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

基本信息

批准号：
9129709
负责人：
BRIAN H. SMITH
金额：
$ 47.35万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
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/9129709
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 Health 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 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 research study self organization simulation social success trait

项目摘要

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（由申请人提供）：许多动物，尤其是人类，依赖社交网络来维持其整体健康，在许多情况下还依赖于其生存。社交网络对个体的生物医学影响可能对调节肥胖和药物或药物产生重要影响。酗酒的影响也可能对任何组织的运作产生重大影响，从小型到大型公共和私人组织，通过军事指挥和控制在许多情况下，对团体的中央控制必然是松散的或不存在的。组织源于一套规则，每个人因此，了解适应性集体行为如何从很少或没有中央控制的个体中产生是很重要的，本提案的研究旨在通过与三个生物尺度的实验相结合的模型来理解群体行为：从基因表达。大脑神经网络的影响，这些网络如何影响行为，在没有中央控制的情况下协调运作的个体的集体动力学，我们将从与通常使用的不同的角度研究团体组织的重要问题。让每个人都在一套共同的规则下运作，就像对于大多数基于主体的方法来说，我们建议研究由行为规则不同的个体组成的群体，后者在人类和许多动物群体中更为典型，因为在很多方面都有所不同——经验、体型、年龄、等等——这会影响它们对各种情况的反应，我们建议开发蜜蜂作为此类工作的动物模型，因为任何个体在大型（约 100,000 只蜜蜂）社会群体中的生存都取决于表现。整个集团的运营此外，我们可以在多个组织尺度上研究蜜蜂生物学，我们可以通过实验操纵已识别基因的表达，监测和操纵大脑中的神经网络，并确定蜂群中不同基因型的蜜蜂的组成。我们将重点关注蜜蜂群体如何解决人类在寻找食物时也面临的一个核心问题，即如何分配资源来开发已知资源，而不是通过数千次觅食来有效地执行这两项任务。蜜蜂面临着失败的风险我们重点关注与一个或另一个觅食专业反复相关的基因位点，我们建议研究该位点的不同等位基因如何影响。我们将使用一种新颖的多尺度建模方法，该方法使用基于标准代理的建模、平均场近似和决策来集成三个生物尺度。通过建模和实验之间的来回相互作用，我们的方法将确定使群体能够应对环境挑战的关键参数。