Multiscale computational models can guide experimentation and targeted measurements for crop improvement. (31st March 2020)

• Record Type: Journal Article
• Authors: Benes, Bedrich; Guan, Kaiyu; Lang, Meagan; Long, Stephen P.; Lynch, Jonathan P.; Marshall‐Colón, Amy; Peng, Bin; Schnable, James; Sweetlove, Lee J.; Turk, Matthew J.
• Is Part Of: Plant journal. Volume 103:Number 1(2020)
• Page Start: 21
• Page End: 31
• Publication Date: 2020-03-31
• Subjects: photosynthesis -- flux modeling -- whole‐plant architecture -- transcriptional regulation -- multiscale modeling; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.14722
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ‐level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single‐scale models are unable to capture the critical cross‐scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium. Significance Statement: Computational plant models increase our comprehension of biological processes and reveal gaps in knowledge. Integrated, multiscale models have the potential to increase our predictive capability for crop response to future environments. This perspective article highlights the need for multiscale modeling for the development of crop ideotypes, and contends that advanced visualization of multiscale model simulations will guide future efforts for experimental measurement and engineering.