Multiscale Predictive Cellular Modeling: Integrating Hypothesis Grammars, Digital Twins, and Multi-Omics for <em>In Silico</em> Oncology and Precision Theranostics

Predictive multiscale cellular modeling redefines precision medicine by integrating hypothesis grammars, digital twins, and integrative genomics to forecast tumor–immune dynamics, therapeutic resistance, and cellular plasticity. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE–PDE systems, enabling rapid in silico hypothesis testing. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions. Integrative genomics, leveraging algorithms like SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Applications include in silico oncology trials optimizing checkpoint blockade and combination therapies. Large language models enhance rule induction, while FAIR-compliant digital cell repositories ensure reproducibility. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity and Kennedy–O’Hagan calibration address non-identifiability. Federated learning mitigates privacy and bias concerns. This framework enables virtual clinical trials and adaptive theranostics, transforming biological understanding into actionable, equitable precision medicine.