Biological systems can be difficult to understand due to a vast array of interacting phenom-ena. The result is that some seemingly "easy" questions go unanswered. For example, we have long known that bacteria utilize many distinct flagellar configurations, but in most cases it remains unclear why they do so. We know that cell differentiation is critical to many biological processes, yet we still do not fully understand how such spatiotemporal patterning occurs. Despite mutation being one of the driving forces of evolution, we still have a hazy understanding of how organisms respond and adapt to high mutation rates. However, ad- vances in technology, modeling, and experimental techniques have enabled us to investigate the small and nuanced effects that can answer these questions. In this dissertation, I have used modern computational tools and statistical techniques to investigate evolutionary and behavioral processes. Agent-based models of evolution and flagellar inheritance have allowed me to investigate the evolution of mutational robustness and trade-offs associated with flagellar motility, respectively. By writing Bayesian mixed- effect models, I have been able to precisely quantify the metabolic cost of producing flagella and describe spatiotemporal patterns of cell differentiation within fruiting bodies of Myxo- coccus xanthus. Careful quantitative modeling of biological phenomena can help cut through the complexity of these systems. As computational power continues to grow and software continues to become more sophisticated, these computational approaches will both become more powerful and easier to use. Computational approaches will not replace experiments in most cases; instead, computational models can direct experiments, which can themselves direct new modeling efforts, in an iterative loop of progressing knowledge.
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