BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks
We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.
Authors
- Takashi Okada (ORCID: https://orcid.org/0000-0002-7769-6857)
- Atsushi Mochizuki (ORCID: https://orcid.org/0000-0002-7714-1755)
- Shion Hosoda (ORCID: https://orcid.org/0000-0001-5093-6833)
- Takuya Miura (ORCID: https://orcid.org/0000-0002-0238-2845)
- Hisashi Iwata (ORCID: https://orcid.org/0000-0001-5031-6124)
- Maiko Tanabe
- Miwa Sato
Institutions
- Kyoto University (JP)
- Kyoto Medical Center (JP)
- Hitachi (Japan) (JP)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1186/s12859-024-05921-4
- Primary Topic
- Microbial Metabolic Engineering and Bioproduction
- Type
- article
- Field-Weighted Citation Impact
- 0.00