The road from systems biology to systems medicine

Abstract

Review nature publishing group as research institutions prepare roadmaps for "systems medicine, " we ask how this differs from applications of systems biology approaches in medicine and what we (should) have learned from about one decade of funding in systems biology. after surveying the area, we conclude that systems medicine is the logical next step and necessary extension of systems biology, and we focus on clinically relevant applications. We specifically discuss three related notions. First, more interdisciplinary collaborations are needed to face the challenges of integrating basic research and clinical practice: integration, analysis, and interpretation of clinical and nonclinical data for diagnosis, prognosis, and therapy require advanced statistical, computational, and mathematical tools. second, strategies are required to (i) develop and maintain computational platforms for the integration of clinical and nonclinical data, (ii) further develop technologies for quantitative and time-resolved tracking of changes in gene expression, cell signaling, and metabolism in relation to environmental and lifestyle influences, and (iii) develop methodologies for mathematical and statistical analyses of integrated data sets and multilevel models. Third, interdisciplinary collaborations represent a major challenge and are difficult to implement. For an efficient and successful initiation of interdisciplinary systems medicine programs, we argue that epistemological, ontological, and sociological aspects require attention. Lessons From systems BioLogyHere, we discuss the developments that can or should take us from systems biology to systems medicine. Although it is easy to agree that medicine should embark on that journey, it remains unclear which route should be taken. Our own experience and analysis of developments in the field of systems biology has taught us several lessons of which the following are of central importance and which shall be the focus of our discussion: (i) many diseases have their origin in cellular malfunction, requiring a deep understanding of the mechanisms underlying cell functions; (ii) the emergence of diseases is a nonlinear dynamical phenomenon, requiring quantitative time-resolved monitoring of key biological parameters at the molecular, cellular, and physiological levels; (iii) advances in measurement technologies can generate large-scale, multilevel but also heterogeneous datasets, requiring not only new computational platforms to manage data but most importantly, requiring new ways of thinking, including the application and development of methodologies from the mathematical sciences; and (iv) to address clinical questions with statistical, mathematical, computational, molecular, and cell-biological methodologies requires strategic efforts to motivate and sustain cross-disciplinary collaborations.Applying systems approaches in a clinical setting, practical, i.e., formal/legal and computational issues of data collection and sharing are the most immediate challenge and potential threat to...

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