[en] Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST’s database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins’ functions.

[en] The Department of Energy site at Hanford, WA, has been historically impacted by U and Cr from the nuclear weapons industry. In an attempt to stimulate microbial remediation of these metals, in-situ lactate enrichment experiments are ongoing. In order to bridge the gap from the laboratory to the field, we inoculated triplicate anaerobic, continuous-flow glass reactors with groundwater collected from well Hanford 100-H in order to obtain a stable, enriched community while selecting for metal-reducing bacteria. Each reactor was fed from a single carboy containing defined media with 30 mM lactate at a rate of 0.223 ml/min under continuous nitrogen flow at 9 ml/min. Cell counts, organic acids, gDNA (for qPCR and pyrosequencing) and gases were sampled during the experiment. Cell counts remained low (less than 1x107 cells/ml) during the first two weeks of the experiment, but by day 20, had reached a density greater than 1x108 cells/ml. Metabolite analysis showed a decrease in the lactate concentrations over time. Pyruvate concentrations ranged from 20-40 uM the first week of the experiment then was undetectable after day 10. Likewise, formate appeared in the reactors during the first week with concentrations of 1.48-1.65 mM at day 7 then the concentrations decreased to 0.69-0.95 on day 10 and were undetectable on day 15. Acetate was present in low amounts on day 3 (0.15-0.33 mM) and steadily increased to 3.35-5.22 mM over time. Similarly, carbon dioxide was present in low concentrations early on and increased to 0.28-0.35 mM as the experiment progressed. We also were able to detect low amounts of methane (10-20 uM) during the first week of the experiment, but by day 10 the methane was undetectable. From these results and pyrosequencing analysis, we conclude that a shift in the microbial community dynamics occurred over time to eventually form a stable and enriched microbial community. Comprehensive investigations such as these allow for the examination of not only which nutrient source will accelerate site remediation, but also provide insight to evaluate remediation strategies through which enriched community members are important for bioremediation.

[en] Sensitivity analysis quantifies the dependence of a system's behavior on the parameters that could possibly affect the dynamics. Calculation of sensitivities of stochastic chemical systems using Kinetic Monte Carlo and finite-difference-based methods is not only computationally intensive, but direct calculation of sensitivities by finite-difference-based methods of parameter perturbations converges very poorly. In this paper we develop an approach to this issue using a method based on the Girsanov measure transformation for jump processes to smooth the estimate of the sensitivity coefficients and make this estimation more accurate. We demonstrate the method with simple examples and discuss its appropriate use