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Defining biological networks for noise buffering and signaling sensitivity using approximate Bayesian computation
Shuqiang Wang1, Yanyan Shen2, Changhong Shi3
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China ; Department of Orthopaedics and Traumatology, University of Hong Kong, Hong Kong.
Thescientificworldjournal
|July 5, 2014
Summary
This study identifies three biological circuit motifs that buffer cellular noise while maintaining signal sensitivity. These findings aid in understanding biological information processing and ranking models of cellular signaling pathways.
Area of Science:
- Systems Biology
- Computational Biology
- Cellular Signaling
Background:
- Cellular information processing demands sensitivity to signals and noise resistance.
- Multiple biological circuits can perform similar functions, necessitating model comparison.
- Ranking biological models requires experimental validation to support hypotheses.
Purpose of the Study:
- To identify biological circuits that balance signal sensitivity with noise minimization.
- To rank different biological circuit models based on their functional performance.
- To analyze rapid fluctuations in biological signaling pathways.
Main Methods:
- Utilized approximate Bayesian computation (ABC) with sequential Monte Carlo (SMC).
- Systematically analyzed three-component biological circuits.
- Applied principal component analysis to posterior distributions.
Main Results:
- Identified three basic biological motifs that effectively buffer noise.
- These motifs maintain sensitivity to long-term input signal changes.
- Demonstrated a specific application in yeast nutrient homeostasis control.
Conclusions:
- Identified key biological circuit motifs for robust cellular information processing.
- The ABC-SMC method is effective for ranking and analyzing biological signaling models.
- Understanding noise buffering mechanisms is crucial for cellular function and disease research.
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