Modification of microfiltration membranes by hydrogel impregnation for pDNA purification

The huge efforts for the achievement of highly purified biomolecules are growing every day. A great number of efficient techniques, such as chromatography, are already available in laboratory for separation processes. However, membrane-based technologies are the best match to assure simplicity, efficiency and easy scale-up procedures. Herein we report the modification of a commercial microfiltration membrane for plasmid DNA purification by agarose gel impregnation. The membrane was characterized by SEM, ATR-FTIR, EDS, contact angle, and porosity measurements. Additionally, the membrane pore radius was estimated from observed rejections of different proteins and with that information the rejection of a 6050 bp plasmid DNA (pDNA) molecule was estimated for different values of flux using a theoretical model of large flexible molecules in membranes with parallel cylindrical pores, which is applicable to pDNA ultrafiltration in conventional membranes, as recently shown in the literature. The experimental results show that the modified membrane has higher pDNA rejections than the predicted by the model, suggesting that the different type of porous structure that a hydrogel has, may have a positive effect on pDNA rejections as compared to other biomolecules with more rigid structures, making this type of modified membranes potential better candidates to be used for the selective recovery of pDNA in this type of bioprocesses.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.