosystem ; Models, Theoretical
Language	English
Publishing date	2019-10-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	50140-2
ISSN	1521-1878 ; 0265-9247
ISSN (online)	1521-1878
ISSN	0265-9247
DOI	10.1002/bies.201900069
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Article ; Online: Coexistence holes characterize the assembly and disassembly of multispecies systems.

Angulo, Marco Tulio / Kelley, Aaron / Montejano, Luis / Song, Chuliang / Saavedra, Serguei

Nature ecology & evolution

2021 Volume 5, Issue 8, Page(s) 1091–1101

Abstract: A central goal of ecological research has been to understand the limits on the maximum number of species that can coexist under given constraints. However, we know little about the assembly and disassembly processes under which a community can reach such ...

Abstract	A central goal of ecological research has been to understand the limits on the maximum number of species that can coexist under given constraints. However, we know little about the assembly and disassembly processes under which a community can reach such a maximum number, or whether this number is in fact attainable in practice. This limitation is partly due to the challenge of performing experimental work and partly due to the lack of a formalism under which one can systematically study such processes. Here, we introduce a formalism based on algebraic topology and homology theory to study the space of species coexistence formed by a given pool of species. We show that this space is characterized by ubiquitous discontinuities that we call coexistence holes (that is, empty spaces surrounded by filled space). Using theoretical and experimental systems, we provide direct evidence showing that these coexistence holes do not occur arbitrarily-their diversity is constrained by the internal structure of species interactions and their frequency can be explained by the external factors acting on these systems. Our work suggests that the assembly and disassembly of ecological systems is a discontinuous process that tends to obey regularities.
MeSH term(s)	Ecosystem
Language	English
Publishing date	2021-05-27
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2397-334X
ISSN (online)	2397-334X
DOI	10.1038/s41559-021-01462-8
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Article ; Online: Identifying keystone species in microbial communities using deep learning.

Wang, Xu-Wen / Sun, Zheng / Jia, Huijue / Michel-Mata, Sebastian / Angulo, Marco Tulio / Dai, Lei / He, Xuesong / Weiss, Scott T / Liu, Yang-Yu

Nature ecology & evolution

2023 Volume 8, Issue 1, Page(s) 22–31

Abstract: ... keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key ... validated this DKI framework using synthetic data and applied DKI to analyse real data. We found ... The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem ...

Abstract	Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.
MeSH term(s)	Deep Learning ; Microbiota ; Machine Learning
Language	English
Publishing date	2023-11-16
Publishing country	England
Document type	Journal Article
ISSN	2397-334X
ISSN (online)	2397-334X
DOI	10.1038/s41559-023-02250-2
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Article ; Online: A theoretical framework for controlling complex microbial communities

Marco Tulio Angulo / Claude H. Moog / Yang-Yu Liu

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 12

Abstract: ... Here, the authors introduce the notion of structural accessibility and develop a framework to identify ...

Abstract	Controlling microbial communities could help restore ecosystems and maintain healthy microbiota. Here, the authors introduce the notion of structural accessibility and develop a framework to identify minimal sets of driver species, manipulation of which could allow control of a microbial community.
Keywords	Science ; Q
Language	English
Publishing date	2019-03-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A theoretical framework for controlling complex microbial communities

Marco Tulio Angulo / Claude H. Moog / Yang-Yu Liu

Nature Communications, Vol 10, Iss 1, Pp 1-

2019 Volume 12

Abstract: ... Here, the authors introduce the notion of structural accessibility and develop a framework to identify ...

Abstract	Controlling microbial communities could help restore ecosystems and maintain healthy microbiota. Here, the authors introduce the notion of structural accessibility and develop a framework to identify minimal sets of driver species, manipulation of which could allow control of a microbial community.
Keywords	Science ; Q
Language	English
Publishing date	2019-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks.

Angulo, Marco Tulio / Castaños, Fernando / Moreno-Morton, Rodrigo / Velasco-Hernández, Jorge X / Moreno, Jaime A

Journal of the Royal Society, Interface

2021 Volume 18, Issue 178, Page(s) 20200803

Abstract: For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or ... ...

Abstract	For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significant economic and societal costs. Here, we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions for mitigating epidemic outbreaks. We find that reducing the reproduction number below one is sufficient but not necessary. Instead, our criterion prescribes the required reduction in the reproduction number according to the desired maximum of disease prevalence and the maximum decrease of disease transmission that the interventions can achieve. We study the implications of our theoretical results for designing non-pharmaceutical interventions in 16 cities and regions during the COVID-19 pandemic. In particular, we estimate the minimal reduction of each region's contact rate necessary to control the epidemic optimally. Our results contribute to establishing a rigorous methodology to design optimal non-pharmaceutical intervention policies for mitigating epidemic outbreaks.
MeSH term(s)	COVID-19 ; Communicable Disease Control ; Disease Outbreaks/prevention & control ; Humans ; Pandemics ; SARS-CoV-2
Language	English
Publishing date	2021-05-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2020.0803
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