Enabling multiscale modeling in systems medicine

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.

The fields of theoretical and mathematical biology have pioneered the development of mathematical and computational models of biological systems. Systems biology has contributed workflows for data-driven modeling and model-driven experimentation to the life sciences. Taken together, these provide a considerable body of experience for modeling at different levels (Box 1).

This list is necessarily incomplete and serves here to indicate the progression of modeling efforts, with an increasing diversity of approaches available, a tendency to combine approaches in modeling workflows, and clear indications of an increasing number of successful case studies.

Multiscale modeling serves as a ‘macroscope’ that integrates the evidence available at different levels of a particular system’s structural and functional organization while enabling us to zoom in on the details where necessary. For experimental models, it is almost certain that not all parameters influencing a disease will be known and, often, known parameters cannot be influenced and/or measured. This makes it difficult to unambiguously identify the mechanisms at work in diseases. Modeling therefore makes an epistemic contribution by allowing in silico (simulation) experiments under defined conditions, aiding the exploration of hypotheses by explicitly accounting for the uncertainty that arises from biological complexity.

Central to these efforts are data of high quality that are quantitative, derived from reproducible studies and that cover multiple levels of functional and structural organization, generated from a range of experimental systems. A particularly important challenge is to enable clinical validation of models and simulations.

Our discussions have identified a number of recommendations.

Molecular modeling, for example

Modeling of subcellular processes, for example:

Individual-cell or cell-based models, for example:

Tissue/organ level models, for example:

Body systems level models, for example: