<p>Predictive multiscale cellular modeling is emerging as a consequential direction in precision medicine, converging hypothesis grammars, digital twins, and integrative genomics to interrogate tumor-immune dynamics, therapeutic resistance, and cellular plasticity. This perspective synthesizes recent progress across these domains and critically maps their translational potential alongside their current limitations. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE-PDE systems, enabling rapid in silico hypothesis testing while lowering the authoring barrier for domain scientists. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions, though prospective clinical validation of these capabilities remains at an early stage. Integrative genomics, leveraging algorithms such as SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Emerging applications include in silico oncology trials for optimizing checkpoint blockade and combination therapies. Large language models are being explored to enhance rule induction, while FAIR-compliant digital cell repositories aim to ensure reproducibility and reuse. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity analysis and Kennedy-O’Hagan calibration are identified as essential components for addressing non-identifiability and supporting regulatory credibility. Federated learning is discussed as a means of mitigating privacy and bias concerns in multi-institutional settings. Together, these converging approaches outline a plausible pathway toward virtual clinical trials and adaptive theranostics, contingent on the prospective validation, data infrastructure, and governance frameworks that clinical deployment will require.</p> Graphical Abstract <p></p>