Ecological modelling approaches for predicting emergent properties in microbial communities

Berg, Naomi Iris van den; Machado, Daniel; Santos, Sophia Torres; Rocha, Isabel; Chacón, Jeremy M.; Harcombe, William R.; Mitri, Sara; Patil, Kiran Raosaheb

doi:10.1038/s41559-022-01746-7

Cited by 119 publications

(109 citation statements)

References 179 publications

(260 reference statements)

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“… 40 − 43 Advances in next-generation sequencing coupled with genome-scale metabolic models and constraint-based reconstruction and analysis present platforms for mining novel microbe–microbe interactions and predicting emergent interspecies interactions. 44 − 46 Insights from these models, while still predictive, can inform the ecological context of a potential chassis organism.…”

Section: Environmental Biosensing Poses Unique Design Constraints On ...mentioning

confidence: 99%

“…Interspecies microbial interactions are highly diverse and can be stimulated by the organisms themselves, other organisms in their surroundings, or their environment. , These interactions are complex and hierarchical, making them challenging to emulate experimentally. However, the development of simulation toolboxes to represent the microbial interactome in silico has supported the prediction of interspecies interactions. − Advances in next-generation sequencing coupled with genome-scale metabolic models and constraint-based reconstruction and analysis present platforms for mining novel microbe–microbe interactions and predicting emergent interspecies interactions. − Insights from these models, while still predictive, can inform the ecological context of a potential chassis organism.…”

Section: Environmental Biosensing Poses Unique Design Constraints On ...mentioning

confidence: 99%

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A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

2022

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Microbial biosensors sense and report exposures to stimuli, thereby facilitating our understanding of environmental processes. Successful design and deployment of biosensors hinge on the persistence of the microbial host of the genetic circuit, termed the chassis. However, model chassis organisms may persist poorly in environmental conditions. In contrast, non-model organisms persist better in environmental conditions but are limited by other challenges, such as genetic intractability and part unavailability. Here we identify ecological, metabolic, and genetic constraints for chassis development and propose a conceptual framework for the systematic selection of environmental biosensor chassis. We identify key challenges with using current model chassis and delineate major points of conflict in choosing the most suitable organisms as chassis for environmental biosensing. This framework provides a way forward in the selection of biosensor chassis for environmental synthetic biology.

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Section: Environmental Biosensing Poses Unique Design Constraints On ...mentioning

confidence: 99%

Section: Environmental Biosensing Poses Unique Design Constraints On ...mentioning

confidence: 99%

A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

2022

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“…Ecological dynamic . Each of the n communities is composed of S species with continuous abundances ( N i ) i =1,…, S , whose variation is described by the generalized Lotka-Volterra equations [20]: …”

Section: Model For Selection Of Species-rich Communitiesmentioning

confidence: 99%

Artificial selection of communities drives the emergence of structured interactions

Fraboul

Biroli

Monte

2021

Preprint

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Species-rich communities, such as the microbiota or environmental microbial assemblages, provide key functions for human health and ecological resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. Numerical explorations allowed to link the evolutionary dynamics to the multiple parameters involved in this complex, multi-scale evolutionary process. However, a comprehensive theoretical understanding of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of species-rich communities, each described by disordered generalized Lotka-Volterra equations, that we study analytically and by numerical simulations. Our results reveal that a generic response to selection for larger total community abundance is the emergence of an isolated eigenvalue of the interaction matrix that can be understood as an effective cross-feeding term. In this way, selection imprints a structure on the community, which results in a global increase of both the level of mutualism and the diversity of interactions. Our approach moreover allows to disentangle the role of intraspecific competition, interspecific interactions symmetry and number of selected communities in the evolutionary process, and can thus be used as a guidance in optimizing artificial selection protocols.

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“…Microbial communities impact human health [ 1 ], global biogeochemical cycles [ 2 , 3 ] and plant growth [ 4 ]. Properties of microbial communities, such as resilience, coexistence and self-organization, emerge from the interactions between their members and between members and the environment [ 5 , 6 ]. Studying this emergence is challenging due to the complex and dynamic nature of the interactions [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Encounter rates prime interactions between microorganisms

et al. 2023

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Properties of microbial communities emerge from the interactions between microorganisms and between microorganisms and their environment. At the scale of the organisms, microbial interactions are multi-step processes that are initiated by cell–cell or cell–resource encounters. Quantification and rational design of microbial interactions thus require quantification of encounter rates. Encounter rates can often be quantified through encounter kernels—mathematical formulae that capture the dependence of encounter rates on cell phenotypes, such as cell size, shape, density or motility, and environmental conditions, such as turbulence intensity or viscosity. While encounter kernels have been studied for over a century, they are often not sufficiently considered in descriptions of microbial populations. Furthermore, formulae for kernels are known only in a small number of canonical encounter scenarios. Yet, encounter kernels can guide experimental efforts to control microbial interactions by elucidating how encounter rates depend on key phenotypic and environmental variables. Encounter kernels also provide physically grounded estimates for parameters that are used in ecological models of microbial populations. We illustrate this encounter-oriented perspective on microbial interactions by reviewing traditional and recently identified kernels describing encounters between microorganisms and between microorganisms and resources in aquatic systems.

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Ecological modelling approaches for predicting emergent properties in microbial communities

Cited by 119 publications

References 179 publications

A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology

Artificial selection of communities drives the emergence of structured interactions

Encounter rates prime interactions between microorganisms

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