Microorganisms are key drivers of biogeochemical cycles in natural environments. Microbially mediated biogeochemical reactions are influenced by both biotic and abiotic factors, including microbe–microbe interactions, enzyme kinetics, and chemical traits. To address the impact of chemical substrates on biogeochemical reactions, this chapter provides guidance on substrate-explicit thermodynamic modeling (SXTM) (also known as lambda modeling). SXTM enables automatic formulation of stoichiometric and kinetic models of biogeochemistry from the chemical formulas of organic matter (OM). This approach is particularly useful for formulating biogeochemical reaction models from ultra-high-resolution mass spectrometry data that identify thousands of compounds with distinct molecular formulas. Regardless of the complexity of OM data, SXTM requires only two parameters, maximum growth rate (μmax) and harvest volume (Vh), thereby avoiding the issue of overparameterization caused when one attempts to include a large set of chemical compounds. While the original formulation has been demonstrated with a focus on aerobic respiration, recent work has extended its scope to various other forms of electron acceptors. Here, we provide a tutorial on formulating biogeochemical reaction models using SXTM, with river corridor OM data collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium as an example. The software packages for implementing SXTM are available in two languages, Python and R, and are referred to as LambdaPy and LambdaR, respectively. These tools will significantly facilitate modeling of complex OM pools in microbially driven biogeochemical cycling and will help improve our understanding of the interplay among microbes, enzymes, and OM when integrated with complementary approaches, including microbial- and enzyme-explicit modeling.