Related Experiment Video
Updated: Jun 29, 2025
07:41
A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
Published on: February 4, 2017
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Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.
Teddy Groves1, Nicholas Luke Cowie1, Lars Keld Nielsen1,2
1The Novo Nordisk Foundation Center for Biosustainability, DTU, Kongens Lyngby 2800, Denmark.
ACS Synthetic Biology
|April 5, 2024
Summary
Maud is a new tool for Bayesian statistical inference in biochemical metabolic networks. It integrates omics data and biological knowledge for robust kinetic model analysis.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Kinetic models of biochemical networks are crucial for understanding cellular processes.
- Analyzing these models requires sophisticated statistical inference methods.
- Existing tools may not fully integrate diverse data types and prior knowledge.
Conclusions:
- Maud offers a powerful and flexible platform for analyzing biochemical metabolic networks.
- The tool enhances the understanding of metabolic processes by leveraging diverse data sources.
- Maud represents a significant advancement in computational systems biology tools.
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