Metabolomics and Mechanistic and Machine Learning Modeling of Metabolism

Dear Colleagues,

Life on Earth depends on the dynamic transformation of chemicals—metabolites orchestrated by proteins and genes, which are, in turn, extensively regulated by metabolites. Omics data provide measurements of all of these molecules, tabulating their changes in different environments in health and disease. Obtaining knowledge of metabolism and having the ability to predict behaviors of biological systems from available data remains one the main challenges of the omics revolution. Mathematical and computational modelling analysis methods are being actively developed in order to investigate, describe, and predict all steps in the metabolic process, from interaction between molecules, all the way to modelling of complete metabolic networks of cells and even networks of multiple cells. Machine learning methods are also extensively utilized to create predictable system models from data. These efforts require major inputs from highly multidisciplinary teams aided by sophisticated computational technologies. The effort is very much worthwhile, and it has already provided a more detailed understanding of disease development, better determination of optimal targets for different treatments, as well as optimization of cellular growth systems such as bioreactors, to name just a few applications. Combination of data-driven machine learning and knowledge-based mechanistic models is expected to provide ways for simulation and design of biological systems with requested properties. This development will have a huge potential in biotechnology, agriculture, and medicine but requires major scientific as well as ethical considerations.    

This Special Issue will focus on computational advances in the modelling of cell metabolism, including mechanistic, machine learning, AI, and combined methods, as well as experimental approaches used for the development of these methods. Additionally, we will explore application of these methodologies in biotechnology, agriculture, and medicine, with particular interest in the design of optimal processes and biological systems with given characteristics. Our goal is to combine in one issue diverse examples of the application of modelling in the analysis and design of metabolic processes, pathways, and networks.

Dr. Miroslava Cuperlovic-Culf
Guest Editor

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.

• Metabolic models
• Reconstruction of metabolic networks
• Machine learning modeling
• Metabolomics
• Fluxomics
• Lipidomics
• Metabolism regulation
• System biology
• Omics data integration
• Metabolism regulation
• Design of biological systems
• Design of biological processes
• AI models of design of biological systems