Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform

Amy Marshall-Colon1 Stephen P. Long1,2,3 Douglas K. Allen4 Gabrielle Allen5 Daniel A. Beard6 Bedrich Benes7 Susanne von Caemmerer8 A. J. Christensen9 Donna J. Cox9 John C. Hart10 Peter M. Hirst11 Kavya Kannan1 Daniel S. Katz9 Jonathan P. Lynch12,13 Andrew J. Millar14 Balaji Panneerselvam15 Nathan D. Price16 Przemyslaw Prusinkiewicz17 David Raila9 Rachel G. Shekar2 Stuti Shrivastava1 Diwakar Shukla15 Venkatraman Srinivasan2 Mark Stitt18 Matthew J. Turk19 Eberhard O. Voit20 Yu Wang2 Xinyou Yin21 Xin-Guang Zhu22

1Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
2Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana-Champaign, Urbana, Illinois, United States
3Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States
4United States Department of Agriculture - Agricultural Research Service-Donald Danforth Plant Science Center, St. Louis, Missouri, United States
5Department of Astronomy-College of Education, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
6Department of Molecular & Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States
7Department of Computer Graphics Technology, Purdue University, West Lafayette, Indiana, United States
8ARC Centre of Excellence for Translational Photosynthesis, Research School of Biological Sciences, Australian National University, Acton, ACT, Australia
9National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
10Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
11Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, Indiana, United States
12Department of Plant Science, Pennsylvania State University, University Park, Pennsylvania, United States
13Centre for Plant Integrative Biology, University of Nottingham, Nottingham, United Kingdom
14SynthSys and School of Biological Sciences, Edinburgh University, Edinburgh, United Kingdom
15Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
16Institute for Systems Biology, Seattle, Washington, United States
17Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
18Max Planck Institute of Molecular Plant Physiology, Golm, Germany
19School of Information Science, University of Illinois, Urbana-Champaign, Urbana, Illinois, United States
20The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, United States
21Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, Netherlands
22CAS Key Laboratory for Computational Biology-State Key Laboratory for Hybrid Rice, Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China

Abstract

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, 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. 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. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

Reference

Amy Marshall-Colon, Stephen P. Longsup, Douglas K. Allen, Gabrielle Allen, Daniel A. Beard, Bedrich Benes, Susanne von Caemmerer, A. J. Christensen, Donna J. Cox, John C. Hart, Peter M. Hirst, Kavya Kannan, Daniel S. Katz, Jonathan P. Lynchsup, Andrew J. Millar, Balaji Panneerselvam, Nathan D. Price, Przemyslaw Prusinkiewicz, David Raila, Rachel G. Shekar, Stuti Shrivastava, Diwakar Shukla, Venkatraman Srinivasan, Mark Stitt, Matthew J. Turk, Eberhard O. Voit, Yu Wang, Xinyou Yin, and Xin-Guang Zhu. Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform. Frontiers in Plant Science Vol. 8, Article 786, May 2017, pp. 1-7.

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