Physical and mathematical modeling in experimental papers: achieving robustness of mathematical modeling studies

TL;DR

This paper emphasizes the importance of developing multiple mathematical models and using experimental data to select the most robust ones, especially those challenging existing theories, to enhance the reliability of biological system studies.

Contribution

It advocates for creating alternative models and discriminating among them with experimental data to improve robustness in mathematical modeling of biological systems.

Findings

Developing multiple models enhances robustness.

Challenging existing theories yields more valuable models.

Using experimental data for model discrimination is essential.

Abstract

Development of several alternative mathematical models for the biological system in question and discrimination between such models using experimental data is the best way to robust conclusions. Models which challenge existing theories are more valuable than models which support such theories.