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Network topology and logic drives cell fate transitions in carcinomas

Recent trends in modeling and simulation of cancer have increasingly integrated Artificial Intelligence (AI) and Machine Learning (ML), Systems Biology, and Partial Differential Equation (PDE)-based models to better understand tumor dynamics and optimize treatment strategies. <br>AI/ML methods are transforming cancer research by analyzing large-scale datasets, such as genomic data, medical imaging, and patient histories. These techniques can predict cancer progression, identify biomarkers, and recommend personalized treatments. Machine learning models, like neural networks and decision trees, can assist in early diagnosis by detecting patterns in medical images and genomic sequences that may be difficult for humans to discern. <br>Systems biology approaches integrate molecular, cellular, and environmental data to simulate complex interactions within tumor ecosystems. This holistic perspective allows for modeling the multi-scale nature of cancer, including gene regulation, signaling pathways, and microenvironmental interactions. Systems biology helps uncover how tumors evolve, metastasize, and develop resistance to treatments, guiding the development of targeted therapies.<br>PDE-based models are crucial for simulating the spatiotemporal evolution of tumors. These models describe how factors like nutrient supply, drug diffusion, and cell proliferation spread through tissue. Coupled with computational methods, PDE models allow for the simulation of tumor growth, angiogenesis, and response to therapy under various conditions. By incorporating feedback loops and spatial heterogeneity, these models offer deeper insights into tumor behavior and help predict treatment outcomes.<br>In combination, AI/ML, systems biology, and PDE-based models are revolutionizing cancer research, enabling more precise, data-driven approaches to diagnosis, treatment planning, and understanding cancer progression.

Recent Trends in Modeling & Simulation of Cancer

Joint Meeting of Asian Conference for Mathematical Biology and Annual Meeting of Japanese Society for Mathematical Biology (ACMB-JSMB2025)

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[SS24-03]Network topology and logic drives cell fate transitions in carcinomas

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