DSGRN: Dynamic Software for Network Discovery

Breschine Cummins (June 28, 2019)

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Abstract

The mathematical field of dynamical systems plays a crucial role in describing the behavior of a cellular or genetic regulatory network over time. Traditional dynamical systems studies concentrate on trajectories and invariant sets as the primary approaches to network analysis. We present a new angle on dynamical systems that instead focuses on a robust, scalable and computable description of dynamics in terms of graphs and partially ordered sets (posets). A poset represents a “dynamic signature� of the network that is constant over a large region of parameter space. The number of such parameter regions is finite, leading to a global description of the dynamics across high dimensional parameter space. Our software tool Dynamic Signatures Generated by Regulatory Networks (DSGRN) ingests a regulatory network and produces the posets representing network dynamics over all of parameter space. The dynamic signatures generated by DSGRN can be used to answer questions about regulatory network performance in the context of network discovery, as well as other goals such as network design in synthetic biology and diagnosis of misbehavior. I will briefly overview the graphical approach of DSGRN and then discuss the role of DSGRN in a pipeline for network discovery using a case study of time series data measured in vitro from the malaria parasite P. falciparum.