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SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology.

Tobias Newmiwaka1, Benjamin Engelhardt1,2,3, Philipp Wendland1

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The SEEDS R-package helps biological modelers identify structural errors and unknown environmental inputs. This improves mechanistic modeling accuracy and experimental design for biological systems.

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Area of Science:

  • Systems biology
  • Computational biology
  • Bioinformatics

Background:

  • Dynamic models, often ordinary differential equations, are crucial for understanding biological systems.
  • Challenges in mechanistic modeling include inaccurate knowledge of interactions and unknown environmental inputs.
  • Effective interventions and experimental design rely on accurate biological models.

Purpose of the Study:

  • To introduce the R-package SEEDS, which implements algorithms for inferring structural model errors and unknown inputs.
  • To aid researchers in addressing misfits between models and experimental data.
  • To enhance the efficiency of model recalibration and experimental design.

Main Methods:

  • The SEEDS R-package utilizes two novel algorithms.
  • These algorithms infer structural model errors from output measurements.
  • Unknown environmental inputs are also identified using these algorithms.

Main Results:

  • SEEDS facilitates the identification of structural discrepancies in dynamic biological models.
  • The package helps uncover unmeasured inputs affecting biological systems.
  • This leads to more accurate mechanistic models and better-informed experimental designs.

Conclusions:

  • The SEEDS R-package provides essential tools for improving mechanistic modeling in biology.
  • By addressing model errors and unknown inputs, SEEDS enhances the reliability of biological system analysis.
  • This supports more effective targeted interventions and future research directions.