Much of the recent focus on reverse engineering of gene networks has been on the identification of causal interaction topologies between genes. Indeed that was the focus of several challenges in past editions of DREAM. Such interactions are usually inferred from high-dimensional gene expression experiments involving different perturbations to the network of interest. Once network interaction topologies are characterized with some reasonable level of confidence, how can we decide between models having very similar topologies and how do we characterize the actual kinetics of these networks in a way that accurately reflects the causal relationships implied in the proposed topology?
In addressing these questions, two key areas that require attention are the tasks of estimating model parameters from data in the case of a known (assumed) model structure, and designing the most informative experiments for inference of parameters and correct network topology. These reverse engineering tasks provide the area of focus for the current challenge, namely the Network Topology and Parameter Inference Challenge.
The desired outcome for the Network Topology and Parameter Inference Challenge is the development, improvement and application of optimization methods (for model parameter estimation) and experimental design that are particularly well-suited for models of complex (highly-parameterized) systems and for which data are limited, often quite noisy and experiments need to be designed iteratively with the model building process. Despite the fact that these conditions are commonly encountered in biological systems modeling, the problem of parameter estimation and iterative experimental design remains one of the hardest challenges in systems biology.
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
Pablo Meyer, Thomas Cokelaer, Deepak Chandran, Kyung Hyuk Kim, Po-Ru Loh, George Tucker, Mark Lipson, Bonnie Berger, Clemens Kreutz, Andreas Raue, Bernhard Steiert, Jens Timmer, Erhan Bilal, DREAM 6&7 Parameter Estimation consortium, Herbert M Sauro, Gustavo Stolovitzky and Julio Saez-Rodriguez
BMC Systems Biology 8:13. 2014
Link to BMC Systems Biology