Multiscale model of exploration-exploitation tradeoff: from genes to collectives

探索-利用权衡的多尺度模型：从基因到集体

• 批准号: 8863779
• 负责人: BRIAN H. SMITH
• 金额: $ 49.81万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8863779
• 关键词: Address; Affect; Age; Alcohol abuse; Alleles; Animal Model; Animals; Back; Bees; Behavior; Behavioral; Biogenic Amine Receptors; Biological; Biological Neural Networks; Biology; Brain; Characteristics; Collection; Complex; Computer Simulation; Control Groups; Controlled Environment; Decision Making; Drug abuse; Environment; Epigenetic Process; Exploratory Behavior; Face; Failure; Feedback; Food; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Genotype; Goals; Honey; Human; Individual; Individual Differences; Insecta; Learning; Link; Military Personnel; Modeling; Monitor; Natural Selections; Neurons; Obesity; Outcome; Performance; Personal Satisfaction; Phenotype; Physiological; Regulation; Regulator Genes; Research; Resources; Risk; Series; Shapes; Social Behavior; Social Network; Solutions; Stimulus; Syndrome; Testing; Time; Translating; Variant; Work; base; behavior influence; design; epigenetic regulation; experience; food resource; gene function; interest; medical specialties; multi-scale modeling; novel; preference; public health relevance; research study; self organization; simulation; social; success; trait

 DESCRIPTION (provided by applicant): Many animals, and particularly humans, depend on social networks for their general well-being and in many cases for their survival. The biomedical impacts of social networks on individuals can have important implications for regulating obesity and drug or alcohol abuse. The effects can also have a large impact on the functioning of any organization, ranging from small to large public and private organizations through military command and control. In many cases the central control of groups is by necessity loose or nonexistent, with the organization arising from sets of rules that each individual employs. It is therefore important to understand how adaptive, collective behavior emerges from a collection of individuals with little or no central control. The research in this proposal is aimed toward understanding group behavior by integrating models with experiments at three biological scales: from gene expression effects in neural networks of the brain, to how those networks affect behavior, to the collective dynamics of a coordinated group of individuals that operates without central control. We will investigate an important problem of group organization from a different perspective than is commonly used. Instead of having individuals who all operate under a common set of rules, as would be true of most agent-based approaches, we propose to study groups composed of individuals who vary in their behavioral rules. The latter condition is more typical of human and many animal groups; because individuals naturally differ in many ways - experience, size, age, etc. - that influence how they respond to various situations. We propose to develop the honey bee as an animal model for this type of work precisely because the survival of any individual in a large (ca 100,000 honey bees) social colony depends on the performance of the group as a whole which operates without central control. Moreover, we can study honey bee biology at multiple organizational scales. We can experimentally manipulate the expression of identified genes, monitor and manipulate neural networks in the brain, and determine the composition of honey bees of different genotypes in the colony. We will focus on how honey bee colonies solve a central problem in looking for food that humans also face. That is, how to allocate resources to exploiting a known resource versus exploring for new resources. Failure to efficiently perform both tasks by the several thousand foraging honey bees risks failure of the colony. We focus on a gene locus that has been repeatedly affiliated with one or another foraging specialty. We propose to investigate how different alleles at this locus influence the behavioral choices of individuals, and then investigate how those individuals are integrated into a colony's strategy for solving this foraging problem. We will use a novel multiscale modeling approach that integrates three biological scales using standard agent-based modeling, mean field approximations, decision making models of the brain, and gene regulatory models. Through a back-and-forth interplay between modeling and experiments, our approach will identify critical parameters that allow groups to face environmental challenges.

 描述（由适用提供）：许多动物，尤其是人类，都取决于社交网络的一般福祉，在许多情况下为其生存。社交网络对个人的生物医学影响可能对确定肥胖，毒品或酗酒的重要意义。这种影响还可能对任何组织的运作产生很大的影响，从小型公共和私人组织通过军事指挥和控制。在许多情况下，群体的中心控制是必要的或不存在的，该组织是由每个员工的一组规则引起的。因此，重要的是要了解几乎没有或没有中央控制的个人的自适应，集体行为是如何出现的。该提案中的研究旨在通过将模型与三个生物学量表的实验整合在一起：从大脑神经网络中的基因表达效应到这些网络如何影响行为，再到没有中心控制的个人运作的个人组的集体动态。我们将从不同的角度研究小组组织的重要问题。我们没有像大多数基于代理的方法那样让所有人都按照一套共同的规则进行操作，而是要研究由在行为规则中变化的个人组成的小组。后一种状况更为典型，是人类和许多动物群体。因为个人在许多方面自然不同 - 经验，大小，年龄等 - 会影响他们对各种情况的反应。我们建议将蜂蜜蜜蜂作为这种工作的动物模型发展，这恰恰是因为任何个人在大型（CA 100,000个蜜蜂）社会殖民地中的存活取决于整个群体的表现，而该群体的整体表现无需中央控制。此外，我们可以在多个组织量表上研究蜂蜜蜜蜂生物学。我们可以通过实验操纵已鉴定的基因的表达，监测和操纵大脑中的中性网络，并确定菌落中不同基因型的蜂蜜蜜蜂的组成。我们将重点关注蜜蜂殖民地如何解决人类面临的食物中的核心问题。也就是说，如何分配资源来利用已知资源而不是探索新资源。未能通过数千个觅食蜜蜂的风险失败而有效地执行这两项任务。我们专注于一个与一个或另一个觅食专业的基因基因座。我们建议研究该基因座的不同等位基因如何影响 个人的行为选择，然后研究如何将这些人纳入殖民地解决这个觅食问题的策略。我们将使用一种新型的多尺度建模方法，该方法使用基于标准代理的建模，平均场近似，大脑的决策模型和基因调节模型整合了三个生物尺度。通过建模和实验之间的来回相互作用，我们的方法将确定允许群体面临环境挑战的关键参数。