Dynamic self-guiding analysis of Alzheimer’s disease

Abstract

We applied a self-guiding evolutionary algorithm to initiate the synthesis of the Alzheimer’s disease-related data and literature. A protein interaction network associated with amyloid-beta precursor protein (APP) and a seed model that treats Alzheimer’s disease as progressive dysregulation of APP-associated signaling were used as dynamic “guides” and structural “filters” in the recursive search, analysis, and assimilation of data to drive the evolution of the seed model in size, detail, and complexity. Analysis of data and literature across sub-disciplines and system-scale discovery platforms suggests a key role of dynamic cytoskeletal connectivity in the stability, plasticity, and performance of multicellular networks and architectures. Chronic impairment and/or dysregulation of cell adhesions/synapses, cytoskeletal networks, and/or reversible epithelial-to-mesenchymal-like transitions, which enable and mediate the stable and coherent yet dynamic and reconfigurable multicellular architectures, may lead to the emergence and persistence of the disordered, wound-like pockets/microenvironments of chronically disconnected cells. Such wound-like microenvironments support and are supported by pro-inflammatory, pro-secretion, de-differentiated cellular phenotypes with altered metabolism and signaling. The co-evolution of wound-like microenvironments and their inhabitants may lead to the selection and stabilization of degenerated cellular phenotypes, via acquisition of epigenetic modifications and mutations, which eventually result in degenerative disorders such as cancer and Alzheimer’s disease.