2013 DREAM 8 Challenges
We are pleased to announce that the DREAM8 Challenges will be open for participation starting June 10, 2013. The three Challenges described below will run through the summer with final submissions scored in September, 2013. The best performers in these Challenges will be invited to present in the joint RECOMB Systems Biology/Regulatory Genomics/DREAM8 Conference taking place in Toronto, Canada in November 2013.
About DREAM Challenges
Sage Bionetworks and DREAM are convinced that running open computational Challenges focused on important unsolved questions in systems biomedicine can help advance basic and translational science. By presenting the research community with well-formulated questions that usually involve complex data, we effectively enable the sharing and improvement of predictive models, accelerating many-fold the analysis of such data. The ultimate goal, beyond the competitive aspect of these Challenges, is to foster collaborations of like-minded researchers that together will find the solution for vexing problems that matter most to citizens and patients.
During the “Challenge season” spanning from June to September 2013, Sage Bionetworks and DREAM plan to run the three Challenges described below. The Challenges' top performing teams will be provided with travel grants and an invitation to present their results at the annual DREAM conference taking place Nov 8-12, 2013 in Toronto, Canada.
If you want to sign up for one of these Challenges, please click on "Read More" next to its description below.
HPN-DREAM Breast Cancer Network Inference Challenge
The goal of the Heritage-DREAM Breast Cancer RTK Network Reconstruction Challenge is to use crowd-sourcing to increase our understanding of the signaling pathways at work in breast cancer.
To that end, participants in this Challenge will be provided with an extensive proteomics time-course dataset on four breast cancer cell lines. Participants will be tasked with analyzing these data to solve the following 3 sub-challenges: 1) build network models that represent the active cell signaling pathways in breast cancer, 2) predict the dynamic response of various phospho-proteins to drug perturbations, and 3) propose novel strategies to visualize these high dimensional data.
NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge
The NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge represents a groundbreaking new direction for toxicity testing and is intended to help us understand how genetic variation affects individual response to common environmental and pharmaceutical chemicals. For this Challenge, Sage Bionetworks and DREAM are teaming up with scientists at the National Institute for Environmental Health Sciences (NIEHS), the National Center for Advancing Translational Sciences (NCATS), and the University of North Carolina at Chapel Hill (UNC). These groups have generated population-scale toxicity data in a human ex-vivo model system. To do this, they leveraged the 1000 Genomes Project, which provides public access to genotype and transcriptomic data derived from cell lines collected from thousands of individuals representing 9 distinct geographical populations with defined genetic heterogeneity. The NIEHS/NCATS/UNC team conducted the largest ever population-based ex-vivo cytotoxicity study by treating 920 of these cell lines with approximately 170 common, pharmaceutical or important environmental chemicals. The NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge aims to ask the “crowd” of researchers to use the 1000 genomes genetics, genomics and cytotoxicity data to build models that can predict: (1) the toxic response of individuals to each chemical based on genetics and genomics data; (2) the parameters of distribution for the toxic effects of each chemical (e.g., mean and variance of toxic response across the population) based primarily on chemical information about the compounds being evaluated.
For every chemical that has been tested for toxicity, there are myriad others that still remain untested. Thus the best models resulting from the NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge that can accurately predict either what groups of individuals will be most sensitive to chemicals or the range of toxicity for different types of chemicals, will provide powerful new tools for EPA and other government agencies to do more targeted experimentation based on computational predictions.
Best performers will be invited to present at the DREAM conference with travel expenses covered by the Sage/DREAM organizers. We are working with high impact journals to ensure that the methodology developed by the best performer will be considered for publication under the challenge-assisted peer review format.
The Whole-Cell Parameter Estimation DREAM Challenge
The “DREAM Whole-cell parameter estimation challenge” is a crowd-sourcing effort organized by the DREAM project and Sage Bionetworks, in collaboration with Prof. M. Covert’s lab from Stanford University, in which the ability of the community to infer the kinetic parameters underlying biological processes will be tested. Building upon two previous DREAM challenges to infer the parameters of small biological networks, this challenge will be based on a first-of-its-kind whole-cell computational model of the human pathogen Mycoplasma genitalium [Cell, 150 2, 389-401 (2012)]. The model is unique in that it integrates diverse mathematical approaches to account for all the essential cellular processes as well as all annotated genes, resulting in a number of biological predictions that were validated experimentally. The challenge consists of predicting a subset of the kinetic parameters used in the model to represent fundamental biological processes. Many parameter estimation processes done in the modeling community field start from already existing experiments, from which researchers fit the parameters of their models. The present challenge will be based on a parameter inference process comprising an experimental design component that forms an integral part of the optimization procedure. The challenge will be structured around a credit budget system in which participants can purchase in-silico generated data of their choice (within a restricted set of possibilities) and use it to infer the model parameters. This selection can be done iteratively until the budget is exhausted, each round choosing the experiment most needed according to the current model constrains. This iterative data-acquisition setup proved very efficient for modelers to accurately find the actual parameters underlying the biological networks in the two previous DREAM parameter estimation challenges. The present challenge, however, presents modelers with a substantial leap in computational demands as hundreds of parameter values will have to be predicted from a model that is orders of magnitude more complex than in the previous DREAM runs. As the model requires extensive computational power, we expect to provide participants with a platform to run the whole-cell model and test their parameter optimization methods. When applied to real experimental systems, the results of this challenge will show how well we can reverse engineer the kinetics of cellular processes. In the longer run this understanding will allow us to model cellular behavior, predict a cell’s response to external agents such as therapeutic or toxic compounds and allow for drug target identification among many potential applications.
As in the other challenges, the best performing team will be invited to present at the DREAM conference with travel expenses covered. Sage/DREAM organizers are also working with high impact journals to ensure that the best performer methodology could be considered for publication under the challenge-assisted peer review format.
DREAM-DEMO-J
DREAM8 Press Releases
Click here to read the Heritage Provider Network press release
Click here to read the April 19, 2013 DREAM8 Challenge press release.
