Beschreibung: 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.

Beschreibung: 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.