CAREER: Algorithms in nature: Uncovering principles of plant structure, growth, and adaptation

这个项目的目标是帮助使算法成为描述生物系统使用的问题解决策略的首选语言。长期以来，算法一直是计算的语言，所有生物系统都必须进行计算(即处理信息)才能生存。因此，“自然界中的算法”的研究代表了计算机科学和生物学交叉研究的一个新领域。更具体地说，这个项目试图揭示自然界中分支结构所共有的基本网络设计策略和优化原则，包括植物地上和根(地下)结构，以及大脑中的神经分支乔木。了解进化用来设计这些系统的基本模式有双向的好处；它可以导致对这些自然网络如何处理信息和在健康和疾病中发挥作用的更好理解，还可以导致建立更好的工程网络的新计算策略。在教育方面，将算法研究作为生命科学课程的必修课，有助于培养下一代跨学科科学家。这个项目有三个目标。第一个目标是发现控制植物架构如何生长和适应不断变化的环境的原则。这一目标将：(1)研究不同的网络优化权衡如何利用帕累托最优理论塑造植物地上部结构的形状，以及如何根据环境和物种对不同的权衡目标进行优先排序；(2)确定决定优先顺序的分子机制(基因)；以及(3)确定植物新梢使用什么搜索算法来寻找资源和制定生长战略。这些问题将使用3D激光扫描在多个条件和时间点生长的作物物种(番茄、烟草、高粱、玉米、水稻)以及具有不同遗传背景的模式物种来研究。总体而言，这一目标将把计算机科学中普遍研究的网络设计原则与那些推动植物网络形成和适应的原则联系起来，并可能有助于设计和评估提高作物产量的育种策略。第二个目标是开发网络战模型来研究植物与植物之间的竞争。这一目标将：(1)创建“角斗士风格”的竞技场，以研究两种植物如何争夺有限的光线；(2)开发博弈论方法，以评估是否出现主导或稳定的策略；以及(3)基于两种植物的物种及其生长环境，量化竞争策略的不同。这一目标将导致植物社会相互作用的预测模型，这些模型可以根据哪些植物“相处”得最好，为多文化耕作空间的设计提供信息。第三个目标是测试这些原理对其他生物和工程分支结构的一般性。这一目标将测试从植物地上部结构学到的分枝特性是否也决定了地下植物根部结构和大脑中神经(轴突和树突)结构的结构。例如，根架构是否也是帕累托最优的？他们使用什么搜索算法来寻找营养素？竞争是如何影响这些战略的？这也将导致首次对从植物到神经元的两个生命王国的分支结构进行定量比较。最后，这个目标还将研究人类工程网络，这些网络也必须使其结构适应动态环境中的资源可用性和需求。来自生物学的见解可以揭示战后或自然灾害后最佳重建受损基础设施的新战略，或将现有基础设施扩展到发展中地区。URL：http://www.snl.salk.edu/~navlakha/This奖反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to help make algorithms a preferred language for describing problem-solving strategies used by biological systems. Algorithms have long been the language of computing, and all biological systems must compute (i.e., process information) to survive. Thus, the study of "algorithms in nature" can represent a new field of interdisciplinary research between computer science and biology. More specifically, this project seeks to uncover fundamental network design strategies and optimization principles shared by branching structures in nature, including plant shoot (above-ground) and root (below-ground) architectures, as well as neural branching arbors in the brain. Understanding the basic patterns that evolution has used to design these systems has bi-directional benefits; it can lead to improved understanding of how these natural networks process information and function in both health and disease, and it can lead to new computational strategies for building better engineered networks. Educationally, making the study of algorithms a requirement in life science curricula can help educate the next-generation of interdisciplinary scientists. This project has three Aims. The first Aim is to discover principles governing how plant architectures grow and adapt to changing environments. This Aim will: (1) study how different network optimization trade-offs sculpt the shape of plant shoot architectures using the theory of Pareto optimality, and how different trade-off objectives are prioritized depending on the environment and species; (2) determine the molecular mechanisms (genes) that drive prioritizations; and (3) determine what search algorithms are used by plant shoots to find resources and to strategize growth. These questions will be studied using 3D laser scanning of crop species (tomato, tobacco, sorghum, corn, rice) grown across multiple conditions and time-points, and of model species with different genetic backgrounds. Overall, this Aim will link network design principles commonly studied in computer science with those driving network formation and adaptation in plants, and may help design and evaluate breeding strategies to enhance crop yield. The second Aim is to develop models of network warfare to study plant-plant competition. This Aim will: (1) create "gladiator-style" arenas to study how two plants battle for limited light; (2) develop game theory methods to assess whether dominant or stable strategies emerge; and (3) quantify how competition strategies differ based on the species of the two plants and their growth environment. This Aim will lead to predictive models of plant social interactions that can inform the design of polyculture farming spaces based on which plants "get along" the best. The third Aim is to test the generality of these principles to other biological and engineered branching structures. This Aim will test if the branching properties learned from plant shoot architectures also dictate the structure of plant root architectures below ground and neural (axonal and dendritic) architectures in the brain. For example, are root architectures also Pareto optimal? What search algorithms do they use to find nutrients? How does competition affect these strategies? This will also lead to the first quantitative comparison of branching structures across two kingdoms of life, from plants to neurons. Finally, this Aim will also study human-engineered networks that also must adapt their structure to resource availability and demand in dynamic environments. Insights from biology could reveal new strategies for optimal reconstruction of damaged infrastructure after war or natural disasters, or extension of existing infrastructure into developing areas. URL: http://www.snl.salk.edu/~navlakha/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.