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Computational Biology

A section of Computation (ISSN 2079-3197).

Section Information

This Section, entitled “Computational Biology”, encompasses a diverse range of computational techniques that advance biological research at all scales, from the molecular to the population level. It encompasses key computational methodologies for the life sciences and covers the entire spectrum of bioinformatics.

Contributions to Computational Biology span novel algorithms, methodologies, and their applications in biological research, with a strong emphasis on the computational aspects of the research. In particular (but not exclusively), this Section welcomes contributions that address the following topics:

Genomic data analysis methods for understanding genetic variations and disease mechanisms;
Protein structure prediction tools and molecular docking simulation methods for drug discovery;
Molecular dynamics simulation approaches for the study of biomolecular interactions and dynamics;
Systems biology methodologies, including the computational modeling of biological processes and cellular functions, and network analysis approaches that unravel biological pathways and regulatory networks;
Machine learning and artificial intelligence developments for applications in biological data analysis;
Innovations in bioinformatics tools and databases for biological data storage, retrieval, and analysis;
Computational methods for synthetic biology, including tools for designing synthetic pathways and circuits, optimizing metabolic processes, and creating engineered organisms;
Innovative approaches to computational phylogenetics and evolutionary biology for the study of evolutionary patterns, reconstructing phylogenetic trees, and modelling evolutionary processes using computational tools;
Application of quantum computing in solving complex biological problems, including quantum algorithms for molecular modeling;
Contributions to computational neuroscience, including methods for the analysis of brain networks, neural simulations, and understanding neural mechanisms using computational tools;