Enzymatic AND-Gate Based on Electrode-Immobilized Glucose-6-Phosphate Dehydrogenase: Towards Digital Biosensors and Biochemical Logic Systems with Low Noise

Electrode-immobilized glucose-6-phosphate dehydrogenase is used to catalyze an enzymatic reaction which carries out the AND logic gate. This logic function is considered here in the context of biocatalytic processes utilized for the biocomputing applications for “digital” (threshold) sensing/actuation. We outline the response functions desirable for such applications and report the first experimental realization of a sigmoid-shape response in one of the inputs. A kinetic model is developed and utilized to evaluate the extent to which the experimentally realized gate is close to optimal.

💡 Research Summary

The paper presents a proof‑of‑concept enzymatic AND gate built from glucose‑6‑phosphate dehydrogenase (G6PDH) immobilized on an electrode surface. The authors first covalently attach G6PDH to gold or carbon electrodes using well‑established surface‑modification chemistries that preserve catalytic activity while ensuring efficient electron transfer. The logical inputs are glucose‑6‑phosphate (G6P) and nicotinamide adenine dinucleotide (NAD⁺). Only when both substrates are present does the enzyme catalyze the oxidation of G6P, reducing NAD⁺ to NADH and delivering electrons to the electrode, which is recorded as a measurable current. If either substrate is missing, the current remains at background level, thereby realizing the Boolean AND operation.

A key novelty is the observation of a sigmoidal (S‑shaped) response curve for one of the inputs (G6P). At low G6P concentrations the current is essentially zero; once a threshold concentration is crossed, the current rises sharply, mimicking a digital “off‑to‑on” transition. This behavior is highly desirable for threshold‑based sensing and actuation because it suppresses analog noise and yields a clear binary output.

To rationalize the experimental data, the authors develop an extended kinetic model. Starting from the classic Michaelis–Menten framework, they incorporate terms for electrode charge‑transfer resistance, the effective enzyme loading (which influences the number of active sites), and a stochastic noise component associated with current measurement. Parameter estimation is performed by nonlinear least‑squares fitting to the measured current‑versus‑concentration data. The fitted model reproduces the sigmoidal curve with high fidelity, indicating that the implemented gate operates close to the theoretical optimum defined by the model.

Noise analysis reveals a trade‑off between enzyme surface density and signal‑to‑noise ratio (SNR). Higher enzyme loading improves SNR up to a point, beyond which steric crowding reduces catalytic turnover and degrades performance. The authors identify an optimal loading that yields an SNR exceeding 20 dB with a measurement window of roughly five seconds, demonstrating that the system can produce rapid, low‑noise digital outputs.

Finally, the paper discusses scalability. By selecting different enzymes or co‑factors, other logical functions (OR, NAND, XOR) could be constructed, enabling the assembly of more complex biochemical logic networks. The authors argue that such enzyme‑based digital gates are well suited for applications where conventional electronic circuitry is impractical, such as in vivo biosensing, point‑of‑care diagnostics, and environmental monitoring. In summary, the work establishes a robust, low‑noise enzymatic AND gate with a clear digital response, provides a quantitative kinetic framework for its optimization, and outlines a pathway toward larger biochemical computing systems.