IT Future of Medicine: from molecular analysis to clinical diagnosis and improved treatment

The IT Future of Medicine (ITFoM, http://www.itfom.eu/) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in 'virtual patient' models. The establishment of such 'virtual patient' models is now possible due to the enormous progress in analytical techniques, particularly in the '-omics' technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate 'virtual patient' models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs

Strategies for structuring interdisciplinary education in Systems Biology: an European perspective

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon disciplinary degrees, we believe that the most effective way to implement education in Systems Biology would be at the Master’s level, as it offers a more flexible framework. Our team of experts and active performers of Systems Biology education suggest here (i) a definition of the skills that students should acquire within a Master’s programme in Systems Biology, (ii) a possible basic educational curriculum with flexibility to adjust to different application areas and local research strengths, (iii) a description of possible career paths for students who undergo such an education, (iv) conditions that should improve the recruitment of students to such programmes and (v) mechanisms for collaboration and excellence spreading among education professionals. With the growing interest of industry in applying Systems Biology approaches in their fields, a concerted action between academia and industry is needed to build this expertise. Here we present a reflection of the European situation and expertise, where most of the challenges we discuss are universal, anticipating that our suggestions will be useful internationally. We believe that one of the overriding goals of any Systems Biology education should be a student’s ability to phrase and communicate research questions in such a manner that they can be solved by the integration of experiments and modelling, as well as to communicate and collaborate productively across different experimental and theoretical disciplines in research and development

predictive precision medicine towards the computational challenge

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level

Large scale automated genome and proteome analysis in plants – GABI-LAPP

Im Rahmen der Technologieplattform GABI-LAPP wird für die pflanzliche Genomforschung Technologie für die Hochdurchsatzanalyse im Proteomics-Bereich entwickelt. Das LAPP-Projekt wird am Ende der Förderperiode eine breite Basis an technologischen Ressourcen für die wissenschaftliche Arbeit an Pflanzen zur Verfügung stellen. Die Methoden werden zunächst an der Modellpflanze Arabidopsis thaliana entwickelt, die Ergebnisse stellen jedoch die Grundlage für angewandte Forschung an Nutzpflanzen, wie z.B. Gerste, dar. Eine zentrale Aufgabe für alle LAPP-Teilprojekte ist die Entwicklung von Hochdurchsatztechnologie für unterschiedliche Arten von Proben und Ansätzen. Diese Entwicklungen sollen die genomweite Analyse von DNA, RNA und Proteinen sowie die Aufklärung von deren Interaktionen ermöglichen. Ausgehend von Arabidopsis werden unterschiedliche Arten von Daten in einer Datenbank erfasst, um durch deren Integration ein großes Potential für angewandte Forschung sowie für Data-Mining zur Verfügung zu stellen. Die LAPP-Teilprojekte sind hauptsächlich am Max-Planck-Institut für molekulare Genetik in Berlin in der Abteilung von Prof. Hans Lehrach angesiedelt

GABI-LAPP - Large-scale Automated Plant Proteomics in GABI

Im Rahmen der Technologieplattform GABI-LAPP wird für die pflanzliche Genomforschung Technologie für die Hochdurchsatzanalyse im Proteomics-Bereich entwickelt. Das LAPP-Projekt wird am Ende der Förderperiode eine breite Basis an technologischen Ressourcen für die wissenschaftliche Arbeit an Pflanzen zur Verfügung stellen. Die Methoden werden zunächst an der Modellpflanze Arabidopsis thaliana entwickelt, die Ergebnisse stellen jedoch die Grundlage für angewandte Forschung an Nutzpflanzen, wie z.B. Gerste, dar. Eine zentrale Aufgabe für alle LAPP-Teilprojekte ist die Entwicklung von Hochdurchsatztechnologie für unterschiedliche Arten von Proben und Ansätzen. Diese Entwicklungen sollen die genomweite Analyse von DNA, RNA und Proteinen sowie die Aufklärung von deren Interaktionen ermöglichen. Ausgehend von Arabidopsis werden unterschiedliche Arten von Daten in einer Datenbank erfasst, um durch deren Integration ein großes Potential für angewandte Forschung sowie für Data-Mining zur Verfügung zu stellen. Die LAPP-Teilprojekte sind hauptsächlich am Max-Planck-Institut für molekulare Genetik in Berlin in der Abteilung von Prof. Hans Lehrach angesiedelt