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.
Highlights
Generating knowledge requires the integration and contextualization of information: “A collection of facts is no more a science than a heap of stones is a house” (Henri Poincaré)
We address how these types of models are used in different subfields of plant biology, and how pattern and mechanistic mathematical models complement each other (Bucksch et al, 2017; Passot et al, 2019)
We focus on modeling approaches that are under-used in plant biology
Summary
Generating knowledge requires the integration and contextualization of information: “A collection of facts is no more a science than a heap of stones is a house” (Henri Poincaré). Challenges in Computational Plant Biology describing complex systems in a logically consistent and explicit manner using a quantitative framework (Nijhout et al, 2015). Such models can generate testable hypotheses by relating possible mechanisms and relationships to observable, measurable phenomena (Bennett et al, 2019). We address how these types of models are used in different subfields of plant biology, and how pattern and mechanistic mathematical models complement each other (section 2) (Bucksch et al, 2017; Passot et al, 2019). We identify the current challenges and potential solutions to broadening engagement with models in plant biology, such as the required expertise and the difficulty finding modeling collaborators
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