Apoptosis is a programmed cell death that plays a critical role during the development of the nervous system and in many chronic neurodegenerative diseases, including Alzheimer's disease (AD). This pathology, characterized by a progressive degeneration of cholinergic function resulting in a remarkable cognitive decline, is the most common form of dementia with high social and economic impact. Current therapies of AD are only symptomatic, therefore the need to elucidate the mechanisms underlying the onset and progression of the disease is surely needed in order to develop effective pharmacological therapies. Because of its pivotal role in neuronal cell death, apoptosis has been considered one of the most appealing therapeutic targets, however, due to the complexity of the molecular mechanisms involving the various triggering events and the many signaling cascades leading to cell death, a comprehensive understanding of this process is still lacking. Modular systems biology is a very effective strategy in organizing information about complex biological processes and deriving modular and mathematical models that greatly simplify the identification of key steps of a given process. This review aims at describing the main steps underlying the strategy of modular systems biology and briefly summarizes how this approach has been successfully applied for cell cycle studies. Moreover, after giving an overview of the many molecular mechanisms underlying apoptosis in AD, we present both a modular and a molecular model of neuronal apoptosis that suggest new insights on neuroprotection for this disease.

Alberghina, L., Colangelo, A. (2006). The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration. BMC NEUROSCIENCE, 7(1), S2 [10.1186/1471-2202-7-S1-S2].

The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration

ALBERGHINA, LILIA;COLANGELO, ANNA MARIA
2006

Abstract

Apoptosis is a programmed cell death that plays a critical role during the development of the nervous system and in many chronic neurodegenerative diseases, including Alzheimer's disease (AD). This pathology, characterized by a progressive degeneration of cholinergic function resulting in a remarkable cognitive decline, is the most common form of dementia with high social and economic impact. Current therapies of AD are only symptomatic, therefore the need to elucidate the mechanisms underlying the onset and progression of the disease is surely needed in order to develop effective pharmacological therapies. Because of its pivotal role in neuronal cell death, apoptosis has been considered one of the most appealing therapeutic targets, however, due to the complexity of the molecular mechanisms involving the various triggering events and the many signaling cascades leading to cell death, a comprehensive understanding of this process is still lacking. Modular systems biology is a very effective strategy in organizing information about complex biological processes and deriving modular and mathematical models that greatly simplify the identification of key steps of a given process. This review aims at describing the main steps underlying the strategy of modular systems biology and briefly summarizes how this approach has been successfully applied for cell cycle studies. Moreover, after giving an overview of the many molecular mechanisms underlying apoptosis in AD, we present both a modular and a molecular model of neuronal apoptosis that suggest new insights on neuroprotection for this disease.
Articolo in rivista - Articolo scientifico
Caspases; Amyloid; Oxidative Stress; Animals; Nerve Degeneration; Nerve Growth Factors; Humans; Apoptosis; Neuroprotective Agents; Systems Biology; Signal Transduction; Alzheimer Disease; Cell Cycle
English
2006
7
1
S2
S2
open
Alberghina, L., Colangelo, A. (2006). The modular systems biology approach to investigate the control of apoptosis in Alzheimer's disease neurodegeneration. BMC NEUROSCIENCE, 7(1), S2 [10.1186/1471-2202-7-S1-S2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/15404
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