A new algorithm capable of analyzing models of biological systems can lead to greater understanding of their underlying decision-making mechanisms, with implications for studying how complex behaviors are rooted in relatively simple actions.
Pennsylvania State University (Penn State)'s Jordan Rozum said the modeling framework includes Boolean networks.
Said Penn State's Reka Albert, "Boolean models describe how information propagates through the network," and the nodes' on/off states eventually slip into repeating patterns that correspond to the system's stable long-term behaviors.
Complexity can scale up dramatically as the system incorporates more nodes, particularly when events in the system are asynchronous. The researchers used parity and time-reversal transformations to boost the efficiency of the Boolean network analysis.
From Penn State News
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