Biomolecular signaling networks enable cells to decode signals from their environment into the appropriate responses needed for survival and maintaining biological function. Using principles derived from these systems, synthetic approaches such as optogenetics were developed to grant users with precise control over cellular activity. Nonetheless, it remains challenging to engineer synthetic signaling systems towards predefined dynamic behaviors. In this work, we present a quantitative framework that integrates multi-objective optimization, dynamic modeling, and biosensor imaging to streamline the design and implementation of synthetic signaling networks in cells. First, we formulated a general signaling model and applied an evolutionary algorithm to derive a network design capable of decoding two distinct frequency modulated stimuli into separate response channels. Based on this design, we implemented an optogenetic circuit to control protein interactions in cells according to the blue light input pattern. To aid this process, dimerization-dependent fluorescent proteins were used to characterize and select optogenetic components with the required dynamic characteristics. Lastly, we applied our frequency decoder system to regulate downstream biological outputs such as gene transcription, antigen presentation, and antigen triggered CAR-T killing in mice. This framework provides an integrative approach for developing synthetic signaling systems that reduces reliance on trial-and-error.
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