StochDecomp--Matlab package for noise decomposition in stochastic biochemical systems
Tomasz Jetka1, Agata Charzyńska, Anna Gambin
1Institute of Fundamental Technological Research, Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, Faculty of Mathematics Informatics and Mechanics, Institute of Informatics, University of Warsaw, Warsaw, Poland and Division of Molecular Biosciences, Imperial College London, London, UK.
Bioinformatics (Oxford, England)
|November 6, 2013
Summary
This study introduces a novel tool to decompose noise in biochemical systems, quantifying individual reaction contributions to cellular variability. This method aids in understanding stochasticity origins and propagation, applicable to pathways like JAK-STAT signaling.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Cellular processes exhibit inherent stochasticity (noise).
- Previous studies offered fragmented theoretical analyses of noise origins and propagation.
- Understanding noise is crucial for deciphering cellular behavior.
Purpose of the Study:
- To present a flexible and widely applicable tool for noise decomposition in biochemical systems.
- To quantify the contribution of individual reactions to overall system variability.
- To enable inference of noise contributions from experimental data.
Main Methods:
- Development of a novel noise decomposition tool.
- Application of the linear noise approximation (LNA).
- Integration with Bayesian parameter inference for data analysis.
Main Results:
- The tool quantifies individual reaction contributions to system output variability.
- Demonstrated application using the JAK-STAT signaling pathway.
- Successful inference of noise contributions from experimental data.
Conclusions:
- The developed tool provides a unified approach to analyzing stochasticity in biochemical systems.
- Enables quantitative understanding of how noise enters and propagates.
- Facilitates data-driven insights into cellular noise through experimental validation.
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