Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

Silica nanoparticles (SiO2 NPs) synthesized by the sol–gel approach were engineered for size and surface properties by grafting hydrophobic chains to prevent their aggregation and facilitate their contact with the phase boundary, thus improving their dispersibility in lubricant base oils. The surface modification was performed by covalent binding of long chain alkyl functionalities using lauric acid and decanoyl chloride to the SiO2 NP surface. The hybrid SiO2 NPs were characterized by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, simultaneous differential thermal analysis, nuclear magnetic resonance and dynamic light scattering, while their dispersion in two base oils was studied by static multiple light scattering at low (0.01% w/v) and high (0.50%w/v) concentrations. The nature of the functional layer and the functionalization degree seemed to be directly involved in the stability of the suspensions. The potential use of the functional SiO2 NPs as lubricant additives in base oils, specially designed for being used in hydraulic circuits, has been outlined by analyzing the tribological properties of the dispersions. The dendritic structure of the external layer played a key role in the tribological characteristics of the material by reducing the friction coefficient and wear. These nanoparticles reduce drastically the waste of energy in friction processes and are more environmentally friendly than other additives.

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-Individual loci of economic importance (QTL) can be detected by comparing the inheritance of a trait and the inheritance of loci with alleles readily identifiable by laboratory methods (genetic markers). Data on allele segregation at the individual level are costly and alternatives have been proposed that make use of allele frequencies among progeny, rather than individual genotypes. Among the factors that may affect the power of the set up, the most important are those intrinsic to the QTL: the additive effect of the QTL, and its dominance, and distance between markers and QTL. Other factors are relative to the choice of animals and markers, such as the frequency of the QTL and marker alleles among dams and sires. Data collection may affect the detection power through the size of half-sib families, selection rate within families, and the technical error incurred when estimating genetic frequencies. We present results for a sensitivity analysis for QTL detection using pools of DNA from selected half-sibs. Simulations showed that conclusive detection may be achieved with families of at least 500 half-sibs if sires are chosen on the criteria that most of their marker alleles are either both missing, or one is fixed, among dams.quantitative trait loci / genetic marker / selective DNA pooling