Editorial: The Impact of Systems Medicine on Human Health and Disease

Complex disorders including obesity, diabetes, fatty liver diseases, cardiovascular disease (CVD), and cancer are results from a combination of genetic, environmental, and lifestyle factors. The prevalence of such disorders has increased dramatically in the last two decades and there is an urgent need for the development of new prognostic tools for the treatment of such diseases. However, this requires a deep understanding of the underlying molecular mechanisms involved in the occurrence of the diseases. With the advances in high throughput technologies, biological components of cells can be measured with a very high resolution and these data can be used for investigating whole systems properties using a network-based approach. Systems medicine provides an integrative platform for studying the interactions between the biological components of the cell using a holistic approach and generating mechanistic explanations for the emergent systems properties (Mardinoglu and Nielsen, 2012). This inter-disciplinary field of study allows for understanding biological processes of cells in health and disease states, gaining new insights into what drives the appearance of the disease, and finally identifying proteins and metabolites implicated in human disease. Systems medicine utilizes mathematical approaches to generate models which can be employed for designing new sets of experiments and for mapping the response of the system to perturbations quantitatively. These models as well as the developed tools can accelerate the emergence of personalized medicine which can transform the practice of medicine and offer better targets for drug development with minimum side effects. Genome Scale Metabolic Models (GEMs) are important tools in systems biology and employed for simulating the metabolism of cells/tissues in different states (Mardinoglu et al., 2013). GEM includes all known metabolic reactions and associated protein coding genes in a particular cell or tissue and the reconstruction of GEMs is costly and time consuming process. Pacheco et al. benchmarked the algorithms that have been used in the automated reconstruction of context-specific GEMs. This may allow researchers to identify limitations of each method and help them to increase the quality of context-specific reconstruction algorithms and as well as the generated models. Zhang and Hua reviewed the potential applications of GEMs in systems medicine and industrial biotechnology. The authors described the key concepts and assumptions used in the application of the GEMs and provided detailed explanation about the recent applications which may promote the use of GEMs by biologists. GEMs have been widely used in the discovery of biomarkers and drug …