Metabolic Modelling: Methods, Applications and Future Perspectives
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 8954
Special Issue Editors
Interests: systems biology; evolutionary genomics; metabolic modelling
Special Issues, Collections and Topics in MDPI journals
Interests: metabolic modelling; multi-omic integration; machine learning
Interests: synthetic biology; metabolic engineering; bioproduction; microbial communities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Metabolic modelling refers to a wide range of computational techniques that allow investigating, understanding and predicting cellular metabolism at the system level. Metabolic modelling is central in many disciplines, from synthetic to environmental biology, from theoretical to systems biology. Predictions obtained with such an approach can be used to guide focused experimental design, thus, reducing the search space during wet-lab practices. Additionally, some models (e.g., genome-scale metabolic reconstructions) are excellent scaffolds for the integration of -omics data, providing a context-specific, or patient-specific understanding of major biological circuits. A plethora of methods and approaches to computing and predicting metabolic phenotype exists, ranging from deterministic static approaches to modelling frameworks that account for molecular kinetics and cellular stochasticity in general. This Special Issue is devoted to reviewing the current practical and theoretical aspects of metabolic modelling workflows, starting from the basic theory behind such modelling frameworks to the implementation of modelling outcomes. We, therefore, invite research articles, review and viewpoint manuscripts devoted to various aspects within metabolic modelling, with a specific emphasis on omics data integration, dynamic modelling, constraint-based metabolic modelling, ODE-based modelling and metabolic engineering, as well as those highlighting the best practices for experimental-modelling integration
Dr. Marco FondiDr. Claudio Angione
Dr. Rodrigo Ledesma-Amaro
Guest Editors
Manuscript Submission Information
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Keywords
- Metabolic modelling
- kinetic modelling
- constraint-based metabolic modelling
- genome-scale metabolic reconstruction
- machine learning
- ODEs
- community modelling
- flux Balance Analysis
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