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Scalable nonlinear programming framework for parameter estimation in dynamic biological system models.

Sungho Shin1, Ophelia S Venturelli1,2, Victor M Zavala1

  • 1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Plos Computational Biology
|March 26, 2019
PubMed
Summary
This summary is machine-generated.

We developed a fast nonlinear programming framework for biological system modeling. This approach efficiently solves complex parameter estimation problems, significantly outperforming existing methods.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Biophysics

Background:

  • Parameter estimation in dynamic biological models is computationally intensive due to complex differential equations and large datasets.
  • Existing simulation-based methods are often slow and inefficient for these challenging problems.

Purpose of the Study:

  • To present a novel nonlinear programming (NLP) framework for scalable and efficient parameter estimation in dynamic biological systems.
  • To demonstrate the framework's computational efficiency, robustness, and ease of use compared to traditional approaches.

Main Methods:

  • A time discretization approach is employed, avoiding repetitive simulations and enabling algebraic model implementation.
  • The framework utilizes efficient nonlinear interior point solvers with sparse linear algebra techniques.
  • Demonstrated on synthetic human gut microbiome community models.

Main Results:

  • Solved a complex instance (156 parameters, 144 ODEs, 1,704 data points) in under 3 minutes, compared to over 7 hours for simulation-based methods.
  • Scalability demonstrated by solving problems up to 2,352 parameters, 2,304 ODEs, and 20,352 data points in under 15 minutes.
  • Framework successfully handled parameter uncertainty quantification, observability diagnostics, model selection, and outlier handling.

Conclusions:

  • The proposed NLP framework offers a computationally efficient and scalable solution for parameter estimation in dynamic biological models.
  • Its flexibility and ease of use allow broad application in systems biology and advanced estimation techniques.
  • This approach significantly accelerates the analysis of complex biological systems.