Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment.

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models.

Meta-analysis of heterogeneous Down Syndrome data reveals consistent genome-wide dosage effects related to neurological processes.

Background: Down syndrome (DS; trisomy 21) is the most common genetic cause of mental retardation in the human population and key molecular networks dysregulated in DS are still unknown. Many different experimental techniques have been applied to analyse the effects of dosage imbalance at the molecular and phenotypical level, however, currently no integrative approach exists that attempts to extract the common information.

Results: We have performed a statistical meta-analysis from 45 heterogeneous publicly available DS data sets in order to identify consistent dosage effects from these studies.