Modern cosmology is entering a new era of precision thanks in part to the next generation of imaging and spectroscopic surveys. These surveys will provide an unprecedented volume of high quality observations. As a result, cosmological analyses will no longer be limited by statistical uncertainty, but instead be limited by the ability to characterize and control systematic errors. Cosmological simulations and the models derived from these simulations play an irreplaceable role in our ability to test, validate and control systematics in analyses. This thesis presents a novel method of modeling galaxies within halos, and the construction of a realistic synthetic galaxy catalog. We first describe a novel method of tracking substructure within dark matter halos to be used in conjunction with galaxy models. Many galaxy models, including empirical and semi-analytical (SAM), rely on subhalos and subhalo merger trees for determining galaxy positions and evolutions within simulated dark matter halos. Subhalos and subhalo merger trees are computationally expensive and difficult to robustly define. Furthermore, the abundance of subhalos does not match the abundance of galaxies within halos. To fix this mismatch, some galaxy models introduce galaxies, called 'orphan' galaxies, that are not hosted in a subhalo. In our approach, instead of relying on subhalos and 'orphan' galaxies, we introduce a new method, called 'halo core tracking', to track substructure and its evolution within halos. To test core tracking utility and viability in galaxy modeling, we model the distribution of galaxies within galaxy clusters. In the second part, we discuss the methods used to construct a highly realistic, and yet tunable, synthetic galaxy catalog, named cosmoDC2. Large galaxy catalogs generally use two broad types of models: empirical models and SAMs. CosmoDC2 is built by hybridizing an empirical model with a SAM, and so taking the advantages of each and minimizing the disadvantages. The backbone of cosmoDC2 is an empirical model that is able to precisely capture the distribution of key galaxy properties, such as luminosity and galaxy color. By matching each 'backbone' galaxy to an individual galaxy in the SAM catalog, we are able to include detailed and consistent galaxy properties that the empirical model cannot provide.