In a complex system, such as a human genome containing 20,000 interconnected genes, it is almost impossible to monitor the entire system at once. According to the physicist organization network recently, researchers from Northeastern University, Massachusetts Institute of Technology and other units have developed a new algorithm that can identify subunits or necessary nodes of complex systems, making it possible to monitor large complex systems . Related papers were published in the recently published "Journal of the National Academy of Sciences".
Complex systems such as biological genomes, biochemical reaction systems, social networks, etc., are composed of many interrelated sub-parts, and any change in one part will affect the rest. Therefore, it is extremely difficult to analyze and monitor a complex system. "The essence of a complex system is the connection, and these components must rely on these connections to distribute information to the entire network. Therefore, we do not have to monitor each part, but we can also grasp the entire system as a whole." One of the authors of the paper, Northeast Albert Laszlo Barabas, a professor of physics at the College of Biology and the School of Computer and Information Science, said that the method they designed took advantage of the interdependence of the subunits of complex systems and could look beyond the number limit to observe the overall situation of the system.
Using this new algorithm, the research team for the first time determined all the mathematical equations used to describe the dynamics of the system, such as calculating the impact of a small reaction between peripheral molecules on the final result of the entire system in a biochemical reaction system. By observing the impact of each response on system variables, a graph of the entire system can be drawn. The connecting points are the basis of the graph, and each point is indispensable for understanding the rest of the system.
"What surprises us is that in most of the examples, the necessary nodes are also sufficient conditions." Said Liu Yangyu, an associate professor at Northeastern University's Complex Network Research Center, as long as these necessary nodes are present, no other part is needed to know The whole situation of the whole system.
The researchers point out that the new method combines cybernetics, graph theory, and network science to simplify large and complex systems into a set of necessary "sensing nodes." For example, the biological metabolism system is a collection of hundreds of molecules and thousands of biochemical reactions. The new algorithm can greatly simplify the process of identifying biomarkers. Biomarkers are molecules in the blood that can help doctors discern whether a person is healthy.
"Most of the current biomarkers are almost randomly selected, only chemists and doctors have found that they work." Barabbas said, "And our method is observable, providing a rational way to select biomarkers , As long as we know what system to monitor. "
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