Computational identification of potential multitarget treatments for ameliorating the adverse effects of amyloid-β on synaptic plasticity.

Thomas J Anastasio

doi:10.3389/fphar.2014.00085

Abstract

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.

Highlights

Alzheimer Disease (AD) is the most prevalent form of dementia (Whitehouse et al, 2000)
MODEL BEHAVIOR UNDER NORMAL CONDITIONS AND IN THE PRESENCE OF Aβ Under normal circumstances in the model [i.e., with Abeta(0)], long-term depression (LTD) is produced at presynaptic activity (preSYN)(2) and long-term potentiation (LTP) is produced at preSYN(3), each by a distinct pattern of kinase/phosphatase activation and inactivation
They are under the control of a complex set of interactions, the specific kinase/phosphatase pattern that occurs at any level of presynaptic activity is determined by the level of calcium ions (Ca) produced by that activity

Summary

Introduction

Alzheimer Disease (AD) is the most prevalent form of dementia (Whitehouse et al, 2000). Part of the difficulty in developing treatments for AD may be the sheer complexity of the pathological processes that cause it. The earliest signs of AD are cognitive impairments including deficits in memory formation and storage that are caused by disruption of the processes of synaptic plasticity, which are complex in their own right. One avenue toward understanding these complex processes is to represent, simulate, and analyze them using “process algebra,” a computational technique that falls under the umbrella of formal methods in computer science (Monin and Hinchey, 2003). The purpose of this study is to develop an initial computational framework for understanding how various chemical compounds might aid memory in AD by using formal methods to simulate the deficits in synaptic plasticity that accompany the disorder

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