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WIREs Syst Biol Med
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Systems medicine: evolution of systems biology from bench to bedside

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High‐throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high‐throughput data and predictive computational modeling to understand biological functions as system‐level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision. WIREs Syst Biol Med 2015, 7:141–161. doi: 10.1002/wsbm.1297 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods Translational, Genomic, and Systems Medicine > Translational Medicine
High‐throughput data and their hierarchical relationships in describing cellular phenotypes or human diseases. Each type of biological data represents a certain dimension of complex biological systems. Interpretation of cellular phenotypes or human diseases using systems biology approaches requires integration of all heterogeneous high‐throughput data types.
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Basic elements of systems medicine. System medicine is far more than systems biology of human diseases. It comprehensively integrates computational modeling, 'omics data, physiological data, clinical data, and environmental factors to address major human diseases uniquely, efficiently, and with personalized precision.
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An example of network‐based drug repositioning. (a) Drug repositioning by identifying new drug–target interactions (the dotted line). (b) Drug repositioning by identifying new target–disease associations (the dotted line).
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Directionality of disease progression. (Reprinted with permission from Ref . © 2009 Hidalgo et al. (a) Distribution of λ1→2. (b) Disease precedence Λi as a function of disease prevalence Pi. The inset shows the same plot after removing the trend from disease precedence [Λi* = Λi + 496.08 log10(Pi) − 2446.2]. (c) Disease connectivity calculated from the ϕ‐phenotypic disease network (PDN) as a function of Λi*. The yellow line shows the best fit for the 518 diseases with a prevalence larger than 1/500 (yellow circles), while the red line shows the best fit for the 463 diseases at the center of the cloud (red points). The correlation coefficient is represented by r and its associated P‐value by P. (d) Percentage of patients who died 2 and 8 years after being diagnosed with a disease with a given detrended precedence Λi*. The yellow lines show the best fit for all the 518 diseases (yellow circles), while the red lines show the fit for the 434 (top panel) and 465 (bottom panel) diseases at the bulk of the cloud.
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Illustration of some concepts of network analysis. (a) Network biomarker. Different from traditional individual biomarkers, a network biomarker is a subnetwork consisting of two or more differentially expressed components in control samples versus disease samples. (b) Differential network analysis examines the same network over two different conditions, highlighting the topological changes induced by diseases. (c) A disease module represents a group of nodes whose perturbation can be linked to a particular disease phenotype. (d) Network alignment compares two networks from different species and aligns orthologous components and their interactions.
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An overview of computational modeling methods used in systems biology. Computational approaches in systems biology apply a wide spectrum of mathematical formalisms across different scales, ranging from data‐driven top‐down methods to model‐driven bottom‐up methods, and from static qualitative models to dynamic quantitative models.
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Laboratory Methods and Technologies > Macromolecular Interactions, Methods
Analytical and Computational Methods > Computational Methods
Translational, Genomic, and Systems Medicine > Translational Medicine

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