| Alzheimer's disease (AD) is the most common cause of late life dementia. In the brain of an AD patient, neurons are lost and spatial neuronal organizations (microcolumns) are disrupted. An adequate quantitative analysis of microcolumns requires that we automate the neuron recognition stage in the analysis of microscopic images of human brain tissue. We propose a recognition method based on statistical physics. Specifically, Monte Carlo simulations of an inhomogeneous Potts model are applied for image segmentation. Unlike most traditional methods, this method improves the recognition of overlapped neurons, and thus improves the overall recognition percentage.; Although the exact causes of AD are unknown, as experimental advances have revealed the molecular origin of AD, they have continued to support the amyloid cascade hypothesis, which states that early stages of aggregation of amyloid beta (Abeta) peptides lead to neurodegeneration and death. X-ray diffraction studies reveal the common cross-beta structural features of the final stable aggregates-amyloid fibrils. Solid-state NMR studies also reveal structural features for some well-ordered fibrils. But currently there is no feasible experimental technique that can reveal the exact structure or the precise dynamics of assembly and thus help us understand the aggregation mechanism.; Computer simulation offers a way to understand the aggregation mechanism on the molecular level. Because traditional all-atom continuous molecular dynamics simulations are not fast enough to investigate the whole aggregation process, we apply coarse-grained models and discrete molecular dynamics methods to increase the simulation speed. First we use a coarse-grained two-bead (two beads per amino acid) model. Simulations show that peptides can aggregate into multilayer beta-sheet structures, which agree with X-ray diffraction experiments. To better represent the secondary structure transition happening during aggregation, we refine the model to four beads per amino acid. Typical essential interactions, such as backbone hydrogen bond, hydrophobic and electrostatic interactions, are incorporated into our model. We study the aggregation of Abeta16-22, a peptide that can aggregate into a well-ordered fibrillar structure in experiments. Our results show that randomly-oriented monomers can aggregate into fibrillar subunits, which agree not only with X-ray diffraction experiments but also with solid-state NMR studies. Our findings demonstrate that coarse-grained models and discrete molecular dynamics simulations can help researchers understand the aggregation mechanism of amyloid peptides. |
