Collaborative Research: Computational Models for Neuroendocrine Control of Social Behavior

合作研究：社会行为神经内分泌控制的计算模型

• 批准号: 1257815
• 负责人: Matthias Scheutz
• 金额: $ 32.5万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1257815&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Models; Neuroendocrine

The long-term objective of this research project is to identify the interactions among chemical messengers that control behaviors. Neuropeptides regulate social behaviors in representative species from all vertebrate classes. Species differences in behavior, however, have made development of clear mechanistic models difficult. This project investigates the complexity of behavioral mechanisms using a combination of animal experiments and computational models. Field and laboratory studies will be combined to address important emerging questions on neuropeptide modulation of male vertebrate behaviors. The agent-based modeling approach is especially well-suited to address this problem. Synthetic experiments with the model allow for exploration of conditions impractical or impossible in empirical tests. The model is expected to propose novel mechanisms underlying neuropeptide control of behavior. This project thus implements a tightly controlled experiment-model-experiment loop and develops a new paradigm for a systems biology approach towards understanding social behavior. Neuropeptides alter a variety of vertebrate social behaviors, including parental, aggressive, and reproductive behaviors. Conserved behaviors and chemical messengers will make the empirical results and the model broadly applicable to vertebrates. Results will significantly advance our understanding of the effect of neuropeptides on vertebrate behavioral plasticity. This project also includes a significant training component, with involvement of an undergraduate field biology class (with a 30% minority makeup) and training of graduate students in a unique inter-disciplinary environment. The data management plan includes deposition of new software at SourceForge and archiving of digital data in the PI's institutional databases.

该研究项目的长期目标是确定控制行为的化学信使之间的相互作用。 神经肽调节所有脊椎动物类别的代表性物种的社会行为。 但是，行为上的物种差异使开发清晰的机械模型变得困难。 该项目使用动物实验和计算模型的组合研究了行为机制的复杂性。 现场和实验室研究将合并，以解决有关男性脊椎动物行为的神经肽调节的重要问题。基于代理的建模方法特别适合解决此问题。与该模型的合成实验可以探索经验测试中不切实际或不可能的条件。 预计该模型将提出神经肽对行为控制的新机制。 因此，该项目实现了严格控制的实验模型循环，并为了解社会行为的系统生物学方法开发了新的范式。 神经肽会改变各种脊椎动物的社会行为，包括父母，侵略性和生殖行为。 保守的行为和化学使者将使经验结果和模型广泛适用于脊椎动物。 结果将大大提高我们对神经肽对脊椎动物行为可塑性的影响的理解。该项目还包括一个重要的培训组成部分，并涉及本科生野外生物学课（少数群体的30％）以及在独特的跨学科环境中对研究生的培训。数据管理计划包括在SourceForge的新软件和PI机构数据库中数字数据的存档。