Description: Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results: We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions: A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.

Description: Background: Multiple solutes are retained in uremia, but it is currently unclear which solutes are toxic. Small studies suggest that protein-bound solutes, such as p-cresol sulfate and indoxyl sulfate and intracellular solutes, such as methylamine (MMA) and dimethylamine (DMA), may be toxic. Our objective was to test whether elevated levels of these solutes were associated with mortality. Methods: We conducted a prospective cohort study in 521 U.S. incident hemodialysis patients to evaluate associations between these solutes and all-cause and cardiovascular mortality. P-cresol sulfate, indoxyl sulfate, MMA and DMA levels were measured from frozen plasma samples obtained 2 to 6 months after initiation of dialysis. Mortality data was available through 2004 using the National Death Index. Results: Elevated (greater than the population median) p-cresol sulfate, MMA or DMA levels were not associated with all-cause or cardiovascular mortality. Elevated indoxyl sulfate levels were associated with all-cause mortality but not cardiovascular mortality (hazard ratio 1.30 (95% confidence interval 1.01, 1.69) p-value 0.043). Conclusions: In this cohort of 521 incident hemodialysis patients, only elevated indoxyl sulfate levels were associated with all-cause mortality. Further research is needed to identify causes of the toxicity of uremia to provide better care for patients with kidney disease.

Description: Background: The identification of the loci and specific alleles underlying variation in quantitative traits is an important goal for evolutionary biologists and breeders. Despite major advancements in genomics technology, moving from QTL to causal alleles remains a major challenge in genetics research. Near-isogenic lines are the ideal raw material for QTL validation, refinement of QTL location and, ultimately, gene discovery. Results: In this study, a population of 75 Arabidopsis thaliana near-isogenic lines was developed from an existing recombinant inbred line (RIL) population derived from a cross between physiologically divergent accessions Kas-1 and Tsu-1. First, a novel algorithm was developed to utilize genome-wide marker data in selecting RILs fully isogenic to Kas-1 for a single chromosome. Seven such RILs were used in 2 generations of crossing to Tsu-1 to create BC1 seed. BC1 plants were genotyped with SSR markers so that lines could be selected that carried Kas-1 introgressions, resulting in a population carrying chromosomal introgressions spanning the genome. BC1 lines were genotyped with 48 genome-wide SSRs to identify lines with a targeted Kas-1 introgression and the fewest genomic introgressions elsewhere. 75 such lines were selected and genotyped at an additional 41 SNP loci and another 930 tags using 2b-RAD genotyping by sequencing. The final population carried an average of 1.35 homozygous and 2.49 heterozygous introgressions per line with average introgression sizes of 5.32 and 5.16 Mb, respectively. In a simple case study, we demonstrate the advantage of maintaining heterozygotes in our library whereby fine-mapping efforts are conducted simply by self-pollination. Crossovers in the heterozygous interval during this single selfing generation break the introgression into smaller, homozygous fragments (sub-NILs). Additionally, we utilize a homozygous NIL for validation of a QTL underlying stomatal conductance, a low heritability trait. Conclusions: The present results introduce a new and valuable resource to the Brassicaceae research community that enables rapid fine-mapping of candidate loci in parallel with QTL validation. These attributes along with dense marker coverage and genome-wide chromosomal introgressions make this population an ideal starting point for discovery of genes underlying important complex traits of agricultural and ecological significance.

Description: Background: Solely in Europoe, Salmonella Typhimurium causes more than 100,000 infections per year.Improved detection of livestock colonised with S. Typhimurium is necessary to preventfoodborne diseases. Currently, commercially available ELISA assays are based on a mixtureof O-antigens (LPS) or total cell lysate of Salmonella and are hampered by cross-reaction.The identification of novel immunogenic proteins would be useful to develop ELISA baseddiagnostic assays with a higher specificity. Results: A phage display library of the entire Salmonella Typhimurium genome was constructed and47 immunogenic oligopeptides were identified using a pool of convalescent sera from pigsinfected with Salmonella Typhimurium. The corresponding complete genes of seven of theidentified oligopeptids were cloned. Five of them were produced in E. coli. The immunogeniccharacter of these antigens was validated with sera from pigs infeced with S. Tyhimurium andcontrol sera from non-infected animals. Finally, human antibody fragments (scFv) againstthese five antigens were selected using antibody phage display and characterised. Conclusion: In this work, we identified novel immunogenic proteins of Salmonella Typhimurium andgenerated antibody fragments against these antigens completely based on phage display. Fiveimmunogenic proteins were validated using a panel of positive and negative sera forprospective applications in diagnostics of Salmonela Typhimurium.

Description: From The Third Annual Conference of the MidSouth Computational Biology and Bioinformatics Society Baton Rouge, Louisiana. 2–4 March, 2006.

Description: © 2006 Nahum et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Description: EGenBio is a system for manipulation and filtering of large numbers of sequences, integrating curated sequence alignments and phylogenetic trees, managing evolutionary analyses, and visualizing their output. EGenBio is organized into three conceptual divisions, Evolution, Genomics, and Biodiversity. The Genomics division includes tools for selecting pre-aligned sequences from different genes and species, and for modifying and filtering these alignments for further analysis. Species searches are handled through queries that can be modified based on a tree-based navigation system and saved. The Biodiversity division contains tools for analyzing individual sequences or sequence alignments, whereas the Evolution division contains tools involving phylogenetic trees. Alignments are annotated with analytical results and modification history using our PRAED format. A miscellaneous Tools section and Help framework are also available. EGenBio was developed around our comparative genomic research and a prototype database of mtDNA genomes. It utilizes MySQL-relational databases and dynamic page generation, and calls numerous custom programs.

Description: This work was partly funded by the National Institutes of Health (R22/R33 Innovation and Development grant to David Pollock), the National Science Foundation (CBM2/EPSCOR), and the State of Louisiana (Biological Computation and Visualization Center, Governor's iotechnology Initiative, and startup funds to David Pollock).