Abstract
Modeling has become a popular tool for inquiry and discovery across biological disciplines. Models allow biologists to probe complex questions and to guide experimentation. Modeling literacy among biologists, however, has not always kept pace with the rise in popularity of these techniques and the relevant advances in modeling theory. The result is a lack of understanding that inhibits communication and ultimately, progress in data gathering and analysis. In an effort to help bridge this gap, we present a blueprint that will empower biologists to interrogate and apply models in their field. We demonstrate the applicability of this blueprint in two case studies from distinct subdisciplines of biology; developmental-biomechanics and evolutionary biology. The models used in these fields vary from summarizing dynamical mechanisms to making statistical inferences, demonstrating the breadth of the utility of models to explore biological phenomena.
Highlights
Biological systems represent nested hierarchies of complex patterns and processes
When a model is ready for publishing, it is critical to detail experimental materials and methods such that readers can reproduce the presented results
Reviewers and readers should have a clear explanation of the theoretical equations and the implementation of their numerical approximations
Summary
Biological systems represent nested hierarchies of complex patterns and processes. Models help expose the function of complex systems: For example, by representing processes on the sub-phenotype scale, models can reveal emergent properties of a system such as tissue patterning that occurs despite local cellular variability (Hong et al, 2016). Modeling can reveal the source of counterintuitive effects; for example, a gene promoting expression of its own negative regulator to obtain stable expression in a feedback loop (Alon, 2007; Ding et al, 2020). Models can use statistical inference to discover patterns in large datasets like inferring evolutionary phylogenies or gene regulatory networks (Beaulieu and Donoghue, 2013). With modern datasets getting ever larger, biologists have turned to advanced statistical methods and modeling approaches to process and interpret these large datasets
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