Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

Abstract

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.

Coupling non‐invasive imaging and reactive transport modeling to investigate water and oxygen dynamics in the root zone

“…As highlighted by Roose et al. (2016), the combination of imaging and modeling allows model generation based on real data, with the caveat that such models might always be specifically adapted to a certain experimental system (Dale et al., 2021). For this work specifically, we found that inclusion of bulk soil heterogeneity was necessary to characterize the experimental system using an RT model, and that doing so increased our understanding of the dynamics of water redistribution and O 2 transport as a function of root uptake.…”

Transporter networks can serve plant cells as nutrient sensors and mimic transceptor-like behavior

“…Mathematical modeling has proven very powerful in providing unprecedented insights into physiological mechanisms ( Dale et al., 2021 ). Here, we continued our analyses of the properties of transport networks.…”

Specialty grand challenge in plant biophysics and modeling

“…The fundamental basis for the correct description of complex biological systems, such as we find in plants, is the mathematical representation of the individual entities. Only in this way is it possible to use computers to simulate the dynamics of the systems in mechanistic mathematical models (Dale et al, 2021). Such models are often designed to mimic the physiological reality in order to support experimental data (see for instance, Chen et al, 2012;Hills et al, 2012;Blatt et al, 2014;Morris, 2018;Holzheu and Kummer, 2019;Iosip et al, 2020;Huang et al, 2021;Jezek et al, 2021).…”

“…They therefore contribute to the efficient use of available resources for research. Nevertheless, despite the huge potential of mechanistic mathematical models and computational cell biology simulations, there are significant obstacles to implementing these approaches (Dale et al, 2021). Some of the biggest challenges are outlined in the following.…”