The Architecture of Theory and Data in Microbiome Design: towards an S-matrix for microbiomes DOI Creative Commons
Shreya Arya,

Ashish Bino George,

James P. O’Dwyer

и другие.

Опубликована: Авг. 9, 2024

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used range mathematical models understand, predict, control microbiomes, ranging from mechanistic models, putting populations their interactions as focus, purely statistical approaches, searching patterns empirical experimental data. We review success limitations these modeling approaches when designing novel especially guided by (inevitably) incomplete Although successful at predicting generic assembly, phenomenological tend fall short precision needed design implement specific functionality microbiome. argue that effectively with optimal functions diverse environments, should combine data-driven techniques models---a middle, third way using theory inform design.

Язык: Английский

More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities DOI Creative Commons
Patricia Geesink,

Jolanda ter Horst,

Thijs J. G. Ettema

и другие.

FEMS Microbiology Ecology, Год журнала: 2024, Номер 100(4)

Опубликована: Март 5, 2024

Abstract Microbial communities are not only shaped by the diversity of microorganisms and their individual metabolic potential, but also vast amount intra- interspecies interactions that can occur pairwise among microorganisms, we suggest more attention should be drawn towards effects on entire microbiome emerge from between community members. The production certain metabolites tied to a specific microbe-microbe interaction might subsequently influence physicochemical parameters habitat, stimulate change in trophic network or create new micro-habitats through formation biofilms, similar antimicrobial substances which negatively affect one microorganism cause ripple effect abundance other Here, argue combining established as well innovative laboratory computational methods is needed predict novel assess secondary effects. Such efforts will enable future studies expand our knowledge dynamics complex microbial communities.

Язык: Английский

Процитировано

7
Connecting microbial community assembly and function DOI
Leonora Bittleston

Current Opinion in Microbiology, Год журнала: 2024, Номер 80, С. 102512 - 102512

Опубликована: Июль 16, 2024

Язык: Английский

Процитировано

5
Emergent predictability in microbial ecosystems DOI Creative Commons
Jacob Moran, Mikhail Tikhonov

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Март 29, 2024

Microbial ecosystems carry out essential functions for global climate, human health, and industry. These complex communities exhibit a surprising amount of functionally relevant diversity at all levels taxonomic resolution, presenting significant challenge most modeling frameworks. A long-standing hope theoretical ecology is that some patterns might persist despite community complexity – or perhaps even emerge because it. deeper understanding such “emergent simplicity” could enable new approaches predicting the behaviors in nature. However, examples described so far afford limited predictive power, as they focused on reproducibility rather than prediction. Here, we propose an information-theoretic framework defining, nuancing quantifying emergent simplicity empirical data based ability simple models to predict community-level functional properties. Applying this two published datasets, demonstrate majority properties measured across both experiments robust evidence predictability: surprisingly, richness increases, compositional descriptions become more predictive. We show behavior not typical within standard frameworks ecology, argue improving our control natural microbial will require shift focus: away from , towards prediction ecosystems.

Язык: Английский

Процитировано

4
Metabolic Plasticity Shapes Microbial Communities across a Temperature Gradient DOI
Xin Sun,

Ariel Favier,

Jacquelyn Folmar

и другие.

The American Naturalist, Год журнала: 2024, Номер 204(4), С. 381 - 399

Опубликована: Июнь 28, 2024

AbstractA central challenge in community ecology is understanding and predicting the effects of abiotic factors on assembly. In particular, microbial communities play a role ecosystem, but we do not understand how changing like temperature are going to affect composition or function. this article, studied self-assembly multiple synthetic environments changes based metabolic responses different functional groups along gradient. many communities, coexist through partitioning carbon sources an emergent trophic structure (cross-feeding). system, respirofermentative bacteria display preference for sugars supplied as only source secrete secondary (organic acids) that more efficiently consumed by obligate respirators. As consequence structure, plasticity respirofermenters has downstream consequences relative abundance respirators across temperatures. We found temperatures can largely be described increase fermentation by-products with increasing from bacteria. This research highlights importance trade-offs species interactions dynamics gradients.

Язык: Английский

Процитировано

4
The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes DOI
Shreya Arya, Ashish B. George, James P. O’Dwyer

и другие.

Current Opinion in Microbiology, Год журнала: 2025, Номер 83, С. 102580 - 102580

Опубликована: Янв. 22, 2025

Язык: Английский

Процитировано

0
Macroecological patterns in experimental microbial communities DOI Creative Commons
William R. Shoemaker,

Álvaro Sánchez,

Jacopo Grilli

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июль 26, 2023

Abstract Ecology has historically benefited from the characterization of statistical patterns biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal diversity abundance that can be captured by effective models. Experimentation simultaneously played a crucial role, advent high-replication community time-series allowed researchers to investigate underlying ecological forces. However, there remains gap between experiments performed in laboratory documented natural systems, we do not know whether these recapitulated lab experimental manipulations produce effects. This work aims at bridging ecology Using time-series, demonstrate observed nature exist setting, despite controlled conditions, unified under Stochastic Logistic Model growth (SLM). We find demographic (e.g., migration) impact patterns. By modifying SLM incorporate said alongside details sampling), obtain predictions are consistent with outcomes. combining models, macroecology viewed predictive discipline.

Язык: Английский

Процитировано

5
The Architecture of Theory and Data in Microbiome Design: towards an S-matrix for microbiomes DOI Creative Commons
Shreya Arya,

Ashish Bino George,

James P. O’Dwyer

и другие.

Опубликована: Авг. 9, 2024

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used range mathematical models understand, predict, control microbiomes, ranging from mechanistic models, putting populations their interactions as focus, purely statistical approaches, searching patterns empirical experimental data. We review success limitations these modeling approaches when designing novel especially guided by (inevitably) incomplete Although successful at predicting generic assembly, phenomenological tend fall short precision needed design implement specific functionality microbiome. argue that effectively with optimal functions diverse environments, should combine data-driven techniques models---a middle, third way using theory inform design.

Язык: Английский

Процитировано

1