# Parameter Estimation and Uncertainty Quantification for Systems Biology Models

## TL;DR

This paper reviews methods and software tools for parameter estimation and uncertainty quantification in systems biology models, focusing on immune system modeling, and discusses their applications and future prospects.

## Contribution

It provides a comprehensive overview of current techniques and tools for parameter estimation and uncertainty quantification in systems biology, highlighting their relevance to immune system modeling.

## Key findings

- Survey of gradient-based and gradient-free estimation methods
- Discussion of profile likelihood, bootstrapping, Bayesian inference
- Application prospects for immune-related systems biology models

## Abstract

Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.