Belmonte Beitia, Juan. García Otero, José . Italia, Matteo. 

Computational medicine leverages in silico methodologies, grounded in biophysical principles, to model, understand, and control complex diseases across multiple spatial and temporal scales. This review provides a comprehensive overview of the conceptual foundations and of a structured workflow—a general modus operandi—that guide these investigations. We discuss key modeling approaches, ranging from systems biology and network medicine to mechanobiological models and interpretable machine learning, highlighting their convergence into a cohesive in silico continuum. Central to translating these tools is the virtual clinic paradigm, encompassing virtual patients, digital twins, and virtual cohorts, whose predictive utility relies on rigorous parameter identification, multimodal data integration, and systematic validation. We examine the challenges of developing models that are simultaneously informative, predictive, tractable, validated, and clinically relevant and highlight how in silico approaches can personalize medicine, optimize treatment strategies, de-risk clinical trials, reduce costs, and address ethical concerns. Finally, we critically discuss the advantages and limitations of this emerging field—from multiscale predictive power to data dependency and regulatory hurdles—and existing barriers and cutting-edge trends to tackle them, such as high-resolution data integration, combining biophysical mechanistic modeling with data-driven approaches, and closed-loop control for adaptive treatments. This review aims to offer both a structured framework for newcomers and a critical, forward-looking perspective on how in silico methodologies are accelerating translational research and improving patient outcomes.