DSGRN: Examining the Dynamics of Families of Logical Models

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We present a computational tool DSGRN for exploring the dynamics of a network by computing summaries of the dynamics of switching models compatible with the network across all parameters. The network can arise directly from a biological problem, or indirectly as the interaction graph of a Boolean model. This tool computes a finite decomposition of parameter space such that for each region, the state transition graph that describes the coarse dynamical behavior of a network is the same. Each of these parameter regions corresponds to a different logical description of the network dynamics. The comparison of dynamics across parameters with experimental data allows the rejection of parameter regimes or entire networks as viable models for representing the underlying regulatory mechanisms. This in turn allows a search through the space of perturbations of a given network for networks that robustly fit the data. These are the first steps toward discovering a network that optimally matches the observed dynamics by searching through the space of networks.




Cummins, Bree, Tomas Gedeon, Shaun Harker, and Konstantin Mischaikow. "DSGRN: Examining the Dynamics of Families of Logical Models." Frontiers in Physiology 9 (June 2018). DOI:10.3389/fphys.2018.00549.
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