In network science, a motif denotes a recurring and statistically significant subgraph pattern within a larger network. These motifs often depict common structural or functional features observed frequently in biological networks, such as feedforward loops, feedback loops, or signaling pathways.
Dynamic modeling of network motifs involves examining how these motifs evolve and interact over time, taking into account factors like changes in node states, inter-node interactions, and external influences on the network. This approach enables researchers to uncover insights into the dynamic behavior of biological systems at the level of network motifs, offering valuable information regarding system stability, robustness, and responses to stimuli or perturbations.
In this project, our primary objective is to delve into the classification and categorization of the dynamic behavior exhibited by biological network motifs. To achieve this, we employ Boolean network modeling as our foundational framework, leveraging its capacity to simulate the intricate dynamics inherent in biological networks. Additionally, we integrate sophisticated machine learning methods into our approach, utilizing them as powerful classifiers to discern and categorize the various dynamic patterns observed within these motifs. By combining Boolean network modeling with advanced machine learning techniques, we aim to provide a comprehensive understanding of the dynamic behavior of biological network motifs, thereby contributing to the advancement of network science and systems biology.
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