Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/115904
Title: Systems Medicine: from molecular features and models to the clinic in COPD
Author: Gomez Cabrero, David
Menche, Jörg
Cano Franco, Isaac
Abugessaisa, Imad
Huertas Migueláñez, M. Mercedes
Tenyi, Akos
Marín de Mas, Igor Bartolomé
Kiani, Narsis A.
Marabita, Francesco
Falciani, Francesco
Burrowes, Kelly
Maier, Dieter
Wagner, P. D. (Peter D.)
Selivanov, Vitaly A.
Cascante i Serratosa, Marta
Roca Torrent, Josep
Barabási, Albert László
Tegnér, Jesper
Keywords: Malalties cròniques
Malalties pulmonars obstructives cròniques
Comorbiditat
Simulació per ordinador
Investigació mèdica
Chronic diseases
Chronic obstructive pulmonary diseases
Comorbidity
Computer simulation
Medicine research
Issue Date: 28-Nov-2014
Publisher: BioMed Central
Abstract: BACKGROUND AND HYPOTHESIS: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. OBJECTIVE AND METHOD: Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. RESULTS: In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. CONCLUSIONS: The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
Note: Reproducció del document publicat a: https://doi.org/10.1186/1479-5876-12-S2-S4
It is part of: Journal of Translational Medicine, 2014, vol. 12, num. Suppl 2, p. S4
URI: http://hdl.handle.net/2445/115904
Related resource: https://doi.org/10.1186/1479-5876-12-S2-S4
ISSN: 1479-5876
Appears in Collections:Articles publicats en revistes (Bioquímica i Biomedicina Molecular)
Articles publicats en revistes (Medicina)

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