Abstract
BackgroundAccurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.ResultsWe proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation.ConclusionsA total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.
Highlights
Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes
We found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission
We ran a second challenge, the network topology inference challenge, where participants were given an incomplete topology with 11 genes and asked to find 3 missing links in the model
Summary
Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. Building models requires a list of molecular components and their interactions This list can be assembled from prior knowledge and/or inferred, or reverse engineered, from dedicated experimental data [1,2,3]. This can be done using a simple causal formalism or, if enough mechanistic detail is available, by writing down the corresponding biochemical reactions. A common and natural way to model biochemical reactions is to derive a dynamical system, typically in the form of ordinary differential equations These equations include associated parameters that quantify the underlying physicochemical processes such as protein binding and enzyme activity. An accurate estimation of the parameters is fundamental to quantitatively understand a system and provide reliable predictions [7,8]
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