title: Functional genomics, evo-devo and systems biology: a chance to overcome complexity?
abstract: OBJECTIVE
  This review addresses the key question of how to integrate a high complexity of processes and data to a unifying picture of disease processes and progression relevant for osteoarthritis.
  
  
  RESULTS
  Many research efforts in the last few years have resulted in the accumulation of a huge amount of data. To date, however, these data have not led to a unifying concept of the pathogenesis and progression of the osteoarthritic disease process. Methods to integrate a lot of information are needed, therefore, in order to progress from experimental findings to practical knowledge. Several such strategies have been followed up in the past: in-vitro models, large-scale gene expression analysis/functional genomics, and an attempt to interpret gene expression patterns on the basis of developmental chondrocyte differentiation. A novel approach is systems biology, which promises to overcome issues of complexity using appropriate models and quantitative simulation.
  
  
  CONCLUSIONS
  Efforts are required to integrate a continuously growing high complexity of experimental data into an understanding of the joint system and its derangement in osteoarthritis. Modelling of the 'whole' picture appears to be needed so that we do not get lost in the plethora of details.
authors.affiliation: Institute of Pathology, University of Leipzig, Leipzig, Germany. thomas.aigner@medizin.uni-leipzig.de
authors.forename: Thomas
authors.initials: T
authors.lastname: Aigner
authors.forename: Jochen
authors.initials: J
authors.lastname: Haag
authors.forename: Ralf
authors.initials: R
authors.lastname: Zimmer
mesh_terms: D002356:Cartilage / Q000254:growth & development / Q000503:physiopathology; D019902:Chondrocytes / Q000502:physiology; D019295:Computational Biology; D015509:Developmental Biology; D023281:Genomics; D006801:Humans; D010003:Osteoarthritis / Q000235:genetics* / Q000503:physiopathology; D015398:Signal Transduction; D049490:Systems Biology