ArticlePLoS computational biology2019
Bayesian inference of metabolic kinetics from genome-scale multiomics data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- A probabilistic and context-dependent cell culture modelling framework for regulatory toxicology.Frontiers in toxicology · 2026Article
- Prediction of non-intuitive metabolic targets with bayesian metabolic control analysis to improve 3-hydroxypropionic acid production inFrontiers in bioengineering and biotechnology · 2026Article
- Evaluating the limitations of Bayesian metabolic control analysis.PLoS computational biology · 2026Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks.BMC bioinformatics · 2024Article
- Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.ACS synthetic biology · 2024Review
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning.Metabolic engineering communications · 2023Article
- BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.PLoS computational biology · 2023Article
- Yeast increases glycolytic flux to support higher growth rates accompanied by decreased metabolite regulation and lower protein phosphorylation.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Standards, dissemination, and best practices in systems biology.Current opinion in biotechnology · 2023Review
- Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations.Nature communications · 2023Article
- Do-calculus enables estimation of causal effects in partially observed biomolecular pathways.Bioinformatics (Oxford, England) · 2022Article
- A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks.Analytical chemistry · 2022Article
- Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.Biomolecules · 2022Review
- Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism.iScience · 2022Article
- The E. coli Whole-Cell Modeling Project.EcoSal Plus · 2021Article
- Addressing uncertainty in genome-scale metabolic model reconstruction and analysis.Genome biology · 2021Review
- Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.Metabolic engineering communications · 2020Review
- Low Entropy Sub-Networks Prevent the Integration of Metabolomic and Transcriptomic Data.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.