MS-Based Metabolomics for the Study of Metabolism in Complex Systems
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Environmental Metabolomics".
Deadline for manuscript submissions: 31 May 2024 | Viewed by 327
Special Issue Editors
Interests: metabolomics; soil organic matter; microbial communities; plant–microbe interactions
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The metabolism occupies a central role in the environmental context, serving as a vital link connecting genetic information with environmental dynamics. The comprehensive assessment of metabolites enables the capture of instantaneous snapshots depicting the metabolic landscape of organisms such as plants, microbes, and entire ecosystems, which, when coupled with multi-omics (genomics, transcriptomics, proteomics), can further advance our understanding of the impact of external influences on ecosystem stability, with exciting implications for our understanding the future of our Earth in the face of changing environmental conditions.
In this context, we extend a cordial invitation for contributions that center on utilizing metabolomics to unearth functional interplays existing between microbiomes and hosts within complex ecosystems, such as the soil and gut. These studies explore the adaptational prowess of these relationships when confronted with stressors and perturbations in complex environments. While complex environments have unique challenges in evaluating ecosystem dynamics, they also have the potential to bridge different ecosystems and to gain insights through common biological links. Therefore, we solicit contributions in the realm of software development, alongside review articles focused on the application of metabolomics in deciphering complex microbiome–metabolome dynamics, as well as work that has adapted compatible approaches from other fields, all of which could further the field of metabolomics.
This encompasses unraveling the complexity of the metabolism and elucidating how such biochemical intricacies undergo transformation in response to evolving different environmental conditions. We encourage submissions exploring the functional interplay within complex microbiomes (soil and gut), alongside software innovations and critical evaluations that spotlight the transformative potential of metabolomics in deciphering the nuances of metabolism within the ever-shifting ecological context.
Dr. Malak Tfaily
Dr. Sumudu Rajakaruna
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- metabolites
- soil
- gut
- microbes
- liquid chromatography
- disturbance
- data science
