This workshop was organized and hosted by Peter Klimek.
Most chronic disorders are caused by multiple genetic processes that act in concert with environmental factors. Recent advances in handling and analyzing large data sets on such disease-causing mechanisms, their interactions, and their associated phenotypes enable a novel, quantitative, and data-driven approach to understand human disease.
The workshop brings together leading researchers of the field to discuss their most recent works on applying methods and tools from systems biology and systems medicine in order to tackle some of today’s most pressing challenges in health care and medicine, next to sharing their vision on the future directions of data-intensive research efforts in the respective fields.
The workshop opened with a presentation of Nicholas Tatonetti from Columbia University, who discussed recent works on how adverse drug events can be identified in an automated way by mining observational health care data, such as electronic health records. His group was even able to validate the so-found drug-drug interactions in mouse models. David Gomez-Cabrero from Karolinska Institutet showed how health care data can be integrated with multi-omics data in a statistically sound way to generate insights into the molecular underpinnings of diseases. Alex Arenas from Universitat Rovira i Virgili presented novel developments in state-of-the-art network methods that allow to identify shared pathophysiological processes between disorders.
Natasa Przulj from University College London presented a novel personalized medicine approach to integrate networked data such that clinically relevant features can be uncovered that were previously hidden in the complex wiring patterns of the data. Sebastian Köhler from Charité Berlin described his work on building an ontology for disease phenotypes (a standardized vocabulary for abnormal medical conditions and semantic relations between them) that can and already has catalyzed research activities in systems medicine. The first day of the workshop was concluded by Miquel Duran-Frigola from IRB Barcelona who showed a novel systems biology approach that identifies the most relevant chemical moieties for hundreds of different diseases, which offers novel opportunities for drug repositioning.
On the second day Jörg Menche from CeMM presented a systematic, experimental approach to predict adverse drug effects in a way that allows to identify all potential interactions of each relevant pair of drugs that are currently used in medicine. Mikael Benson from Linköping University outlined how methods from systems medicine can be used to identify early disease regulators in patients as leverage points for personalized prevention strategies. The workshop was concluded by Peter Klimek from the Medical University of Vienna, who presented novel network approaches for developing personalized disease risk models and elucidating the influence of environmental risk factors.
In summary, the workshop made it clear that a new approach to medicine is currently emerging that makes use of methods from complexity science, such as networks and their generalizations, in order to integrate data from health care and several omics from biology. These approaches have demonstrated their potential to revolutionize health care and medicine by providing a data-driven and comprehensive understanding of human disease, in particular in terms of personalized health risk assessment, individualized disease prevention, and the avoidance of adverse drug effects.