Multiscale computational models can guide experimentation and targeted measurements for crop improvement
Multiscale computational models can guide experimentation and targeted measurements for crop improvement
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DOI：
10.1111/tpj.14722
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发表时间：
2020-03-31
期刊：
影响因子：
7.2
通讯作者：
Turk, Matthew J.
中科院分区：
文献类型：
--
作者：
Benes, Bedrich;Guan, Kaiyu;Turk, Matthew J.
植物的计算模型已经确定了我们对生物系统理解的空白，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）上模拟特定过程。因此，单尺度模型无法捕捉导致系统涌现特性的关键跨尺度相互作用。在这篇前瞻性文章中，我们认为，为了准确预测植物在未经测试的环境中如何反应，有必要整合跨生物尺度的数学模型。计算模拟从基因组到表型的生物信息流是发现新的实验策略以改善作物的重要一步。一个关键的挑战是连接跨生物，时间和计算（例如CPU与GPU）尺度的模型，然后可视化和解释集成模型输出。我们通过描述国际作物硅财团的努力来解决这一挑战。
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.