Understanding disease mechanisms with models of signaling pathway activities

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• 作者：Patricia Sebastian-Leon (1)
  Enrique Vidal (1) (2) (3)
  Pablo Minguez (1) (4)
  Ana Conesa (1)
  Sonia Tarazona (1)
  Alicia Amadoz (1)
  Carmen Armero (5)
  Francisco Salavert (1) (2)
  Antonio Vidal-Puig (6)
  David Montaner (1)
  Joaqu铆n Dopazo (1) (2) (7)
  
  1. Department of Computational Genomics ; Centro de Investigaci贸n Pr铆ncipe Felipe (CIPF) ; Avda. Autopista del Saler ; 16 ; 46012 ; Valencia ; Spain
  2. BIER ; CIBER de Enfermedades Raras (CIBERER) ; Valencia ; 46012 ; Spain
  3. Present Address ; Cancer Epigenetics and Biology Program (PEBC) ; Bellvitge Biomedical Research Institute (IDIBELL) ; L鈥橦ospitalet de Llobregat ; Barcelona ; Spain
  4. Present Address ; Structural and Computational Biology ; European Molecular Biology Laboratory ; Heidelberg ; 69117 ; Germany
  5. Department of Statistics and Operations Research ; University of Valencia ; Valencia ; 46100 ; Spain
  6. Institute of Metabolic Science - Metabolic Research Laboratories and Department of Clinical Biochemistry ; University of Cambridge ; Addenbrooke鈥檚 Hospital ; Cambridge ; CB2 0QQ ; UK
  7. Functional Genomics Node ; (INB) at CIPF ; Valencia ; Spain
  
• 关键词：Signaling pathways ; Probabilistic model ; Disease mechanism ; Precision medicine ; Disease mechanism ; Cancer ; Fanconi anemia ; Obesity ; Stem cells
• 刊名：BMC Systems Biology
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：8
• 期：1
• 全文大小：1,756 KB
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• 刊物主题：Bioinformatics; Systems Biology; Simulation and Modeling; Computational Biology/Bioinformatics; Physiological, Cellular and Medical Topics; Algorithms;
• 出版者：BioMed Central
• ISSN：1752-0509

文摘

Background Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. Results Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. Conclusions The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.