Computer Graphics Technology and Computer Science, Purdue University, Knoy Hall of Technology, West Lafayette, IN, 47906, USA.
College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA.
Plant J. 2020 Jul;103(1):21-31. doi: 10.1111/tpj.14722. Epub 2020 Mar 31.
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.
植物计算模型已经发现了我们对生物系统理解上的差距，并揭示了优化细胞过程或器官水平结构以提高生产力的方法。因此，计算模型是帮助指导实验和测量的学习工具。模型是复杂系统的简化，通常在单一尺度（例如时间、空间、组织等）上模拟特定的过程。因此，单一尺度模型无法捕捉到导致系统出现新属性的关键跨尺度相互作用。在这篇观点文章中，我们认为，要准确预测植物在未经测试的环境中的反应，有必要在不同的生物尺度上整合数学模型。从基因组到表型模拟生物信息流是发现改善作物的新实验策略的重要步骤。一个关键的挑战是在生物、时间和计算（例如 CPU 与 GPU）尺度上连接模型，然后可视化和解释整合模型的输出。我们通过描述国际作物信息学联盟的努力来应对这一挑战。
