Integrating

The traditional paradigm of linking single genes to individual phenotypes is being replaced by a systems-level framework to understand the complexity of the tumor microenvironment. In this context, in silico knockout has emerged as a powerful computational approach to predict system-wide responses to genetic or cellular perturbations. This review summarizes how multilayer regulatory information across the genome, transcriptome, proteome, and metabolome can be integrated into computational models for virtual perturbation analysis. We outline major multi-omics data sources, including bulk, single-cell, and spatial omics, and emphasize how these data are transformed into model-compatible inputs such as constraint-based matrices and latent embeddings. We then discuss the evolution of in silico knockout methodologies, from genome-scale metabolic models and flux balance analysis to advanced deep learning frameworks that enable the prediction of non-linear and unseen perturbations. The integration of spatial transcriptomics further extends these approaches to tissue-level modeling of cell-cell interactions. In tumor immunology, these methods facilitate the identification of immune regulatory genes, the analysis of immune evasion mechanisms, and the prioritization of therapeutic targets. Despite current challenges in multi-omics integration and biological complexity, in silico knockout provides a promising framework for advancing precision immunotherapy.