The invention of the microscope transformed biology by revealing structures and processes that were previously beyond the reach of observation. Mathematical modeling offers a different but equally powerful way of seeing: it allows us to probe mechanisms, test hypotheses, and explore scenarios that cannot be directly examined experimentally. This talk will discuss how mathematics can serve as a tool for discovery in the life sciences. By combining physical principles, biological knowledge, and quantitative data, mechanistic models provide a framework for connecting processes that span multiple spatial and temporal scales, from molecular interactions to whole-organ and organism-level function. Like a microscope with adjustable magnification, mathematical models allow us to focus on different levels of organization while maintaining a coherent view of the underlying system. Using examples from computational physiology, I will illustrate how mathematical models can be used to uncover emergent behavior, quantify the contributions of interacting mechanisms, identify critical knowledge gaps, and generate experimentally testable predictions. These examples highlight the increasingly important role of mathematics not merely as a language for describing biological systems, but as an instrument for investigating them. In this sense, mathematical models can reveal aspects of physiology that would otherwise remain hidden, much as the microscope once revealed an unseen biological world.