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Observability of Complex Systems: Finding the Gap.

J D Stigter1, D Joubert2, J Molenaar2

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Determining if dynamic system model values are uniquely identifiable from data is crucial. A new, efficient algorithm successfully addresses this challenge for large-scale models, proving its utility in systems and control sciences.

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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.