| 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. |