Proteomic profiling of mitochondria: what does it tell us about the ageing brain?

Mitochondrial dysfunction is evident in numerous neurodegenerative and age-related disorders. It has also been linked to cellular ageing, however our current understanding of the mitochondrial changes that occur are unclear. Functional studies have made some progress reporting reduced respiration, dynamic structural modifications and loss of membrane potential, though there are conflicts within these findings. Proteomic analyses, together with functional studies, are required in order to profile the mitochondrial changes that occur with age and can contribute to unravelling the complexity of the ageing phenotype. The emergence of improved protein separation techniques, combined with mass spectrometry analyses has allowed the identification of age and cell-type specific mitochondrial changes in energy metabolism, antioxidants, fusion and fission machinery, chaperones, membrane proteins and biosynthesis pathways. Here, we identify and review recent data from the analyses of mitochondria from rodent brains. It is expected that knowledge gained from understanding age-related mitochondrial changes of the brain should lead to improved biomarkers of normal ageing and also age-related disease progression

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models