BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks-Reference-Cited by-同舟云学术

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

Published:2024-09-10 Issue:1 Volume:25 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Hosoda Shion,Iwata Hisashi,Miura Takuya,Tanabe Maiko,Okada Takashi,Mochizuki Atsushi,Sato Miwa

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s12859-024-05921-4.pdf

Reference51 articles.

1. Ögmundarson Ó, Sukumara S, Herrgård MJ, Fantke P. Combining environmental and economic performance for bioprocess optimization. Trends Biotechnol. 2020;38(11):1203–14. https://doi.org/10.1016/j.tibtech.2020.04.011.

2. Tsopanoglou A, Jiménez del Val I. Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses. Curr Opin Chem Eng. 2021;32: 100691. https://doi.org/10.1016/j.coche.2021.100691.

3. Rathore AS, Mishra S, Nikita S, Priyanka P. Bioprocess control: current progress and future perspectives. Life. 2021;11(6):557. https://doi.org/10.3390/life11060557.

4. Behera B, Unpaprom Y, Ramaraj R, Maniam GP, Govindan N, Paramasivan B. Integrated biomolecular and bioprocess engineering strategies for enhancing the lipid yield from microalgae. Renew Sustain Energy Rev. 2021;148: 111270. https://doi.org/10.1016/j.rser.2021.111270.

5. Voigt CA. Synthetic biology 2020–2030: six commercially-available products that are changing our world. Nat Commun. 2020;11(1):6379. https://doi.org/10.1038/s41467-020-20122-2.