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Parameter Estimation and Uncertainty Quantification for Systems Biology Models.

Eshan D Mitra¹, William S Hlavacek¹

¹Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

Current Opinion in Systems Biology

|July 29, 2020

Summary

This summary is machine-generated.

Mathematical models offer biological insights but need parameterization and uncertainty quantification for accurate predictions. This review covers methods and tools for these essential steps in systems biology and immunology modeling.

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Area of Science:

Computational Biology
Immunology
Mathematical Modeling

Background:

Mathematical models are crucial for understanding complex biological systems like immunoreceptor signaling.
Reliable model predictions depend on accurate parameterization and robust uncertainty quantification.
Existing methods and software tools for these tasks are diverse and require systematic review.

Purpose of the Study:

To review current methods and software tools for parameterization and uncertainty quantification in mathematical modeling.
To provide an overview of techniques applicable to systems-level modeling of immune-related phenomena.
To guide researchers in selecting appropriate methods for reliable quantitative biological insights.

Main Methods:

Reviewed gradient-based and gradient-free optimization techniques for parameter point estimation.
Examined profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification.
Considered the application of these methods in the context of systems biology and immunology.

Main Results:

A comprehensive overview of established and emerging methods for model parameterization is presented.
Various approaches for quantifying parameter uncertainty are discussed, highlighting their strengths and limitations.
The review identifies current and potential future applications in systems-level immune modeling.

Conclusions:

Effective parameterization and uncertainty quantification are critical for the predictive power of mathematical models in biology.
A range of computational tools and statistical methods are available to address these challenges.
The application of these methods is expanding, promising deeper insights into immune system dynamics.