Core Viewpoint - The article discusses a groundbreaking research published in the journal Cell, which introduces a novel framework called Cell Behavior Hypothesis Grammar. This framework allows researchers to use natural language to model complex multicellular systems, facilitating virtual cell laboratories for accelerated research in cancer and neuroscience [3][4][19]. Group 1: Research Framework - The Cell Behavior Hypothesis Grammar enables the construction of mathematical models using natural language descriptions of cellular rules, integrating biological knowledge and multi-omics data to create virtual cell models for thought experiments [4][10]. - This framework allows researchers to predict multicellular emergent behaviors and test new hypotheses, showcasing its potential in understanding cancer metastasis and drug resistance [4][19]. Group 2: Applications in Cancer Research - In cancer research, simulations revealed that cancer-associated fibroblasts (CAFs) can induce cancer cell shape changes, accelerating metastasis, while high-density CAFs can create physical barriers to cancer cell escape [13]. - During immune therapy simulations, it was found that macrophages could "betray" by secreting EGF signals that enhance breast cancer cell migration, leading to a 75% reduction in metastasis when EGF receptors were blocked [14]. Group 3: Applications in Neuroscience - The research team successfully simulated brain development, demonstrating how stem cells undergo asymmetric division to form different cortical layers, accurately replicating the differences between sensory and auditory cortices using data from the Allen Brain Atlas [16]. Group 4: Virtual Clinical Trials - The model initialized with pancreatic cancer patient data tested various immunotherapy combinations, revealing that tumors with high macrophage content responded best to a triplet therapy, paving the way for personalized treatment approaches [18]. Group 5: Future Implications - The open-source nature of the Cell Behavior Hypothesis Grammar allows researchers to quickly test drug regimens, potentially reducing drug development timelines from ten years to a single night in front of a computer screen [21].