Washington: One is familiar with the disturbance in the circadian rhythm or the 24-hour biological cycle, which manages nearly all aspects of metabolism, from sleep-wake cycles to body temperature to digestion. Every cell in the body has a circadian clock, but researchers are unclear about how networks of cells connect with each other over time and how those time-varying connections impact network functions.
Researchers at Washington University developed a unified, data-driven computational approach, named as the ICON (infer connections of networks) method, to infer and reveal these connections in biological and chemical oscillatory networks, known as the topology of these complex networks, based on their time-series data.
Abnormal synchrony has been linked to a variety of brain disorders, such as epilepsy, Alzheimer’s disease, and Parkinson’s disease.
Researchers first tested their method on a simulated network of different sizes they created. Next, they tested the method on a network of oscillators — populations of dynamic units that repeatedly fire together, go silent, then fire together again — created in the lab. When they applied the algorithm to the network of interactions among the synthetic oscillators, the results matched the previous experiments, finding the same connections in a network of 15 chemical oscillators. Such prediction of this dynamic topology was not previously possible, the researchers said.
“The connection at one time may be strong, but at another time it may be stronger or weaker, so we can use this data to recover the functional connectivity. If we know this, then we know the network, then we can do more study and investigate over time whether this network will be synchronized or whether specific dynamic patterns will emerge,” said Jr-Shin Li, a researcher.
They also stated that ICON would help them and other scientists to understand principles that allow systems to efficiently synchronize.
In another experiment, the researchers tested the method on seven groups of five mice who were housed together for a period of time as social networks.
They measured the oscillations of the mice at the end of the experiment and then applied the algorithm to infer results from the data. In the end, researchers found that four of the groups of mice had social synchronization because they had the same body temperatures at the end of their time together.
The findings appeared in the Journal of Proceedings of the National Academy of Sciences.