Modeling signal transduction in classical conditioning with network motifs.
Modeling signal transduction in classical conditioning with network motifs.
复制标题
通过网络基序对经典条件进行建模信号转导。
DOI：
10.3389/fnmol.2011.00009
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发表时间：
2011
影响因子：
4.8
通讯作者：
Houk JC
中科院分区：
文献类型：
--
作者：
Keifer J;Houk JC
生物网络是由重复的简化模式或模块构成的，称为网络基序。网络基序可以在包括细菌、植物和动物在内的多种生物体中发现，以及在神经元回路中用于基因表达和信号转导过程的细胞内转录网络。突触可塑性和学习的信号转导事件的标准模型往往无法捕捉这些过程背后的分子相互作用的复杂性和协同性。在这里，我们应用网络图案的信号转导模型在体外形式的眨眼经典条件反射，揭示了这些分子通路的基本组织。实验证据表明，有两个阶段的突触AMPA受体（AMPAR）运输条件反射。含有GluR 1的AMPAR的突触结合早期发生以激活传递听觉条件刺激的沉默突触，并且该初始步骤之后是支持习得条件反应（CR）的GluR 4亚基的递送。总的来说，在调节期间突触AMPAR递送的两个阶段的网络设计描述了具有AND逻辑的相干前馈回路（C1-FFL）。GluR 1突触传递和3-磷酸肌醇依赖性蛋白激酶-1（PDK-1）的持续激活的组合输入导致含GluR 4的AMPAR的突触掺入和CR的逐渐获得。假设这里描述的用于调节的网络架构通常充当与调节过程的非线性一致的符号敏感延迟元件。有趣的是，这种FFL结构还执行重合检测。基于基序的信号转导建模方法可以作为一种新的工具，用于理解突触可塑性和学习的分子机制，并比较不同形式的学习和模型系统的结果。
Biological networks are constructed of repeated simplified patterns, or modules, called network motifs. Network motifs can be found in a variety of organisms including bacteria, plants, and animals, as well as intracellular transcription networks for gene expression and signal transduction processes in neuronal circuits. Standard models of signal transduction events for synaptic plasticity and learning often fail to capture the complexity and cooperativity of the molecular interactions underlying these processes. Here, we apply network motifs to a model for signal transduction during an in vitro form of eyeblink classical conditioning that reveals an underlying organization of these molecular pathways. Experimental evidence suggests there are two stages of synaptic AMPA receptor (AMPAR) trafficking during conditioning. Synaptic incorporation of GluR1-containing AMPARs occurs early to activate silent synapses conveying the auditory conditioned stimulus and this initial step is followed by delivery of GluR4 subunits that supports acquisition of learned conditioned responses (CRs). Overall, the network design of the two stages of synaptic AMPAR delivery during conditioning describes a coherent feed-forward loop (C1-FFL) with AND logic. The combined inputs of GluR1 synaptic delivery AND the sustained activation of 3-phosphoinositide-dependent protein-kinase-1 (PDK-1) results in synaptic incorporation of GluR4-containing AMPARs and the gradual acquisition of CRs. The network architecture described here for conditioning is postulated to act generally as a sign-sensitive delay element that is consistent with the non-linearity of the conditioning process. Interestingly, this FFL structure also performs coincidence detection. A motif-based approach to modeling signal transduction can be used as a new tool for understanding molecular mechanisms underlying synaptic plasticity and learning and for comparing findings across forms of learning and model systems.
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影响因子：
1.9
作者：
Houk, JC
通讯作者：
Houk, JC
影响因子：
56.9
作者：
Milo, R;Shen-Orr, S;Alon, U
通讯作者：
Alon, U
影响因子：
4.8
作者：
Sato, S;Fujita, N;Tsuruo, T
通讯作者：
Tsuruo, T
DOI：
10.1073/pnas.0603412103
发表时间：
2006-09-19
影响因子：
11.1
作者：
Sharma, Shiv K.;Sherff, Carolyn M.;Carew, Thomas J.
通讯作者：
Carew, Thomas J.
DOI：
10.1523/jneurosci.3649-09.2009
发表时间：
2009-11-25
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Keifer J;Sabirzhanov BE;Zheng Z;Li W;Clark TG
通讯作者：
Clark TG