Title |
User guides for biologists to learn computational methods |
Author |
Dokyun Na* |
Address |
Department of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea |
Bibliography |
Journal of Microbiology, 58(3),173-175, 2020,
|
DOI |
10.1007/s12275-020-9723-1
|
Key Words |
computational biology, machine learning, microbiome,
Ribo-seq, drug discovery |
Abstract |
System-wide studies of a given molecular type are referred
to as “omics.” These include genomics, proteomics, and metabolomics,
among others. Recent biotechnological advances
allow for high-throughput measurement of cellular components,
and thus it becomes possible to take a snapshot of all
molecules inside cells, a form of omics study. Advances in
computational modeling methods also make it possible to
predict cellular mechanisms from the snapshots. These technologies
have opened an era of computation-based biology.
Component snapshots allow the discovery of gene-phenotype
relationships in diseases, microorganisms in the human
body, etc. Computational models allow us to predict new outcomes,
which are useful in strain design in metabolic engineering
and drug discovery from protein-ligand interactions.
However, as the quantity of data increases or the model
becomes complicated, the process becomes less accessible
to biologists. In this special issue, six protocol articles
are presented as user guides in the field of computational
biology. |