Of (Biological) Models and Simulations

TL;DR

The paper discusses the importance, challenges, and philosophical aspects of modeling and simulation in biology, emphasizing their role in scientific understanding and the need for proper validation and application.

Contribution

It advocates for viewing biological modeling as a fundamental learning process and highlights the significance of simulation as an essential scientific tool.

Findings

Simulation is often the only experimental method in biology.

Model validation and verification are crucial for representativeness.

Failures in modeling often stem from incorrect knowledge or misuse of models.

Abstract

Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is modeling; hence, arriving at a contradiction if models are not useful. Representing biological systems through mathematical models can be difficult but the modeling procedure is a process in itself that follows a semi-formal set of rules. Although seldom reported, failure in modeling is not a rare event but I argue that this is usually a result of erroneous underlying knowledge or misapplication of a model beyond its intended purpose. I argue that in many biological studies, simulation is the only experimental tool. In others, simulation is a means of reducing possible combinations of experimental work; thereby, presenting an economical case for simulation; thus, worthwhile to engage in this endeavor. The representativeness of simulation depends on the validation, verification, assumptions, and limitations of the underlying model. This will be illustrated using the inter-relationship between population, samples, probability theory, and statistics.