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Updated: Apr 29, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Leveraging mathematical models to predict and control T-cell activation.

Xabier Rey Barreiro^1,2, Jose Faro^3,4, Alejandro F Villaverde^1,2

¹CITMAga, Santiago de Compostela, Galicia, Spain.

Plos Computational Biology

|April 27, 2026

Summary

This summary is machine-generated.

Mathematical models of T-cell receptor (TCR)-mediated T-cell activation are crucial for understanding adaptive immunity. This study assesses the parameter identifiability and sensitivity of existing TCR activation models to gauge their predictive power for therapeutic applications.

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Area of Science:

Immunology
Computational Biology
Systems Biology

Background:

T-cell receptor (TCR)-mediated T-cell activation is central to adaptive immunity.
Mathematical models are used to describe the complex kinetics of antigen-TCR interactions and T-cell activation.
Assessing the predictive capabilities of these models is essential before therapeutic application.

Purpose of the Study:

To evaluate the parameter identifiability and sensitivity of published mathematical models of TCR-based T-cell activation.
To determine how different experimental output quantities influence model assessment.
To establish the reliability of these models for predicting and controlling T-cell activation.

Main Methods:

Analysis of parameter identifiability for existing TCR activation models.
Sensitivity analysis of model parameters using various experimental output quantities.
Comparison of model performance across different experimental scenarios.

Main Results:

The study systematically examines the identifiability and sensitivity of parameters within TCR activation models.
The availability of specific experimental data significantly impacts the models' predictive accuracy.
Identifiability and sensitivity analyses reveal the strengths and limitations of each model.

Conclusions:

Parameter identifiability and sensitivity analyses are critical for validating TCR activation models.
Model reliability for predicting T-cell activation varies depending on the data used for fitting.
This work provides a framework for selecting and refining models for therapeutic target development.