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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.