Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

Area of Science:

• Plant biology
• Computational biology
• Systems biology

Background:

• Computational modeling is crucial for complex biological systems but underutilized in plant biology.
• Plant biologists face challenges in adopting modeling methods, especially mechanistic mathematical modeling.
• Existing approaches often lack accessible integration of diverse modeling techniques.

Purpose of the Study:

• To address challenges limiting the adoption of computational modeling in plant biology.
• To categorize modeling approaches into pattern models and mechanistic mathematical models.
• To provide recommendations and resources for increased modeling integration in plant research.

Main Methods:

• Categorization of computational modeling into pattern-based (e.g., bioinformatics, machine learning) and mechanistic mathematical models (e.g., biochemical, biophysical, population models).
• Analysis of barriers to modeling adoption, particularly mathematical and quantitative methods.
• Development of recommendations for non-specialists and interdisciplinary collaboration.

Main Results:

• Computational modeling, encompassing pattern and mechanistic types, offers diverse applications across scales in plant biology.
• Mathematical and quantitative aspects present a significant hurdle for many plant biologists.
• Accessible tools and collaborative strategies can facilitate the adoption of modeling.

Conclusions:

• Increased adoption of computational modeling, particularly mechanistic mathematical modeling, can drive interdisciplinary and transformative research in plant biology.
• Bridging the gap between plant biology and quantitative sciences requires accessible tools and collaborative frameworks.
• A comprehensive understanding of different modeling paradigms is essential for advancing plant science research.