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Differential methods for assessing sensitivity in biological models.

Rachel Mester1, Alfonso Landeros1, Chris Rackauckas2,3,4

  • 1Department of Computational Medicine, University of California Los Angeles, Los Angeles, California, United States of America.

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Summary
This summary is machine-generated.

Differential sensitivity analysis helps fit biological model parameters and forecast experiments. Forward mode automatic differentiation is fastest, while complex perturbation is most generalizable and easiest to implement.

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Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Systems Biology

Background:

  • Differential sensitivity analysis is crucial for parameter fitting, uncertainty quantification, and forecasting in biological models.
  • Selecting the appropriate sensitivity analysis method for a specific biological model can be challenging due to the variety of available techniques.

Purpose of the Study:

  • To elucidate various differential sensitivity analysis methods.
  • To evaluate the utility of these methods in common biological models.
  • To compare the performance of different methods regarding speed, accuracy, and ease of implementation.

Main Methods:

  • Mathematical explanation of adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis.
  • Application of these methods to four distinct biological models: CARRGO (tumor-immune interaction), deterministic SIR, stochastic SIR, and a discrete birth-death-migration model.
  • Comparative assessment of computational time, accuracy, and implementation complexity.

Main Results:

  • Differential sensitivity analysis provides insights beyond traditional methods, as shown in the CARRGO model.
  • Second-order sensitivity analysis enhances predictive accuracy in the deterministic SIR model.
  • Forward mode automatic differentiation offers the fastest computation.
  • Complex perturbation sensitivity analysis is the most straightforward to implement and broadly applicable.

Conclusions:

  • The choice of differential sensitivity analysis method depends on specific model characteristics and research goals.
  • Forward mode automatic differentiation is recommended for speed-critical applications.
  • Complex perturbation analysis is suitable for broader model generalizability and ease of use.