Multiscale Modeling of the Mammalian Circadian Clock: The Role of GABA Signaling

哺乳动物昼夜节律钟的多尺度建模:GABA 信号传导的作用

基本信息

  • 批准号:
    9352333
  • 负责人:
  • 金额:
    $ 44.43万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-09-15 至 2020-06-30
  • 项目状态:
    已结题

项目摘要

 DESCRIPTION (provided by applicant): The synchronization and entrainment of coupled biological oscillators is an emerging research area in complex network systems. The mammalian circadian clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus consists of approximately 20,000 pacemaker neurons that are coupled together to produce a robust overall rhythm that drives other bodily functions such as sleep patterns. The SCN represents an ideal model system for studying biological network design and behavior due to accumulating data on individual SCN neurons and their interactions. Experimental studies have shown that SCN intercellular communication is primarily mediated by two neurotransmitters: vasoactive intestinal peptide (VIP) and -aminobutyric acid (GABA). While VIP is well established as an essential synchronizing agent, the role of GABA with respect to its inhibitory/excitatory, day/night, synchronizing and entrainment effects remains controversial. Improved understanding of neurotransmitter mediated intercellular signaling in the SCN will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders and metabolic diseases. The goal of this project is to develop a multiscale model of the SCN and to integrate this model with targeted experiments and novel computational tools to gain improved understanding of SCN connectivity, synchronization and entrainment properties. The research focuses on GABA signaling because its role in the SCN is prominent, not well understood, and recent advances by the three participating investigators will enable a complete and careful dissection of the role of this common neurotransmitter with synapse-level resolution across large arrays of circadian neurons. The multiscale model will establish a link between core clock genes and ion channels at the individual cell level and network synchronization and entrainment behavior at the SCN tissue level through cell-to-cell connectivity. Targeted experiments will be performed to inform the construction and validate the predictions of the network model. General computational techniques for model reduction and efficient simulation of heterogeneous cellular networks will be developed to facilitate analysis of model behavior over a wide range of environmental conditions. The research has the potential to be highly transformative by both advancing the multiscale modeling of coupled oscillators/complex networks and by fundamentally changing our understanding of GABA signaling in circadian timekeeping and potentially in other brain regions. Our participation in the Multiscale Modeling Consortium will provide a unique perspective on networked cellular systems where we will explore cross-cutting topics such as network topology, dynamics, robustness and function.
 描述(由申请人提供):耦合生物振荡器的同步和夹带是复杂网络系统中的一个新兴研究领域。位于下丘脑视交叉上核(SCN)的哺乳动物昼夜节律钟由大约20,000个起搏神经元组成,这些神经元耦合在一起以产生驱动其他身体功能(如睡眠模式)的强大整体节律。SCN代表了一个理想的模型系统,用于研究生物网络的设计和行为,因为它积累了单个SCN神经元及其相互作用的数据。实验研究表明,SCN细胞间通讯主要由两种神经递质介导:血管活性肠肽(VIP)和γ-氨基丁酸(GABA)。虽然VIP是一种重要的同步剂,但GABA在抑制/兴奋、昼夜、同步和夹带效应方面的作用仍存在争议。对SCN中神经递质介导的细胞间信号传导的更好理解将对预防和治疗昼夜节律紊乱(包括情绪和睡眠障碍以及代谢疾病)具有重要的临床意义。该项目的目标是开发SCN的多尺度模型,并将该模型与有针对性的实验和新的计算工具相结合,以更好地理解SCN的连接性,同步性和夹带特性。这项研究的重点是GABA信号,因为它在SCN中的作用是突出的,没有得到很好的理解,三位参与研究人员的最新进展将使这种常见的神经递质的作用得到全面和仔细的解剖,突触水平的分辨率跨越大阵列的昼夜节律神经元。多尺度模型将在单个细胞水平上建立核心时钟基因和离子通道之间的联系,并通过细胞到细胞的连接在SCN组织水平上建立网络同步和夹带行为。将进行有针对性的实验,以告知网络模型的构建和验证预测。将开发用于模型简化和高效模拟异构蜂窝网络的通用计算技术,以便于在广泛的环境条件下分析模型行为。这项研究有可能通过推进耦合振荡器/复杂网络的多尺度建模以及从根本上改变我们对昼夜节律计时和其他大脑区域中GABA信号的理解而具有高度变革性。我们在多尺度建模联盟的参与将提供一个独特的视角对网络蜂窝系统,我们将探索交叉主题,如网络拓扑结构,动态,鲁棒性和功能。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Michael Henson其他文献

Michael Henson的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 44.43万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了