The HiGHmed consortium aims to develop and use innovative information infrastructures to increase the efficiency of clinical research and to swiftly translate research results into validated improvements of patient care. These aims are tightly connected with challenges to integrate and further develop solutions of innovative, internationally interoperable data integration and methods, with the aim to demonstrate their added value for health research and patient care. The concepts must be designed in a way that will help to develop sustainable structures and with the perspective for an easy roll-out to other hospitals. You might want to refer to the HiGHmed consoritum website for further details.
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The cooperation project Systems Medicine of Heart Failure (SMART) focuses on researching risk factors of heart failures. The onset and course of heart failure (HF) is triggered by a complex regulatory network that includes stressors (pressure overload by individual anatomic hemodynamic settings), intrinsic (genes), environmental (regulating epigenetics), and modifying factors (such as hormones and the immune system). SMART aims to establish individualized strategies for the prevention and management of HF by early detection of the physiological, genomic, proteomic and hemodynamic mechanisms that lead from one common cause of ventricular dysfunction (pressure overload) to maladaptive remodeling and irreversible HF. To cope with the complexity of HF, SMART will interrelate models describing the interplay between genome, proteome and cell function, regulating hormones, tissue composition and hemodynamic whole organ function up to a whole body description of a patient and patient cohorts. The ultimate goal is to demonstrate proof-of-concept tools for predicting disease evolution and efficacy of treatment in a given patient. To achieve this task SMART will apply
– A modelling framework that couples multi-scale mechanistic models with in-depth genome/proteome, cell physiology and whole organ (biomechanical and fluid dynamic) models
– Subsequently, investigate methods validity and relevance for “quantitative prediction” of treatment outcome in a clinical proof-of-concept trial (demonstrator) of patients with aortic valve diseases.
In the following, we assembled related content that might be of your particular interest.
- Kraus, M., Schapranow, M.: An In-Memory Database Platform for Systems Medicine. International Conference on Bioinformatics and Computational Biology. bl. 93--100. The International Society for Computers and Their Applications (ISCA) (2017).
- Schapranow, M.-P., Kraus, M., Danner, M., Plattner, H.: IMDBfs: Bridging the Gap between In-Memory Database Technology and File-Based Tools for Life Sciences. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. bl. 1133--1139. IEEE (2016).
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