Translational Notes: DREAM team 

 

Table 1. Sage Bionetworks-DREAM Project spring 2013 challenges. Sage Bionetworks and The DREAM Project have announced four open-innovation computational challenges that tackle issues relevant to drug discovery and development. The challenges will run between May and September. The partners expect to announce another round of challenges in the fall.
Source: Sage Bionetworks

Title

Description

Data source

Sponsor

HPN-DREAM Breast Cancer Network Interference Challenge

Use quantitative proteomic data to: (i) build network models that represent the active pathways and their response to different stimuli during drug treatment;
(ii) predict the responses of phosphoproteins to various drugs; and (iii) propose new visualization strategies for the high-dimensional data sets

Oregon Health & Science University; The University of Texas MD Anderson Cancer Center; The Netherlands Cancer Institute

Heritage Provider Network Inc. (HPN); National Cancer Institute

NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge

Use genetic and toxicology data to build computational models that can predict: (i) the toxic response of individuals to each chemical based on genetics and genomics data; and (ii) the parameters of distribution for the toxic effects of each chemical based primarily on chemical information about the compounds being evaluated

National Institute of Environmental Health Sciences (NIEHS); National Center for Advancing Translational Sciences (NCATS); The University of North Carolina at Chapel Hill (UNC)

To be announced

National Brain Tumor Society-DREAM Cancer Prediction Challenge

Determine whether systems biology-based models of human glioblastoma multiforme (GBM) are sufficiently advanced to allow the correct prediction of single agents or combinations of drugs that may abrogate tumorigenesis or significantly delay tumor growth in vivo

Multiple sources to be announced

National Brain Tumor Society

Whole-Cell Parameter Estimation DREAM Challenge

Refine a whole-cell computational model describing the biology of Mycobacterium genitalium by predicting a subset of the kinetic parameters used to represent fundamental biological processes, with the goal of determining how accurately the kinetics of cellular processes can be reverse engineered

Stanford University

To be announced