Experimental data on gene regulation is mostly qualitative, where the only information available about pairwise interactions is the presence of either up- or down- regulation. Quantitative data is often subject to large uncertainty and is mostly in terms of fold differences. Given these realities, it is very dicult to make reliable predictions using mathematical models. The current approach of choosing reasonable parameter values, a few initial conditions and then making predictions based on resulting solutions is severely subsampling both the parameter and phase space. In addition, this approach does not provide provable predictions about the dynamics.
We present a new approach that uses continuous time Boolean networks as a platform for qualitative studies of gene regulation. We compute Dynamics Signatures Generated by Regulatory Networks (DSGRN) Database that provides a queryable description of global dynamics over the entire parameter space. The results obtained by this method provably capture the dynamics at a predetermined spatial scale.
We apply our approach to study a neighborhood of a given network in the space of networks. We start with a E2F-Rb network underlying the mammalian cell cycle restriction point and show that a large portion of the parameters support either the proliferative state, quiescent state, or bistability between these two states. We sample perturbations of this network and study robustness of this dynamics in the network space. We also show how to use our approach to propose, based on time series data, a network responsible for cyclical emergence of malaria parasite from red blood cells.

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