Department of Molecular and Integrative Physiology, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign Urbana, IL, USA.
Front Pharmacol. 2014 May 8;5:85. doi: 10.3389/fphar.2014.00085. eCollection 2014.
The leading hypothesis on Alzheimer Disease (AD) is that it is caused by buildup of the peptide amyloid-β (Aβ), which initially causes dysregulation of synaptic plasticity and eventually causes destruction of synapses and neurons. Pharmacological efforts to limit Aβ buildup have proven ineffective, and this raises the twin challenges of understanding the adverse effects of Aβ on synapses and of suggesting pharmacological means to prevent them. The purpose of this paper is to initiate a computational approach to understanding the dysregulation by Aβ of synaptic plasticity and to offer suggestions whereby combinations of various chemical compounds could be arrayed against it. This data-driven approach confronts the complexity of synaptic plasticity by representing findings from the literature in a course-grained manner, and focuses on understanding the aggregate behavior of many molecular interactions. The same set of interactions is modeled by two different computer programs, each written using a different programming modality: one imperative, the other declarative. Both programs compute the same results over an extensive test battery, providing an essential crosscheck. Then the imperative program is used for the computationally intensive purpose of determining the effects on the model of every combination of ten different compounds, while the declarative program is used to analyze model behavior using temporal logic. Together these two model implementations offer new insights into the mechanisms by which Aβ dysregulates synaptic plasticity and suggest many drug combinations that potentially may reduce or prevent it.
阿尔茨海默病（AD）的主要假说认为，它是由肽淀粉样蛋白-β（Aβ）的积累引起的，Aβ 最初导致突触可塑性失调，最终导致突触和神经元的破坏。限制 Aβ 积累的药物治疗已被证明无效，这带来了双重挑战，即了解 Aβ 对突触的不良影响，并提出预防这些影响的药物治疗方法。本文旨在启动一种计算方法来理解 Aβ 对突触可塑性的失调，并提供建议，通过这些建议可以对抗 Aβ 的各种化合物组合。这种数据驱动的方法通过以粗粒度的方式表示文献中的发现来应对突触可塑性的复杂性，并侧重于理解许多分子相互作用的总体行为。同一组相互作用由两个不同的计算机程序建模，每个程序都使用不同的编程模式编写：一个是命令式的，另一个是声明式的。两个程序都在广泛的测试电池上计算相同的结果，提供了必要的交叉检查。然后，命令式程序用于确定模型中十种不同化合物的每种组合对模型的影响，而声明式程序用于使用时间逻辑分析模型行为。这两个模型实现共同为 Aβ 如何失调突触可塑性的机制提供了新的见解，并提出了许多潜在的药物组合，这些组合可能会减少或预防这种失调。
