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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Expert-guided multi-objective optimization: An efficient strategy for parameter estimation of biological systems with

Léa Da Costa Fernandes1, David Bernard2, François Pérès3

  • 1Université de Toulouse, CNRS UMR 5070, INSERM U1301, EFS, ENVT, Institut RESTORE, Toulouse, France.

Bio Systems
|January 4, 2026
PubMed
Summary
This summary is machine-generated.

Integrating expert knowledge into biological model calibration using a hybrid framework significantly improves the generation of biologically plausible solutions. This approach enhances model accuracy by combining hard and soft constraints for complex systems.

Keywords:
Model calibration for biologyMulti-objective optimizationSoft constraints

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Modeling

Background:

  • Biological model calibration is complex due to high-dimensional parameter spaces and limited experimental data.
  • Existing methods often struggle with overfitting sparse time-course data.

Purpose of the Study:

  • To develop and evaluate a hybrid calibration framework integrating expert knowledge into multi-objective optimization.
  • To combine hard constraints from measurements with soft constraints from domain expertise.

Main Methods:

  • Proposed a hybrid framework integrating expert knowledge into multi-objective optimization (NSGA-III, MOEA/D, MO-TPE).
  • Utilized a dual-constraint strategy combining hard constraints (biological measurements) and soft constraints (qualitative domain expertise).
  • Evaluated the framework on a skin wound healing model.

Main Results:

  • The hybrid framework significantly increased the proportion of biologically plausible solutions (1.8% to 24.3% for NSGA-III).
  • The dual-constraint strategy reduced overfitting to sparse data by favoring dynamically plausible trajectories.
  • Demonstrated improved performance compared to standard and unconstrained optimization.

Conclusions:

  • The proposed framework offers a flexible, iterative, and generalizable method for calibrating complex biological models.
  • Leveraging domain knowledge through soft constraints is crucial for improving model calibration and biological plausibility.
  • This approach provides a principled way to enhance the reliability of computational models in systems biology.