Estimation and discrimination of stochastic biochemical circuits from time-lapse microscopy data

Area of Science:

• Systems Biology
• Synthetic Biology
• Biophysics

Background:

• Current cellular behavior observation methods are limited by sensor capabilities, hindering systems and synthetic biology research.
• Time-lapse microscopy provides valuable data but often suffers from limited sensor information for precise state estimation.

Purpose of the Study:

• To propose a generalized observer structure for estimating the state of stochastic chemical reaction networks from limited microscopy data.
• To incorporate cell division effects into the observer for dynamic state estimation in cell colonies.
• To enable model discrimination by treating model indices as non-time-varying states.

Main Methods:

• Mathematical derivation of an observer structure tailored for time-lapse microscopy of growing cells.
• Inclusion of cell division dynamics within the observer framework.
• Development of conditions for distinguishing continuous-time Markov chain models under different observation schemes.

Main Results:

• A novel observer structure was derived to estimate dynamically changing cellular states from limited sensor data.
• The observer successfully incorporates cell division and can discriminate between different biological models.
• Performance was validated using the Thattai-van Oudenaarden model, showing effectiveness with well-parameterized systems.

Conclusions:

• The developed observer provides a generalized solution for estimating cellular states from microscopy, addressing limitations of current sensing technologies.
• The observer's ability to discriminate models holds promise for advancing synthetic biology, particularly with standardized biological parts.
• Further research is needed to create computationally efficient approximations for broader applicability.