Functional genomics, evo-devo and systems biology: a chance to overcome complexity?

Curr Opin Rheumatol. 2007 Sep;19(5):463-70. doi: 10.1097/BOR.0b013e3282bf6c68.


Purpose of review: 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.

Recent findings: 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.

Summary: 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.

Publication types

  • Review

MeSH terms

  • Cartilage* / growth & development
  • Cartilage* / physiopathology
  • Chondrocytes / physiology
  • Computational Biology
  • Developmental Biology
  • Genomics
  • Humans
  • Osteoarthritis / genetics*
  • Osteoarthritis / physiopathology
  • Signal Transduction
  • Systems Biology*