CFM is a coarse-grained computational model that resolves essential metabolic processes and molecules within microbial cells. The model’s application includes a mechanistic analysis of microbial data, prediction, hypothesis development, and scaling up to regional or global environmental simulations. We apply CFM to a variety of microorganisms, but our key focus has been phytoplankton, which brings CO2 to various organic molecules. The model is highly empirical, backed by data from various scales, allowing realistic simulations. For example, the model reproduces the culture data, as well as global and regional patterns of elemental and macromolecular compositions of marine organic matter, underpinning macromolecular-level phenomena as a key driver in marine biogeochemical cycling. In this presentation, we will introduce a recent study on the application of CFM in climate change simulations, demonstrating a global shift in macromolecular compositions in phytoplankton across the ocean.