The Lipid Invasion Model: Growing Evidence for This New Explanation of Alzheimer's Disease.

The Lipid Invasion Model (LIM) is a new hypothesis for Alzheimer's disease (AD) which argues that AD is a result of external lipid invasion to the brain, following damage to the blood-brain barrier (BBB). The LIM provides a comprehensive explanation of the observed neuropathologies associated with the disease, including the lipid irregularities first described by Alois Alzheimer himself, and accounts for the wide range of risk factors now identified with AD, all of which are also associated with damage to the BBB. This article summarizes the main arguments of the LIM, and new evidence and arguments in support of it. The LIM incorporates and extends the amyloid hypothesis, the current main explanation of the disease, but argues that the greatest cause of late-onset AD is not amyloid-ß (Aß) but bad cholesterol and free fatty acids, let into the brain by a damaged BBB. It suggests that the focus on Aß is the reason why we have made so little progress in treating the disease in the last 30 years. As well as offering new perspectives for further research into the diagnosis, prevention, and treatment of AD, based on protecting and repairing the BBB, the LIM provides potential new insights into other neurodegenerative diseases such as Parkinson's disease and amyotrophic lateral sclerosis/motor neuron disease.

A New Hypothesis for Alzheimer's Disease: The Lipid Invasion Model.

This paper proposes a new hypothesis for Alzheimer's disease (AD)-the lipid invasion model. It argues that AD results from external influx of free fatty acids (FFAs) and lipid-rich lipoproteins into the brain, following disruption of the blood-brain barrier (BBB). The lipid invasion model explains how the influx of albumin-bound FFAs via a disrupted BBB induces bioenergetic changes and oxidative stress, stimulates microglia-driven neuroinflammation, and causes anterograde amnesia. It also explains how the influx of external lipoproteins, which are much larger and more lipid-rich, especially more cholesterol-rich, than those normally present in the brain, causes endosomal-lysosomal abnormalities and overproduction of the peptide amyloid-ß (Aß). This leads to the formation of amyloid plaques and neurofibrillary tangles, the most well-known hallmarks of AD. The lipid invasion model argues that a key role of the BBB is protecting the brain from external lipid access. It shows how the BBB can be damaged by excess Aß, as well as by most other known risk factors for AD, including aging, apolipoprotein E4 (APOE4), and lifestyle factors such as hypertension, smoking, obesity, diabetes, chronic sleep deprivation, stress, and head injury. The lipid invasion model gives a new rationale for what we already know about AD, explaining its many associated risk factors and neuropathologies, including some that are less well-accounted for in other explanations of AD. It offers new insights and suggests new ways to prevent, detect, and treat this destructive disease and potentially other neurodegenerative diseases.

Mathematical modelling of cell migration.

The complexity of biological systems creates challenges for fully understanding their behaviour. This is particularly true for cell migration which requires the co-ordinated activity of hundreds of individual components within cells. Mathematical modelling can help understand these complex systems by breaking the system into discrete steps which can then be interrogated in silico. In this review, we highlight scenarios in cell migration where mathematical modelling can be applied and discuss what types of modelling are most suited. Almost any aspect of cell migration is amenable to mathematical modelling from the modelling of intracellular processes such as chemokine receptor signalling and actin filament branching to larger scale processes such as the movement of individual cells or populations of cells through their environment. Two common ways of approaching this modelling are the use of models based on differential equations or agent-based modelling. The application of both these approaches to cell migration are discussed with specific examples along with common software tools to facilitate the process for non-mathematicians. We also highlight the challenges of modelling cell migration and the need for rigorous experimental work to effectively parameterise a model.