Observability of Complex Systems: Finding the Gap

Area of Science:

• Systems and Control Sciences
• Computational Biology
• Biotechnology

Background:

• Reconstructing state and parameter values in dynamic system models requires assessing unique determinability from data.
• This property, known as observability or identifiability, is challenging for large, modern models.

Purpose of the Study:

• To demonstrate a recently developed, efficient algorithm for assessing model observability.
• To overcome the difficulties in analyzing observability for large-scale dynamic systems.

Main Methods:

• Application of a novel, efficient algorithm for observability analysis.
• Testing the algorithm on complex biological models, including Chinese Hamster Ovary Cell, JAK-STAT signaling, and MAP Kinase models.

Main Results:

• The algorithm efficiently established observability analysis for models with numerous states and parameters.
• Successful application to Chinese Hamster Ovary Cell (34 states, 117 parameters), JAK-STAT signaling (31 states, 51 parameters), and MAP Kinase (100 states, 88 parameters) models.
• Observability analysis was completed in a remarkably short time.

Conclusions:

• The demonstrated algorithm provides an efficient solution for observability analysis in large dynamic system models.
• This approach significantly simplifies and accelerates the assessment of model properties crucial for data-driven reconstruction.