For life to arise from non-life, a metabolism must emerge and maintain itself, distinct from its environment. One line of research seeking to understand this emergence has focused on models of autocatalytic reaction networks (ARNs) and the conditions that allow them to approximate metabolic behavior. These models have identified reaction parameters from which a proto-metabolism might emerge given an adequate matter-energy flow through the system. This dissertation extends that research by answering the question: can dynamically structured interactions with the environment promote the emergence of ARNs? This question was inspired by theories that place the origin of life in contexts such as diurnal or tidal cycles. To answer it, an artificial chemistry system with ARN potential was implemented in the dissipative particle dynamics (DPD) modeling paradigm. Unlike differential equation (DE) models favored in prior ARN research, the DPD model is able to simulate environmental dynamics interacting with discrete particles, spatial heterogeneity, and rare events. This dissertation first presents a comparison of the DPD model to published DE results, showing qualitative similarity with some interesting differences. Multiple examples are then provided of dynamically changing flows from the environment that promote emergent ARNs more than constant flows. These include specific cycles of energy and mass flux that consistently increase metrics for ARN concentration and mass focusing. The results also demonstrate interesting nonlinear interactions between the system and cycle amplitude and period. These findings demonstrate the relevance that environmental dynamics has to ARN research and the potential for broader application as well.
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