A new algorithm developed at Northwestern University aims to improve discovery of gene regulatory networks. According to the developers, current approaches often do not effectively use temporal information and can lead to inaccurate network inference.
"We want to understand how cells make decisions, so we can control the decisions they make," said Neda Bagheri, assistant professor of chemical and biological engineering and senior author on the paper published yesterday in PNAS. "A cell might decide to divide uncontrollably, which is the case with cancer. If we understand how cells make that decision, then we can design strategies to intervene."
Bagheri’s new machine-learning algorithm that can help connect the dots among the genes' interactions inside cellular networks is called "Sliding Window Inference for Network Generation," or SWING.
SWING puts together a more complete picture of the cause-and-effect interactions happening among genes by incorporating time delays and sliding windows, she says. Rather than only looking at the individual perturbations and responses, SWING uses time-resolved, high-throughput data to integrate the time it takes for those responses to occur.
"The dynamics are really important because it's not just if the cell responds to a certain input, but how," Bagheri added. "Is it slow? Is it fast? Is it a pulse-like or more dynamic? If I introduced a drug, for example, would the cell have an immediate response and then recover or become resistant to the drug? Understanding these dynamics can guide the design of new drugs."
After designing the algorithm, Bagheri's team validated it in the laboratory in both computer simulations and in vitro in E. coli and S. cerevisiae models. The algorithm is open source and available online. And although it was initially designed to probe the interior, mysterious life of cells, the algorithm can be applied to many subjects that display activity over time.
Image: The Sliding Window Inference for Network Generation, or SWING, algorithm puts together a more complete picture of cause-and-effect interactions among genes. Image courtesy of Neda Bagheri, Justin Finkle, and Jia Wu.