Northwestern University researchers say they have developed a new machine-learning algorithm that can help scientists better understand the genetic interactions inside cellular networks. Called “Sliding Window Inference for Network Generation,” or SWING, the algorithm uses time-series data to reveal the underlying structure of cellular networks.
The research (“Windowed Granger causal inference strategy improves discovery of gene regulatory networks”) is published in PNAS.
“Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information,” write the investigators.
“We address this limitation by introducing SWING, a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays.
“We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods.
“By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.”
“We want to understand how cells make decisions, so we can control the decisions they make,” said Neda Bagheri, Ph.D., assistant professor of chemical and biological engineering. “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.”
In biological experiments, researchers often perturb a subject by altering its function and then measure the subject's response. For example, researchers might apply a drug that targets a gene's expression level and then observe how the gene and downstream components react.
But it is difficult for those researchers to know whether the change in genetic landscape was a direct effect of the drug or the effect of other activities taking place within the cell.
“While many algorithms interrogate cue-signal responses,” said grad student Justin Finkle, “we used time-series data more creatively to uncover the connections among different genes and put them in a causal order.”
SWING puts together a more complete picture of the cause-and-effect interactions happening among genes by incorporating time delays and sliding windows, explained Dr. Bagheri. 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, she added.
“Other algorithms make the assumption that cellular responses appear more-or-less uniformly in time,” said Jia Wu, another grad student in Dr. Bagheri’s lab. “We incorporated a window that includes different temporal ranges, so it captures responses that have dynamic profiles or different delays in time.”
“The dynamics are really important because it's not just if the cell responds to a certain input, but how,” continued Dr. Bagheri. “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, Dr. 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 now available online.