Molecular communication (MC) networks between nanoscale biological entities require biologically plausible implementations of signal processing algorithms. Chemical reaction networks (CRNs) provide a natural framework to model such computing processes and capture stochastic effects in simulations. In this work, we present ChemSICal-Net, a stochastic CRN receiver model that implements successive interference cancellation (SIC) for molecular multiple access. The receiver maps SIC to modular chemical building blocks and uses a chemical oscillator for timing control. We further propose an adaptive Bayesian optimization (BO) scheme for tuning reaction rate constants and initial molecule counts, and compare it with baseline optimizers under a fair simulation-cost metric. Then, we evaluate ChemSICal-Net stochastically across clock speeds and configurations using communication metrics such as detection accuracy and decision time. The results show that chemical timing reduces error probability by up to a factor of 2 for short decision times, revealing a direct decision-time/error trade-off. BO reduces the error probability by approximately one order of magnitude compared with non-optimized parameters. Overall, the results support a multi-scale design methodology that combines external optimization with stochastic reaction-level simulation for communication-oriented, timing-controlled CRN receivers in self-organizing living systems.
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Molecular communication (MC) networks between nanoscale biological entities require biologically plausible implementations of signal processing algorithms. Chemical reaction networks (CRNs) provide a natural framework to model such computing processes and capture stochastic effects in simulations. In this work, we present ChemSICal-Net, a stochastic CRN receiver model that implements successive interference cancellation (SIC) for molecular multiple access. The receiver maps SIC to modular chemic...
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