Quantifying Turnover in Resource Economy of Synthetic Biology Applications via Stochastic Simulations

We present a quantitative framework for analyzing resource turnover in synthetic biology applications using stochastic simulation of chemical reaction networks (CRNs). Building on a conservative extension of Gillespie's stochastic simulation algorithm, our method tracks causal dependencies between reaction events to generate flux graphs that represent resource flow dynamics. We illustrate how these graphs complement conventional trajectory analyses by enabling comparative assessment of cellular resource expenditure across different model parameter regimes. As a case study, we examine the E. coli two-component system responsible for inorganic phosphate uptake and demonstrate how synthetic promoter variants affect ATP-related resource economy. This framework supports predictive, model-based design for optimizing energy efficiency in living technologies.