| Autor: |
Gollub, Mattia G, Backes, Thierry, Kaltenbach, Hans-Michael, Stelling, Jörg |
| Zdroj: |
Bioinformatics; Nov2024, Vol. 40 Issue 11, p1-4, 4p |
| Abstrakt: |
Motivation Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown. Results The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of K M and k cat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide calibrated uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism. Availability and implementation Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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