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

职业：自然界的算法：揭示植物结构、生长和适应的原理

• 批准号: 2026342
• 负责人: Saket Navlakha
• 金额: $ 102.68万
• 依托单位: Cold Spring Harbor Laboratory
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026342&HistoricalAwards=false
• 关键词: CAREER; Algorithms; nature; Uncovering; principles

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.

该项目的目标是帮助算法成为描述生物系统使用的解决问题策略的首选语言。算法一直是计算的语言，所有的生物系统都必须计算（即，信息（生存）。因此，“自然界中的算法”的研究可以代表计算机科学和生物学之间的跨学科研究的一个新领域。更具体地说，该项目旨在揭示自然界中分支结构共享的基本网络设计策略和优化原则，包括植物芽（地上）和根（地下）架构，以及大脑中的神经分支乔木。理解进化用来设计这些系统的基本模式具有双向的好处;它可以导致更好地理解这些自然网络如何在健康和疾病中处理信息和功能，并可以导致新的计算策略来构建更好的工程网络。在教育方面，将算法研究作为生命科学课程的一项要求，可以帮助教育下一代跨学科科学家。 这个项目有三个目标。第一个目标是发现植物结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使用帕累托最优理论塑造植物枝条结构的形状，以及如何根据环境和物种优先考虑不同的权衡目标;（2）确定驱动优先级的分子机制（基因）;（3）确定植物枝条使用什么搜索算法来寻找资源和制定生长策略。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型物种进行研究。总的来说，这个目标将把计算机科学中常用的网络设计原理与那些驱动植物网络形成和适应的原理联系起来，并可能有助于设计和评估提高作物产量的育种策略。第二个目标是建立网络战模型来研究植物之间的竞争。这一目标将：（1）创造“角斗士式”竞技场，研究两种植物如何争夺有限的光线;（2）发展博弈论方法，评估是否出现主导或稳定策略;（3）量化竞争策略如何根据两种植物的物种及其生长环境而有所不同。这一目标将导致预测模型的植物社会相互作用，可以通知设计的基础上，植物“获得沿着”最好的多元文化农业空间。第三个目的是测试这些原理对其他生物和工程分支结构的通用性。该目标将测试从植物枝条结构中学习到的分支特性是否也决定了地下植物根结构和大脑中的神经（轴突和树突）结构的结构。例如，根架构也是帕累托最优的吗？他们用什么搜索算法来寻找营养？竞争如何影响这些战略？这也将导致第一次定量比较两个生命王国的分支结构，从植物到神经元。最后，本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。生物学的见解可以揭示战争或自然灾害后受损基础设施的最佳重建，或将现有基础设施扩展到发展中地区的新战略。 网址：http://www.snl.salk.edu/~navlakha/This奖反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

A feedback control principle common to several biological and engineered systems.

• DOI: 10.1098/rsif.2021.0711
• 发表时间: 2022-03
• 期刊: Journal of the Royal Society, Interface
• 作者: Suen JY;Navlakha S
• 通讯作者: Navlakha S

Plant 3D (P3D): a plant phenotyping toolkit for 3D point clouds

• DOI: 10.1093/bioinformatics/btaa220
• 发表时间: 2020-03
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Illia Ziamtsov;Saket Navlakha

Effects of stochastic coding on olfactory discrimination in flies and mice.

• DOI: 10.1371/journal.pbio.3002206
• 发表时间: 2023-10
• 期刊: PLOS BIOLOGY
• 影响因子: 9.8
• 作者: Srinivasan, Shyam;Daste, Simon;Modi, Mehrab N.;Turner, Glenn C.;Fleischmann, Alexander;Navlakha, Saket
• 通讯作者: Navlakha, Saket

Branch-Pipe: Improving Graph Skeletonization around Branch Points in 3D Point Clouds

• DOI: 10.3390/rs13193802
• 发表时间: 2021-09
• 期刊: Remote. Sens.
• 作者: Illia Ziamtsov;Kian Faizi;Saket Navlakha