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    Area of Science:

    • Biotechnology
    • Synthetic Biology
    • Molecular Communication

    Background:

    • Molecular communication (MC) leverages molecules for information transmission between nanomachines.
    • Synthetic biology enables the creation of biological components for MC systems using genetic circuits.
    • Genetic circuits are evolving from basic modules to complex networks for novel MC functions.

    Purpose of the Study:

    • Investigate the design of genetic circuits for implementing convolutional coding in MC.
    • Explore the use of concentration shift keying (CSK) for information transmission in a diffusion-based MC channel.
    • Develop a biochemical realization of communication functions, including a majority-logic decoder.

    Main Methods:

    • Design and simulation of genetic circuits for convolutional encoding and CSK modulation.
    • Implementation of a majority-logic decoder using gene activation and inhibition.
    • Biochemical simulations to validate system feasibility and analyze noise effects.

    Main Results:

    • Demonstrated the feasibility of using genetic circuits for convolutional coding in MC.
    • Successfully designed a biochemical majority-logic decoder for received symbols.
    • Analyzed the impact of diffusion and chemical reaction noise on system performance.

    Conclusions:

    • Genetic circuits offer a viable biochemical alternative to electronic circuits for MC systems.
    • The proposed design enables complex communication functions like convolutional coding within biological systems.
    • Further research can optimize these systems by mitigating biochemical noise.