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    This study introduces a novel cellular automaton model for the Hes1 gene network, demonstrating efficient hardware implementation. The model accurately reproduces biological phenomena while significantly reducing power consumption and circuit elements.

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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Biophysics

    Background:

    • The Hes1 gene network is crucial for biological oscillations and cell fate decisions.
    • Conventional models often use delay differential equations, which can be computationally intensive.
    • Efficient simulation of gene networks is vital for understanding complex biological processes.

    Purpose of the Study:

    • To develop a novel ergodic cellular automaton model for the Hes1 mRNA and Hes1 protein network.
    • To compare the efficiency and performance of the cellular automaton model against delay differential equation models.
    • To validate the model through hardware implementation and experimental testing.

    Main Methods:

    • Development of an ergodic cellular automaton model for the Hes1 gene network.
    • Analysis of nonlinear bifurcation phenomena using the cellular automaton model.
    • Implementation of the model on a field-programmable gate array (FPGA) for hardware validation.

    Main Results:

    • The cellular automaton model successfully reproduces nonlinear bifurcation phenomena characteristic of the Hes1 network.
    • The FPGA implementation demonstrates the model's operational feasibility and accuracy.
    • The proposed model exhibits significantly lower power consumption and requires fewer circuit elements compared to delay differential equation models.

    Conclusions:

    • The ergodic cellular automaton model offers an efficient and hardware-friendly approach to simulating the Hes1 gene network.
    • This work provides foundational knowledge for designing efficient hardware-based gene network simulators.
    • The findings pave the way for advanced computational tools in systems biology research.