From reactions in cells to organ physiology
Systems medicine is an interdisciplinary approach that integrates data from basic research and clinical practice to improve our understanding and treatment of diseases. Systems medicine can be seen as a further development of systems biology and bioinformatics towards applications of clinical relevance. The term 'systems' refers to systems approaches, emphasizing a close integration of data generation with mathematical modeling [1–3]. The (mal)functioning of the human body is a complex process, characterized by multiple interactions between systems that act across multiple levels of structural and functional organization - from molecular reactions to cell-cell interactions in tissues to the physiology of organs and organ systems. Over the past decade, we have gained detailed insights into the structure and function of molecular, cellular and organ-level systems, with technologies playing an important role in the generation of data at these different scales.
Under the umbrella of systems biology, workflows of data-driven modeling and model-driven experimentation have led to the development of computational models that describe processes at all levels, including gene regulatory networks, signal transduction pathways and metabolic networks, cell populations, structured tissues, pharmacokinetic and pharmacodynamic models that analyze drug or vaccine action at the whole organism level, and pharmacogenomic models of disease risk and drug and vaccine exposure at the patient-population level.
Major challenges exist for the development of quantitative and predictive models that span the gap between cell-level biochemical models and organism-level pharmacokinetic and pharmacodynamic models. One hurdle is the technical difficulty associated with generating sufficiently comprehensive quantitative datasets for large numbers of system variables, across different levels of organization. Another major challenge is the development of efficient tools that can handle the wide variety of available data and identify relevant data subsets from existing repositories. Overcoming these hurdles will lead to the development of models that can be used in clinical practice while accounting for the uncertainty in the data.
Another important challenge for systems medicine is the integration of this knowledge across the relevant levels of organization. Large, long-term research initiatives, like the Virtual Physiological Human, the Virtual Liver or the Human Brain Project, are aiming to develop comprehensive, computational representations of organs and organ systems. Here, we focus on opportunities for comparatively small interdisciplinary collaborations between clinicians and modelers who are targeting specific questions of clinical relevance. In the projects that we envisage, modeling is not the final goal; rather, it is a tool that can be used to advance understanding of the system, to develop more directed experiments in the laboratory and, ultimately, to generate testable predictions to enable improved therapies and prophylaxes.