Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

Dale, Renee; Oswald, Scott; Jalihal, Amogh; LaPorte, Mary-Francis; Fletcher, Daniel M.; Hubbard, Allen; Shiu, Shin-Han; Nelson, Andrew Lyle; Bucksch, Alexander

doi:10.3389/fpls.2021.687652

Title: Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling

Journal Article · 20 July 2021 · Frontiers in Plant Science

DOI: https://doi.org/10.3389/fpls.2021.687652 · OSTI ID:1827198

Dale, Renee ^[1]; Oswald, Scott ^[2]; Jalihal, Amogh ^[3]; LaPorte, Mary-Francis ^[4]; Fletcher, Daniel M. ^[5]; Hubbard, Allen ^[6]; Shiu, Shin-Han ^[7]; Nelson, Andrew Lyle ^[8]; Bucksch, Alexander ^[2]

Donald Danforth Plant Science Center, St. Louis, MO (United States); Donald Danforth Plant Science Center
Univ. of Georgia, Athens, GA (United States)
Harvard Medical School, Boston, MA (United States)
Univ. of California, Davis, CA (United States)
Univ. of Southampton (United Kingdom)
Donald Danforth Plant Science Center, St. Louis, MO (United States)
Michigan State Univ., East Lansing, MI (United States)
Cornell Univ., Ithaca, NY (United States)

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.

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Research Organization:: Donald Danforth Plant Science Center, St. Louis, MO (United States); Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States); Krell Institute, Ames, IA (United States); Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: European Research Council (ERC); National Science Foundation (NSF); USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AR0000821; SC0018277; SC0018409; SC0021110

OSTI ID:: 1827198

Journal Information:: Frontiers in Plant Science, Journal Name: Frontiers in Plant Science Vol. 12; ISSN 1664-462X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Cooperation through Competition—Dynamics and Microeconomics of a Minimal Nutrient Trade System in Arbuscular Mycorrhizal Symbiosis Schott, Stephan; Valdebenito, Braulio; Bustos, Daniel Frontiers in Plant Science, Vol. 7 https://doi.org/10.3389/fpls.2016.00912	journal	June 2016
Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform Marshall-Colon, Amy; Long, Stephen P.; Allen, Douglas K. Frontiers in Plant Science, Vol. 8 https://doi.org/10.3389/fpls.2017.00786	journal	May 2017
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data Pratapa, Aditya; Jalihal, Amogh; Law, Jeffrey Zenodo https://doi.org/10.5281/zenodo.3466110	dataset	January 2019

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