Cybergenetic control of microbial community composition

Lee, Ting An; Steel, Harrison

doi:10.3389/fbioe.2022.957140

Cited by 8 publications

(7 citation statements)

References 124 publications

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“…This section briefly introduces the concept of closed-loop control, discusses elements of closed-loop control concerning microbiomes, and emphasizes model-based control strategies for microbiomes with examples from biotechnology. Further information on this topic can be found in the review by Lee and Steel [243].…”

Section: Controlling Microbiomesmentioning

confidence: 99%

Microbiome Modeling: A Beginner’s Guide

Lange,

Kranert,

Krüger

et al. 2024

Preprint

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Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

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Section: Controlling Microbiomesmentioning

confidence: 99%

Microbiome Modeling: A Beginner’s Guide

Lange,

Kranert,

Krüger

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This distribution of labor is augmented by substrate cross-feeding and overcomes the challenges of population instability and poor control over strain composition ( Fig. 6c ; Chung et al., 2013 ; Sgobba & Wendisch, 2020 ; Duncker et al., 2021 ; Kang et al., 2022 ; T. A. Lee & Steel, 2022 ; X. Li et al., 2022 ). The initial inoculum ratio of selective sugar utilization strains as well as periodic pulsing of select carbon sources are especially facile tools with which to control metabolic product titers (H. Zhang et al., 2015 ).…”

Section: Sugar Transport As a Metabolic Control Strategymentioning

confidence: 99%

Sugar transport in thermophiles: Bridging lignocellulose deconstruction and bioconversion

Tjo,

Conway

2024

Journal of Industrial Microbiology and Biotechnology

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Biomass degrading thermophiles play an indispensable role in building lignocellulose-based supply chains. They operate at high temperatures to improve process efficiencies and minimize mesophilic contamination, can overcome lignocellulose recalcitrance through their native carbohydrate-active enzyme (CAZyme) inventory, and can utilize a wide range of sugar substrates. However, sugar transport in thermophiles is poorly understood and investigated, as compared to enzymatic lignocellulose deconstruction and metabolic conversion of sugars to value-added chemicals. Here, we review the general modes of sugar transport in thermophilic bacteria and archaea, covering the structural, molecular, and biophysical basis of their high-affinity sugar uptake. We also discuss recent genetic studies on sugar transporter function. With this understanding of sugar transport, we discuss strategies for how sugar transport can be engineered in thermophiles, with the potential to enhance the conversion of lignocellulosic biomass into renewable products. One-Sentence Summary Sugar transport is the understudied link between extracellular biomass deconstruction and intracellular sugar metabolism in thermophilic lignocellulose bioprocessing.

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“…This section briefly introduces the concept of closed-loop control, discusses elements of closed-loop control concerning microbiomes, and emphasizes model-based control strategies for microbiomes with examples from biotechnology and the human gut. Further information on this topic can be found in the reviews by Lee and Steel ( 2022 ) and Liu ( 2023 ).…”

Section: How Modeling Guides Microbiome Controlmentioning

confidence: 99%

Microbiome modeling: a beginner's guide

Lange,

Kranert,

Krüger

et al. 2024

Front. Microbiol.

View full text Add to dashboard Cite

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

show abstract

Cybergenetic control of microbial community composition

Cited by 8 publications

References 124 publications

Microbiome Modeling: A Beginner’s Guide

Microbiome Modeling: A Beginner’s Guide

Sugar transport in thermophiles: Bridging lignocellulose deconstruction and bioconversion

Microbiome modeling: a beginner's guide

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