On the hierarchical design of biochemical-based digital computations

The understanding of the biochemical processes underpinning various biological systems has significantly increased in recent decades and has even prompted reverse engineering of certain of life's more complex processes. The most prominent example is modern computers designed to mimic neuron activity. This work forms part of growing endeavors to return advances in the theory of computation and electronics to biology. In this context, we present a set of requirements sufficient for the design of biochemical analogs of modern electronics in a hierarchical, modular fashion that mimics the design of modern computational devices. This theoretical approach is based on a simple enzymatic analog of the transistor and supported by numerical simulations of biochemical models of enzymatic networks equivalent to complex, and modular, interconnecting electronic circuitry (including clocks, Flip-Flops, adders, decoders, and multiplexers). Furthermore, the proposed approach has been implemented in the form of a Python library capable of creating and testing models of complex bio-analog digital computations based on the execution of an elementary universal logic gate. In tribute to Claude Shannon, our biochemical network materializes his example of a "password" recognition that moves the language of the modern theory of automata beyond combinatorial logic and towards sequential logic.