Systems biology analysis of intracellular signaling networks has tremendously expanded our understanding of normal and diseased cell behaviors and has revealed paths to finding proper therapeutic molecular targets. When it comes to neurons in the human brain, analysis of intraneuronal signaling networks provides invaluable information on learning, memory and cognition-related disorders, as well as potential therapeutic targets. However, neurons in the human brain form a highly complex neural network that, among its many roles, is also responsible for learning, memory formation and cognition. Given the impairment of these processes in mental and psychiatric disorders, one can envision that analyzing interneuronal processes, together with analyzing intraneuronal signaling networks, can result in a better understanding of the pathology and, subsequently, more effective target discovery. In this paper, a hybrid model is introduced, composed of the long-term potentiation (LTP) interneuronal process and an intraneuronal signaling network regulating CREB. LTP refers to an increased synaptic strength over a long period of time among neurons, typically induced upon occurring an activity that generates high-frequency stimulations (HFS) in the brain, and CREB is a transcription factor known to be highly involved in important functions of the cognitive and executive human brain such as learning and memory. The hybrid LTP-signaling model is analyzed using a proposed molecular fault diagnosis method. It allows to study the importance of various signaling molecules according to how much they affect an intercellular phenomenon when they are faulty, i.e. dysfunctional. This paper is intended to suggest another angle for understanding the pathology and therapeutic target discovery by classifying and ranking various intraneuronal signaling molecules based on how much their faulty behaviors affect an interneuronal process. Possible relations between the introduced hybrid analysis and the previous purely intracellular analysis are investigated in the paper as well.

中文翻译：

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一种混合模型，用于研究调节转录因子 CREB ​​的神经元内信号网络中的错误分子如何影响晚期长期增强。

细胞内信号网络的系统生物学分析极大地扩展了我们对正常和患病细胞行为的理解，并揭示了寻找适当治疗分子靶点的途径。对于人脑中的神经元，对神经元内信号网络的分析提供了有关学习、记忆和认知相关疾病以及潜在治疗目标的宝贵信息。然而，人脑中的神经元形成了一个高度复杂的神经网络，在其众多角色中，它还负责学习、记忆形成和认知。鉴于这些过程在精神和精神疾病中的损害，人们可以设想分析神经元间过程以及分析神经元内信号网络，可以更好地理解病理学，并且，随后，更有效的目标发现。本文介绍了一种混合模型，由长时程增强 (LTP) 神经元间过程和调节 CREB ​​的神经元内信号网络组成。LTP 是指神经元中的突触强度在很长一段时间内增加，通常是在发生在大脑中产生高频刺激 (HFS) 的活动时诱导的，CREB ​​是一种转录因子，已知与神经元的重要功能高度相关。认知和执行人类大脑，如学习和记忆。使用提出的分子故障诊断方法分析了混合 LTP 信号模型。它允许研究各种信号分子的重要性，根据它们在出现故障（即功能失调）时对细胞间现象的影响程度。本文旨在通过根据其错误行为对神经元间过程的影响程度对各种神经元内信号分子进行分类和排序，提出另一个理解病理学和治疗靶点发现的角度。本文还研究了引入的混合分析与先前的纯细胞内分析之间的可能关系。