Research

Research at SBML focuses on solving medical and biotech problems through systems biology approaches. Specifically, we develop computational models and workflows to advance synthetic biology/metabolic engineering, and biomedical sciences. Representative research topics include:

Building and analyzing biological networks (metabolism, signaling, and transcriptional regulations) of medically and/or industrially important biological systems.
Predicting effective engineering targets (e.g., gene manipulation targets, biomarkers, and drug targets) for desired biological phenotypes.
Predicting drug responses using chemical and medical data.

To this end, we use a wide range of computational approaches, including machine learning, genome-scale metabolic model and bioinformatics, to effectively analyze bio big data. We also have an extensive research collaboration network for our studies.

Computational optimization of microbial metabolism for enhanced production of natural products

(Kim et al. Natural Product Reports, 2016)

Prediction of biomarkers and drug targets using biological/medical data and computational methods

(Lee and Kim, Molecular Omics, 2021)

Prediction of the biological effects of molecules/drugs using machine learning

(Jeon et al. Natural Product Reports, 2021)