Problems in the Development of Efficient Biotechnology for the Synthesis of Valuable Components from Microalgae Biomass

Dvoretsky, D. S.; Temnov, M.; Markin, I. V.; Ustinskaya, Y.; Es’kova, M. A.

doi:10.1134/s0040579522040224

Cited by 4 publications

(1 citation statement)

References 82 publications

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“…In contrast, DOA utilizes non-linear programming to optimize for an objective over the entire time period of interest to obtain a single solution [ 82 , 83 ]. dFBA has successfully been applied to several biological problems, including the modeling of human disease phenotypes [ 84 ], the modeling of microbial communities [ 3 , 9 , 85 ] and the optimization of biotechnological processes [ 86 , 87 ].…”

Section: Introductionmentioning

confidence: 99%

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Carter,

Constantinidou,

Alam

2023

Briefings in Bioinformatics

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Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of ‘-omics’ datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

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Section: Introductionmentioning

confidence: 99%

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Carter,

Constantinidou,

Alam

2023

Briefings in Bioinformatics

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Biological Activity of Endometabolites of Microalgae Chlorella sorokiniana

Temnov,

Ustinskaya,

Meronyuk

et al. 2024

Industrial Biotechnology

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Inferring microbial interactions with their environment from genomic and metagenomic data

Brunner,

Gallegos-Graves,

Kroeger

2023

PLoS Comput Biol

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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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Problems in the Development of Efficient Biotechnology for the Synthesis of Valuable Components from Microalgae Biomass

Cited by 4 publications

References 82 publications

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Biological Activity of Endometabolites of Microalgae Chlorella sorokiniana

Inferring microbial interactions with their environment from genomic and metagenomic data

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