Table 1.

Top misunderstandings on modelling and how to fight them.

Mathematical models cannot reproduce the complexity of biology. Cells and biochemical systems are governed by the same laws as any other physicochemical system, most of which are successfully simulated with mathematical models (see, for example, computer model-based weather and climate prediction).

Your model is not physiological. The real system is more complex than your mathematical model. A model must include the elements, interactions, and processes necessary to investigate the hypothesis in question (nothing else), and this is equally valid for mathematical and lab bench models.

You should employ data in your mathematical model. A well-formulated mathematical model is based on quantitative data taken from databases and published reports or produced for calibration of the model. Also, one can use qualitative model analysis to formulate hypotheses and propose experiments; here, the experimental data comes after the model.

Your predictions are not experimentally validated. Model derivation and simulation can be temporally detached from experimentation: a researcher can investigate a hypothesis with mathematical modelling and can leave the experimental validation for another team after publication of the model-based predictions.

I do not see the clinical relevance of your predictions. There are mathematical models that are of mandatory use in biomedicine (see pharmocokinetics models for drug approval). However, mathematical modelling is in most cases theoretical research. Similar to any other basic research approach, it has an unpredictable long-term potential for enhancing clinical practice.