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WIREs Syst Biol Med
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Systems biology and the future of medicine

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Contemporary views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine. WIREs Syst Biol Med 2011 3 619–627 DOI: 10.1002/wsbm.144

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Figure 1.

The human systems biology universe. Human systems comprise molecular and phenotypic networks, which are related to, but distinct from, each other, as indicated by the separate linked ovals. The human disease‐ome represents a collection of subnetworks, the disease modules, which are identified by one of two strategies, the molecular network‐based strategy or the functional and structural similarity‐based strategy. The assembly of disease modules into the disease‐ome can be determined by bioinformatics‐based approaches—the shared gene formalism, the shared metabolic pathway formalism, or the disease comorbidity formalism—or by laboratory‐based experimentation.

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Alex Hoffmann

Alex Hoffmann

Dr. Hoffmann is Associate Professor of Signaling Systems Laboratory at UCSD. He moved to biology, tempted by the sense of discovery that he felt was missing as an undergraduate in Physics. During his graduate studies with Dr. Bob Roeder, he identified the genes that make up the transcription factor TFIID. Later on with Dr. David Baltimore, he sought to understand dynamic signaling pathways, realizing that his mathematical background may be relevant to study problems in a quantitative manner.

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