TY - JOUR

T1 - Multiscale computational models can guide experimentation and targeted measurements for crop improvement

AU - Benes, B.

AU - Guan, K.

AU - Lang, M.

AU - Long, S.P.

AU - Lynch, J.P.

AU - Marshall-Colón, A.

AU - Peng, B.

AU - Schnable, J.

AU - Sweetlove, L.J.

AU - Turk, M.J.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - 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.

AB - 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.

KW - flux modeling

KW - multiscale modeling

KW - photosynthesis

KW - transcriptional regulation

KW - whole-plant architecture

KW - Biological organs

KW - Computation theory

KW - Computational methods

KW - Crops

KW - Photosynthesis

KW - Biological information

KW - Experimental strategy

KW - Flux model

KW - Integrated modeling

KW - Multi-scale Modeling

KW - Multiscale computational model

KW - Transcriptional regulation

KW - Whole plants

KW - Biological systems

U2 - 10.1111/tpj.14722

DO - 10.1111/tpj.14722

M3 - Journal article

VL - 103

SP - 21

EP - 31

JO - The Plant Journal

JF - The Plant Journal

SN - 0960-7412

IS - 1

ER -