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If you require further, please contact Chaya Moykopf (4048)

The etiological bases of cancer are a large number of ‘bugs’, mutations in the human genome, mostly accumulating in somatic cells during patient’s lifespan. It took more than a century to translate this etiological insight into new ways to smart-bomb the cancer away. As new treatment options emerge, healthcare guidelines seek ways, such companion testing, to identify the patient, the treatment is most likely to benefit. The dynamic nature of the field of medical discoveries poses a challenge to the clinical decision making process, and guidelines have therefore gone through a series of paradigm shifts, all based on risk-benefit assessments. First, in the evidence-based paradigm, optional treatments are ranked according to the fraction of patients the treatment is likely to benefit, starting from the most commonly useful treatment and down the fractional benefit rank. Then, personalized medicine approach utilizes clinical and genomic sequence and molecular analyses, to rearrange the treatments rank, and recommend each patient with their own best treatment. In the most recent paradigm, immune-oncology, we profile the direct adaptive immune reaction, T-cell receptor sequence, to cancer-borne somatic mutations. The unique sequence of the respective T-cell receptors had been demonstrated to genetically code for the recognition and elimination of cells, carrying and presenting the mutant sequence. In other words, the cure to each patient is hidden in their own body, and once discovered, has the potential to harness the progression of cancer, as is being done for patients with high mutation load and immunological checkpoint inhibitors. While this approach is more bioinformatically and experimentally intensive, the results obtained from this approach are far superior, both in the end-stage patients it succeeds to benefit, as well as the duration of remission. Using double-autologous patient-derived xenografts, that model both the cancer tissue, as well as the immune system of each patient, we are harnessing these technologies to improve and accelerate the implementation of those new paradigms in the clinical practice.

Mammalian whiskers and avian rictal bristles come in a variety of shapes and sizes. Indeed, one of the most striking facial features in all mammals (excluding higher primates and humans) is the presence of whiskers. They are deployed in a wide range of tasks and environments. For example, rodents may use their whiskers to guide arboreal locomotion, whilst seals use theirs to track hydrodynamic trails of vortices shed by the fish upon which they prey (Gläser et al, 2010). Certainly, the evolution of the sense of touch is a recognised cornerstone in mammalian evolution, driving brain complexity and behavioural flexibility. While the whisker system is an established model for sensory information processing, advances in measuring whisker behaviours suggests that whisker movements are also useful for measuring aspects of motor control. Many "whisker specialists" including rodents and pinnipeds employ their whiskers by moving them actively, and all mammals (and even some birds) share a similar muscle architecture that drives the movement of the whiskers. Certainly, changes in whisker movements can indicate a loss of motor control and coordination. In this talk I will consider the anatomy and morphology of whiskers, and consider their function in a range of different species. I will suggest how whisker movements may have evolved, and how they are very important for whisker specialists.

The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) is an open and integrative Web service for advanced protein bioinformatic analysis. It includes a wide array of interconnected, state-of-the-art public and in-house tools, whose functionality ranges from the identification of features such as coiled-coil segments (PCOILS, MARCOIL), internal sequence repeats (HHrepID, REPPER) and secondary structure (Quick2D) to the detection of remote homologs (HHpred) and generation of structural models (MODELLER). In fact, due to this breadth of its tools, our Toolkit has established itself as an important resource for experimental scientists and as a useful platform for teaching bioinformatic inquiry. Recently, we replaced the first version of the Toolkit, which was released in 2005 and had serviced over 2.5 million external queries, with an entirely new version built using modern Web technologies and with improved features for teaching and collaborative research. In this presentation, I will focus on the usefulness of the Toolkit for the systematic analysis of proteins using some examples.