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Rationalised experiment design for parameter estimation with sensitivity clustering.

Harsh Chhajer1, Rahul Roy2,3

  • 1Department of Bioengineering, Indian Institute of Science, Bangalore, 560012, India.

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|October 29, 2024
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Summary
This summary is machine-generated.

We introduce PARameter SEnsitivity Clustering (PARSEC), a novel framework for designing quantitative experiments. PARSEC enhances parameter estimation in complex systems by identifying informative measurements, improving experimental efficiency.

Keywords:
Approximate Bayesian computationClustering-based experiment designInformative experiment designModel fittingParameter sensitivity

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Area of Science:

  • Systems Biology
  • Experimental Design
  • Computational Science

Background:

  • Quantitative experiments are crucial for understanding complex systems.
  • Model-based design of experiments (MBDoE) offers advantages but faces challenges like simplified assumptions and computational demands.
  • Effective experimental design is key to robust scientific investigation.

Purpose of the Study:

  • To present PARameter SEnsitivity Clustering (PARSEC), a new MBDoE framework.
  • To improve the efficiency and accuracy of parameter estimation in complex systems.
  • To provide a method for optimizing experimental sampling strategies.

Main Methods:

  • Developed PARSEC, a framework utilizing parameter sensitivity (PS) clustering to identify informative experimental measurements.
  • Integrated PARSEC with a variant of Approximate Bayesian Computation for automated design assessment and ranking.
  • Applied the framework to two distinct kinetic model systems.

Main Results:

  • PARSEC-based experiments significantly improved parameter estimation in a complex system.
  • The PARSEC framework effectively accounts for experimental constraints and parameter variability.
  • A strong correlation was found between sample size and the optimal number of PS clusters, enabling determination of ideal experimental sampling.

Conclusions:

  • PARSEC offers a robust and computationally efficient approach to MBDoE.
  • Leveraging parameter sensitivity is a validated strategy for optimizing experimental design.
  • The framework has the potential to significantly advance the exploration of experimental design spaces by integrating model architecture and system dynamics.