Metaflux — A Platform for Intelligent Metabolic Engineering
Metabolic engineering aims to redesign cellular systems to efficiently produce valuable chemicals, fuels, and materials. Metaflux is a web-based platform that integrates computational tools for metabolic network analysis and protein function prediction to support data-driven strain design. With Metaflux, users can identify metabolic engineering targets for enhanced chemical production and annotate unknown or poorly characterized proteins from diverse organisms. By bridging metabolic pathway optimization with functional genomics, Metaflux enables users to develop rational engineering strategies and accelerate microbial strain development.
FBA, FSEOF, FVSEOF, and iBridge for the prediction of metabolic engineering targets
(https://github.com/kaistsystemsbiology/iBridge)
DeepTFactor for the prediction of transcription factors
(https://bitbucket.org/kaistsystemsbiology/deeptfactor/src/master/)
DeepECtransformer for the prediction of EC numbers
(https://github.com/kaistsystemsbiology/DeepProZyme)
DeepRFC for the evaluation of reaction feasibility
(https://bitbucket.org/kaistsystemsbiology/deeprfc/src/master/)
How to cite
In silico identification of gene amplification targets for improvement of lycopene production
Hyung Seok Choi, Sang Yup Lee, Tae Yong Kim, Han Min Woo
Appl. Environ. Microbiol., 76(10):3097-3105 (2010).
Flux variability scanning based on enforced objective flux for identifying gene amplification targets
Jong Myoung Park, Hye Min Park, Won Jun Kim, Hyun Uk Kim, Tae Yong Kim, Sang Yup Lee
BMC Syst. Biol., 6:106 (2012).
Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes
Won Jun Kim, Youngjoon Lee, Hyun Uk Kim, Jae Yong Ryu, Jung Eun Yang, Sang Yup Lee
Cell Syst., 14(15):990-1001.e5 (2023)
DeepTFactor: A deep learning-based tool for the prediction of transcription factors
Gi Bae Kim, Ye Gao, Bernhard O. Palsson, Sang Yup Lee
Proc. Natl. Acad. Sci. (PNAS), 118(2): e2021171118 (2021)
Functional annotation of enzyme-encoding genes using deep learning with transformer layers
Gi Bae Kim, Ji Yeon Kim, Jong An Lee, Charles J. Norsigian, Bernhard O. Palsson, Sang Yup Lee
Nat. Commun., 14:7370 (2023)
A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis
Yeji Kim, Jae Yong Ryu, Hyun Uk Kim, Woo Dae Jang, Sang Yup Lee
Biotechnol. J., 16(5): 1-7 (2021)
Contact us
Prof. Sang Yup Lee (leesy@kaist.ac.kr)
Ji Yeon Kim (jiyeon980907@kaist.ac.kr)
Minjee Chae (mjchae@kaist.ac.kr)