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Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.
- Source :
-
ACS synthetic biology [ACS Synth Biol] 2024 Apr 19; Vol. 13 (4), pp. 1205-1214. Date of Electronic Publication: 2024 Apr 05. - Publication Year :
- 2024
-
Abstract
- This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.
- Subjects :
- Bayes Theorem
Kinetics
Gene Regulatory Networks
Subjects
Details
- Language :
- English
- ISSN :
- 2161-5063
- Volume :
- 13
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- ACS synthetic biology
- Publication Type :
- Academic Journal
- Accession number :
- 38579163
- Full Text :
- https://doi.org/10.1021/acssynbio.3c00662