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Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
Published on: April 27, 2021
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Nonlinear biochemical signal processing via noise propagation.
Kyung Hyuk Kim1, Hong Qian, Herbert M Sauro
1Department of Bioengineering, University of Washington, William H. Foege Building, Box 355061, Seattle, Washington 98195, USA.
The Journal of Chemical Physics
|October 15, 2013
Summary
Cellular noise, arising from low molecule counts, significantly impacts cell behavior. This study introduces a quantitative method to analyze noise propagation, revealing its dual effect on sensitivity and enabling the design of novel biochemical signal processors.
Area of Science:
- Biochemistry
- Systems Biology
- Cellular Biophysics
Background:
- Single-cell studies reveal phenotypic variability due to stochastic intracellular biochemical reactions.
- Low molecule abundance amplifies biochemical fluctuations, or "noise," which propagates through regulatory networks.
Purpose of the Study:
- To develop a quantitative method for analyzing how noise affects cellular phenotypes by identifying system nonlinearities and noise propagation pathways.
- To explore the dual role of noise in enhancing or reducing cellular sensitivities.
- To design novel biochemical signal processing modules engineered using noise-induced phenomena.
Main Methods:
- Developed an intuitive, quantitative method to analyze noise effects on cellular phenotypes.
- Identified system nonlinearities and noise propagation characteristics.
- Designed and analyzed three biochemical signal processing modules based on noise-induced effects.
Main Results:
- Noise can simultaneously enhance sensitivity in one cellular response region while reducing it in another.
- Designed a gene regulatory network functioning as a concentration detector with enhanced amplitude and sensitivity.
- Engineered a non-cooperative positive feedback system that becomes a bistable switch due to noise-induced ultra-sensitivity.
- Developed a noise-induced linear amplifier for gene regulation without requiring feedback.
Conclusions:
- Noise propagation in biochemical networks can be quantitatively analyzed to understand its impact on cellular phenotypes.
- Noise can be harnessed to engineer novel biochemical signal processors with enhanced functionalities, such as concentration detection, bistable switching, and linear amplification.
- This work provides a framework for understanding and designing nonlinear biochemical signal processors based on fluctuation-induced phenotypes.
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