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Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.

Fabian Fröhlich1,2, Barbara Kaltenbacher3, Fabian J Theis1,2

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Adjoint sensitivity analysis enhances parameter estimation for large biochemical models. This computational method offers improved efficiency and scalability for genome-scale biological process modeling.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Engineering

Background:

  • Ordinary differential equation (ODE) models are crucial for understanding biological processes.
  • Current computational methods struggle with parameter estimation for large-scale ODE models involving thousands of species and reactions.

Purpose of the Study:

  • To evaluate adjoint sensitivity analysis for efficient parameter estimation in large-scale biochemical reaction networks.
  • To compare the computational efficiency and scalability of adjoint sensitivity analysis against existing methods.

Main Methods:

  • Adjoint sensitivity analysis applied to time-discrete measurements in large-scale ODE models.
  • Comparison with state-of-the-art parameter estimation techniques in systems and computational biology.

Main Results:

  • Adjoint sensitivity analysis demonstrates significantly improved computational efficiency and scalability.
  • Computational complexity is largely independent of the number of model parameters.
  • Parameter estimation for a comprehensive ErbB signaling model was achieved in a fraction of the time required by established methods.

Conclusions:

  • Adjoint sensitivity analysis is a computationally efficient and scalable method for parameter estimation in large-scale biochemical models.
  • This approach facilitates mechanistic modeling of genome-scale cellular processes, crucial for the omics era.