Metabolic Disease Module Identification

Dear Colleagues,

Today, societies around the globe are confronted with a steadily increasing occurrence of metabolic disorders such as cardiovascular diseases, obesity, diabetes, fatty liver diseases, and other manifestations of metabolic syndrome. In general, metabolomics research, particularly regarding lipidomics and foodomics, has contributed to crucial links between the host metabolism, disorders and diseases, and lifestyle, including diet choices and activity. Novel bioinformatics and computational biology methodology have been key in identifying these links by integrated analyses in multi-omics settings. However, a large gap has developed between computational solutions for genomics, transcriptomics, and proteomics on the one hand and metabolomics on the other. For the former, experimental and computational methods have been harmonized and consolidated, and community standards have been established. Additionally, publicly accessible web-based services and analysis platforms are broadly available. This is not yet the case for the latter.

One powerful tool to integrate and mine heterogeneous data sources are biological networks. Protein–protein interactions (PPIs) have been successfully used to identify cohort- or sample-specific alterations in gene regulation. Network algorithms, such as local searches or ant colony optimization, have been developed to discover disease-related active modules and subnetworks that are consistently dysregulated in case observations. Via gene set enrichment (GSE) and pathway mapping, these disease modules could be linked to downstream events involved in phenotype expression. These kinds of solutions for computational data integration and mining barely exist for metabolic or biochemical reaction networks.

Therefore, this Special Issue aims to highlight state-of-the-art computational methods that 1.) identify active modules in metabolic and/or biochemical reaction as well as heterogeneous (hyper-)networks and 2.) that are capable of mechanistically linking these modules to upstream alterations on a protein, transcript, and gene level. Hence, this Special Issue focuses on the demonstration and application of innovative computational approaches in the fields of metabolomics, including closely related fields such as lipidomics and foodomics, etc., as well as biochemistry and biophysics. These methods should lead to insights into molecular disease mechanisms, marker, and drug identification, options for treatment and therapy, and molecular stratification and disease classification. Methods may include and combine statistical analyses, biological network analyses and network biomedicine, pathway analyses and computational models, machine learning and AI, and big data analyses. Integrated multi-omics approaches with a strong contribution to filling the previously described gap in metabolomics are highly encouraged. Manuscripts will be reviewed accordingly, considering their innovative value in creating solutions to the described current problems. All methods should be easily applicable to researchers of all involved disciplines.

Prof. Dr. Jan Baumbach
Dr. Josch K. Pauling
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.

• metabolomics
• systems medicine
• disease mechanisms
• bioinformatics
• multi-omics