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Published on: April 27, 2021
Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors
V Ibarra-Junquera1, L A Torres, H C Rosu
1Potosinian Institute of Science and Technology, San Luis Potosí, Mexico. vrani@ipicyt.edu.mx
Summary
Nonlinear control techniques, applied to genetic regulation, can accurately reconstruct biological system concentrations despite noise and model uncertainties. This method effectively filters noisy data, enhancing understanding of gene expression dynamics.
Area of Science:
- Systems Biology
- Biotechnology
- Chemical Engineering
Background:
- Nonlinear control techniques are standard in chemical engineering.
- Genetic regulation involves complex dynamics often obscured by experimental noise and model uncertainties.
Purpose of the Study:
- To adapt nonlinear control techniques, using software sensors, for analyzing genetic regulation processes.
- To demonstrate the robustness of these methods against noise and model errors.
Main Methods:
- Implementation of nonlinear control with a software sensor for genetic regulation.
- Inclusion of additive white Gaussian noise and model errors to simulate realistic experimental conditions.
- Application to Goodwin dynamics for single gene and prokaryotic operon systems.
Main Results:
- Successful reconstruction of non-measured concentrations (mRNA, protein, metabolite) despite uncertainties in regulation functions or unknown mRNA dynamics.
- Demonstration that concentration rebuilding is unaffected by additive white Gaussian noise.
- Effective filtering of noisy biological system outputs.
Conclusions:
- Nonlinear control techniques offer a powerful approach for analyzing and understanding complex genetic regulatory networks.
- Software sensors can reliably estimate unmeasured biological parameters even with incomplete system knowledge and noisy data.
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