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Bree Cummins1, Tomas Gedeon1, Shaun Harker2

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The Dynamic Signatures for Genetic Regulatory Network (DSGRN) database provides a computational framework to analyze complex gene regulatory networks. It enables searching for dynamical behaviors like bistability and oscillations in biological systems.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding the global dynamics of large gene regulatory networks (GRNs) with multiple interactions and parameters is crucial but computationally challenging.
  • Existing methods struggle to analyze the complex dynamics of signal transduction and gene regulatory networks involving 5-10 nodes or more.

Purpose of the Study:

  • To develop a theoretical and computational framework for the Dynamic Signatures for Genetic Regulatory Network (DSGRN) database.
  • To enable effective searching for dynamical signatures within complex biological networks.

Main Methods:

  • Constructed a computational model based on switching networks from regulatory network inputs (directed graphs).
  • Decomposed phase space into cells, describing dynamics combinatorially via state transition graphs and annotated Morse graphs.
  • Developed a computable finite decomposition of parameter space to identify domains with constant dynamical descriptions.

Main Results:

  • Created an SQL database storing dynamical signatures (e.g., bistability, oscillations, stable equilibria) derived from annotated Morse graphs.
  • Demonstrated the framework with simple 3-node networks and a 5-node p53 network example.
  • The DSGRN database effectively represents and allows querying of global network dynamics across parameter space.

Conclusions:

  • The DSGRN database offers a novel approach to understanding the complex dynamics of genetic regulatory networks.
  • This framework facilitates the discovery of specific dynamical behaviors within biological signaling pathways.
  • The computational approach provides a scalable solution for analyzing large and complex biological networks.