Influence of network topology and data collection on network inference.
We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.
Smith
V Anne
VA
Department of Neurobiology, Duke University Medical Center, Box 3209, Durham, NC 27710, USA.
Jarvis
Erich D
ED
Hartemink
Alexander J
AJ
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005786:Gene Expression Regulation; D006801:Humans; D008957:Models, Genetic; D020308:Songbirds / Q000502:physiology; D014828:Vocalization, Animal