Systems medicine: helping us understand the complexity of disease

Abstract

Advances in genomics and other -omic fields in the last decade have resulted in unprecedented volumes of complex data now being available. These data can enable physicians to provide their patients with care that is more personalized, predictive, preventive and participatory. The expertise required to manage and understand this data is to be found in fields outside of medical science, thus multidisciplinary collaboration coupled to a systems approach is key to unlocking its potential, with concomitant new ways of working. Systems medicine can build on the successes in the field of systems biology, recognizing the human body as the multidimensional network of networks that it is. While systems medicine can provide a conceptual and theoretical framework, its practical goal is to provide physicians the tools necessary for harnessing the rapid advances in basic biomedical science into their routine clinical arsenal.

Problems, challenges and promises: perspectives on precision medicine

“…The practice of medicine is currently undergoing a paradigm shift from the primarily reactive medicine of the past to a more proactive predictive medicine aimed at disease prevention [1][2][3][4][5][6][7][8][9]. Rather than treating a disease, there is a shift towards the treatment of individual patients, based on a personalised data driven approach.…”

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

“…Furthermore, quantitation of metabolites, which concentration varies as a consequence of the disease, is important to identify new biomarkers [21]. The 'omics' may help to identify these changes in metabolism or signaling and the results could be integrated to get more information, since there are metabolites, genes, and proteins that are constantly interacting and that may be responsible for the disease pathophysiology [22]. Nevertheless, it is necessary to use computational tools to manipulate and integrated this data, such as Archimedes for diabetes [23], diverse signaling and metabolic networks stored at Biomodels, the network-based drug-repositioning PROMISCUOUS a database for; [24], or enhanced pharmacodynamics models [25], among others.…”

Models and Data Sources Used in Systems Medicine

“…Both are "practically identical concepts, except [systems biology] is general and [systems medicine] is particularly focused on medicine" [19]. Vandamme et al state, systems biology has the "potential to make a more translational impact in the arena of medical science" [50] while systems medicine is supposed to "aid physicians in handling more complex data in their day-to-day practice" [33]. For Wang et al this can be done, for example, by detecting and stratifying various pathological conditions with the help of "clinically detectable molecular fingerprints resulting from disease-perturbed biological networks" [44], as discovered in the domain of systems biology.…”