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Robust control of uncertain context-sensitive probabilistic Boolean networks.
S Z Denic1, B Vasic, C D Charalambous
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA. sdenic@ece.arizona.edu
IET Systems Biology
|July 31, 2009
Summary
This study introduces a risk-sensitive control paradigm to manage uncertainty in gene regulatory networks (GRNs). This approach enhances model robustness by minimizing exponential costs, unlike traditional risk-neutral methods.
Area of Science:
- Systems Biology
- Control Theory
- Computational Biology
Background:
- Gene regulatory networks (GRNs) are inherently uncertain due to measurement errors and model limitations.
- Model inaccuracies can lead to false control signals, potentially causing undesirable cellular states.
Purpose of the Study:
- To propose a risk-sensitive control paradigm for robust control of GRNs under uncertainty.
- To address the limitations of traditional risk-neutral control methods in GRN applications.
Main Methods:
- Developed an optimal risk-sensitive controller for GRNs modeled as context-sensitive probabilistic Boolean networks (CSPBNs).
- Utilized the relationship between relative entropy and free-energy to analyze controller robustness.
- Investigated the influence of specific attractors on controller robustness.
Main Results:
- Demonstrated the relative stability of the risk-sensitive controller's cost under perturbed CSPBN attractor distributions.
- Showcased that the risk-neutral controller's cost increases under similar perturbations.
- Validated the efficiency of the risk-sensitive controller using a CSPBN model from malignant melanoma research.
Conclusions:
- Risk-sensitive control offers a robust solution for managing uncertainty in GRN models.
- The proposed method provides enhanced stability compared to risk-neutral approaches, particularly in dynamic biological systems.
- This approach holds promise for applications in areas like cancer research.
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