Cellular Response to Surface Morphology: Electrospinning and Computational Modeling.

Surface properties of biomaterials, such as chemistry and morphology, have a major role in modulating cellular behavior and therefore impact on the development of high-performance devices for biomedical applications, such as scaffolds for tissue engineering and systems for drug delivery. Opportunely-designed micro- and nanostructures provides a unique way of controlling cell-biomaterial interaction. This mini-review discusses the current research on the use of electrospinning (extrusion of polymer nanofibers upon the application of an electric field) as effective technique to fabricate patterns of micro- and nano-scale resolution, and the corresponding biological studies.

Adult Stem Cell Therapies for Wound Healing: Biomaterials and Computational Models.

The increased incidence of diabetes and tumors, associated with global demographic issues (aging and life styles), has pointed out the importance to develop new strategies for the effective management of skin wounds. Individuals affected by these diseases are in fact highly exposed to the risk of delayed healing of the injured tissue that typically leads to a pathological inflammatory state and consequently to chronic wounds. Therapies based on stem cells (SCs) have been proposed for the treatment of these wounds, thanks to the ability of SCs to self-renew and specifically differentiate in response to the target bimolecular environment.

A protein structure prediction service in the ProGenGrid system.

This paper describes a protein tertiary structure prediction service implemented in a Grid Environment. The service has been used for predicting the dicarboxylate carrier (DIC) of Saccharomyces cerevisiae by using the homology modelling approach. The visualization of the predicted model is made possible by using an interactive virtual reality environment based on X3D and Ajax3d technologies.

A grid-enabled protein secondary structure predictor.

We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master-Worker paradigm.