Medical Applications of Systems Biology

Systems Medicine is the application of systems biology approaches to medical research and medical practice. Its objective is to integrate a variety of biological and medical data at all relevant levels of cellular organization using the power of computational and mathematical modeling, to enable understanding of the pathophysiological mechanisms, prognosis, diagnosis and treatment of disease.

Medical applications of systems biology is now made possible by identification of preliminary circadian biomarkers for more efficient and personalized drug treatments in cancer patients, mechanistic insights into Creutzfeldt-Jakob disease and identification of new therapies for more effective treatment of psychiatric disorders and understanding of energy metabolism in Parkinson’s disease and modeling of risk factors and better understanding the pathology. The major challenge is how systems biology can contribute to change the medical paradigm in order to build the foundation for a prospective medicine that will be predictive, personalized, preventive and participatory.

Currently there exists substantial detailed data on single biological entities associated with or linked to complex diseases. We can understand single components and, possibly for several of them, their function, but these are only parts of a complex system. Systems biology approaches have so far been successful in plants, animal, cellular models. Studies are accumulating on interacting network analysis of basic biological mechanisms, as well as on analysis of cell-cell interactions to understand behavior of cell populations. The applications of systems biology approaches are already delivering computational and mathematical models of basic biological pathways related to diseases in-vivo in animal models. The technology and tools, in particularly high-throughput methods for genome-wide analysis are now available to apply systems biology in patients. Major and chronic diseases are multifactorial in nature and reductionist biology will not provide solutions. Human disease can be perceived as perturbations of complex, integrated genetic, molecular and cellular networks. Such complexity necessitates the power of mathematical modeling in predicting a systems response in health and disease. The applications of systems biology approaches permit a comprehensive evaluation of underlying predisposition to disease, disease diagnosis and progression.

Success stories for medical applications are starting to appear in the literature. The first examples are at the edge of moving into clinical applications; some examples include: EGF-receptor system stratification in breast cancer; heart modeling – approval by U S Food and Drug Administration of the use of a model to test new cardiac drugs; Entelos has created “virtual asthma patients”; a model of Creutzfeldt-Jakob disease explaining the pathogenesis and disease progression, suggesting new and more effective treatments for psychiatric disorders; chronobiological modeling of drug effects that could improve the efficacy of cancer chemotherapy. The high quality and conclusive systems biology studies of human clinical samples are still rare and that a concrete impact of systems biology applications for patients is still to be proven.

Several areas, for example, drug discovery, where systems biology approaches can bring an important contribution as a paradigm for understanding complexity. Despite the extraordinary advances in biomedical research over the past decade, the positive effect of these efforts on drug discovery and the identification of new, more effective therapies have been limited, possibly because of the inability to visualize the complexity of biological systems. There is little or no information on cellular-system interactions and there is a necessity to understand target response within physiological networks and not in isolation. A vast amount of data is being generated from clinical trials and from individual patients and it is becoming increasingly difficult to piece together this data and to maximize its content. Systems medicine should provide a framework within which such disparate data can be integrated, so that medicine will move from a “guess & pray” mentality to “predict & test” strategies.

However, it is necessary that systems biology efforts world-wide deliver solid experimental evidence with clear added value and societal benefit. An important ingredient for developing Systems Medicine is coordination – a coordinated approach, across disciplines, and across academia and industry. Furthermore, the systems biology community should make best efforts to communicate correctly its goals to the general public and to policy makers, remaining ambitious, but pragmatic and tangible and not overpromising what might be achieved, while considering all potential ethical aspects. There is need for coordination of efforts of all relevant actors to take the first steps towards systems medicine and make a paradigm shift in classical medicine. The endeavor should have a scale of ambition and vision similar to that of the Human Genome project. Creation of a strong networking effort among the funded systems biology projects, in order to share resources on successful methodological approaches and tools with the broader systems biology and clinical community; there is a need to build on already existing systems biology initiatives without re-inventing. There is need for establishment of proof-of-concept for medical applications in order to attract industry into the area. Most importantly, the relevant clinical needs should be the driving force to pave the way for systems medicine.

The importance of formulating the right questions in systems biology is important. Systems biology is neither data gathering, nor solely predictive modeling but about a ‘systems’ understanding of biological systems in health and disease. The challenge is integration of complex data, across time, space and different organizational levels, without forgetting interactions with the external environment. Also fundamental is comparison across different experimental systems. Clinicians should have an overview of the new technologies that have enabled systems biology from – omics that detail our genome, transcriptome, metabolome and proteome to advanced imaging techniques in biology and medicine. The challenge lies in transforming the data produced by the new technologies into new knowledge in biological and medical sciences. This is achieved by common questions that need to be formulated together by researchers and medical doctors. In addition, short and long term goals need to be addressed. One has to identify opportunities for the future and the bottlenecks for systems medicine to become a reality and challenges ahead if systems biology would be applied to medicine.


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