» Articles » PMID: 38192220

Genome-scale Metabolic Modelling of Extremophiles and Its Applications in Astrobiological Environments

Overview
Date 2024 Jan 9
PMID 38192220
Authors
Affiliations
Soon will be listed here.
Abstract

Metabolic modelling approaches have become the powerful tools in modern biology. These mathematical models are widely used to predict metabolic phenotypes of the organisms or communities of interest, and to identify metabolic targets in metabolic engineering. Apart from a broad range of industrial applications, the possibility of using metabolic modelling in the contexts of astrobiology are poorly explored. In this mini-review, we consolidated the concepts and related applications of applying metabolic modelling in studying organisms in space-related environments, specifically the extremophilic microbes. We recapitulated the current state of the art in metabolic modelling approaches and their advantages in the astrobiological context. Our review encompassed the applications of metabolic modelling in the theoretical investigation of the origin of life within prebiotic environments, as well as the compilation of existing uses of genome-scale metabolic models of extremophiles. Furthermore, we emphasize the current challenges associated with applying this technique in extreme environments, and conclude this review by discussing the potential implementation of metabolic models to explore theoretically optimal metabolic networks under various space conditions. Through this mini-review, our aim is to highlight the potential of metabolic modelling in advancing the study of astrobiology.

Citing Articles

: A Model Eukaryotic Organism for Astrobiological Studies on Microbial Interactions with Martian Soil Analogs.

Dos Santos A, Schultz J, DalRio I, Molodon F, Almeida Trapp M, Guerra Tenorio B JACS Au. 2025; 5(1):187-203.

PMID: 39886583 PMC: 11775710. DOI: 10.1021/jacsau.4c00869.


Polyextremophile engineering: a review of organisms that push the limits of life.

Caro-Astorga J, Meyerowitz J, Stork D, Nattermann U, Piszkiewicz S, Vimercati L Front Microbiol. 2024; 15:1341701.

PMID: 38903795 PMC: 11188471. DOI: 10.3389/fmicb.2024.1341701.


Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments.

Noirungsee N, Changkhong S, Phinyo K, Suwannajak C, Tanakul N, Inwongwan S Environ Microbiol Rep. 2024; 16(1):e13231.

PMID: 38192220 PMC: 10866088. DOI: 10.1111/1758-2229.13231.

References
1.
Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T . Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput Biol. 2012; 8(7):e1002575. PMC: 3390398. DOI: 10.1371/journal.pcbi.1002575. View

2.
Hameri T, Fengos G, Ataman M, Miskovic L, Hatzimanikatis V . Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations. Metab Eng. 2018; 52:29-41. DOI: 10.1016/j.ymben.2018.10.005. View

3.
Kulyashov M, Peltek S, Akberdin I . A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria . Microorganisms. 2020; 8(7). PMC: 7409357. DOI: 10.3390/microorganisms8071002. View

4.
Harrison S, Lane N . Life as a guide to prebiotic nucleotide synthesis. Nat Commun. 2018; 9(1):5176. PMC: 6289992. DOI: 10.1038/s41467-018-07220-y. View

5.
Orth J, Thiele I, Palsson B . What is flux balance analysis?. Nat Biotechnol. 2010; 28(3):245-8. PMC: 3108565. DOI: 10.1038/nbt.1614. View