Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #332150

Research Project: Soybean Seed Improvement Through Quantitative Analysis of Phenotypic Diversity in Response to Environmental Fluctuations

Location: Plant Genetics Research

Title: Crops in silico: Generating virtual crops using an integrative and multi-scale modeling platform

Author
item MARSHALL-COLON, AMY - University Of Illinois
item LONG, STEPHEN - University Of Illinois
item Allen, Douglas - Doug
item ALLEN, GABRIELLE - University Of Illinois
item BEARD, DANIEL - University Of Michigan
item BENES, BEDRICH - Purdue University
item CAEMMERER, SUSANNE VON - Australian National University
item CHRISTENSEN, AJ - University Of Illinois
item COX, DONNA - University Of Illinois
item HART, JOHN - University Of Illinois
item HIRST, PETER - Purdue University
item KANNAN, KAVYA - University Of Illinois
item KATZ, DANIEL - University Of Illinois
item LYNCH, JONATHAN - Pennsylvania State University
item MILLAR, ANDREW - University Of Edinburgh
item PANNEERSELVAN, BALAJI - University Of Illinois
item PRICE, NATHAN - Institute For Systems Biology
item PRUSINKIEWICZ, PRZEMYSLAW - University Of Calgary
item RAILA, DAVID - University Of Illinois
item SHEKAR, RACHEL - University Of Illinois
item SHRIVASTAVA, STUTI - University Of Illinois
item SHUKLA, DIWAKAR - University Of Illinois
item SRINIVASAN, VENKATRAMAN - University Of Illinois
item STITT, MARK - Max Planck Institute Of Molecular Plant Physiology
item TURK, MATTHEW - University Of Illinois
item VOIT, EBERHARD - Georgia Tech
item WANG, YU - University Of Illinois
item YIN, XINYOU - Wageningen University
item ZHU, XINGUANG - Chinese Academy Of Sciences

Submitted to: Frontiers in Plant Science
Publication Type: Review Article
Publication Acceptance Date: 4/26/2017
Publication Date: 5/15/2017
Citation: Marshall-Colon, A., Long, S.P., Allen, D.K., Allen, G.D., Beard, D., Benes, B., Caemmerer, S., Christensen, A., Cox, D.J., Hart, J., Hirst, P., Kannan, K., Katz, D.S., Lynch, J., Millar, A., Panneerselvan, B., Price, N., Prusinkiewicz, P., Raila, D., Shekar, R.G., Shrivastava, S., Shukla, D., Srinivasan, V., Stitt, M., Turk, M.J., Voit, E.O., Wang, Y., Yin, X., Zhu, X. 2017. Crops in silico: Generating virtual crops using an integrative and multi-scale modeling platform. Frontiers in Plant Science. 8:786. doi: 10.3389/fpls.2017.00786.

Interpretive Summary:

Technical Abstract: There are currently 795 million hungry people in the world and 98 percent of them are in developing countries. Food demand is expected to increase by 70% by 2050. With a reduction in arable land, decreases in water availability, and an increasing impact of climate change, innovative technologies are required to sustainably improve crop yield. A biologically informed computational framework is critical to increasing food production under different climate scenarios and resource constraints. Multi-scale models can facilitate whole plant simulations of systems, linking gene networks, protein synthesis, metabolic pathways, physiology, growth, and development. These models have the potential to fill in missing mechanistic details and generate new testable hypotheses for directed engineering efforts. Outcomes from these models will accelerate efforts to improve plant yield and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes, and builds on the achievements in systems modeling of microbial and mammalian organisms. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. Here, we introduce Crops in silico, an integrative and multi-scale modeling platform as a solution to combine isolated modeling efforts toward the generation of a virtual plant, open and accessible to the entire plant biology community.