Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

• Main Authors: Renee Dale, Scott Oswald, Amogh Jalihal, Mary-Francis LaPorte, Daniel M. Fletcher, Allen Hubbard, Shin-Han Shiu, Andrew David Lyle Nelson, Alexander Bucksch
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-07-01
• Series: Frontiers in Plant Science
• Subjects: computational modeling; mathematical modeling; bioinformatics; collaboration; experimental design
• Online Access: https://www.frontiersin.org/articles/10.3389/fpls.2021.687652/full

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.