Opening Pandora's box: caveats with using toolbox-based approaches in mathematical modeling in biology

Area of Science:

• Mathematical Biology
• Systems Biology
• Computational Biology

Background:

• Mathematical modeling is crucial for understanding biological systems, involving analytical and numerical analyses.
• The field has grown in complexity, leading to new analysis and data-comparison tools.
• These tools include sensitivity analyses, information criteria (AIC/BIC), and mixed-effect modeling.

Purpose of the Study:

• To critically evaluate the impact of "toolbox" approaches on mathematical modeling in biology.
• To argue that these automated methods can obscure fundamental understanding of model behavior.
• To advocate for a return to intuitive insights in mathematical biology.

Main Methods:

• Philosophical analysis of modeling principles.
• Illustrative examples of limitations in toolbox-based approaches.
• Discussion on the balance between automation and insight generation.

Main Results:

• Over-reliance on automated analysis tools can negatively impact the core goal of understanding biological models.
• Toolbox approaches may hinder the generation of deep insights into system dynamics.
• Current methods can lead to a mechanical application of modeling techniques.

Conclusions:

• Mathematical modeling in biology should prioritize intuitive understanding alongside automated analyses.
• A balance is needed to ensure that "toolbox" methods enhance, rather than replace, fundamental insights.
• Recognizing mathematical modeling as an art is key to generating meaningful biological discoveries.