Harnessing synthetic biology for energy-efficient bioinspired electronics: applications for logarithmic data converters.

Neuronal networks have driven advances in artificial intelligence, while molecular networks can provide powerful frameworks for energy-efficient information processing. Inspired by biological principles, we present a computational framework for mapping synthetic gene circuits into bio-inspired electronic architectures. In particular, we developed logarithmic Analog-to-Digital Converter (ADC), operating in current mode with a logarithmic encoding scheme, compresses an 80 dB dynamic range into three bits while consuming less than 1 µW, occupying only 0.02 mm², and operating at 4 kHz. Our bio-inspired approach achieves linear scaling of power, unlike conventional linear ADCs where power consumption increases exponentially with bit resolution, significantly improving efficiency in resource-constrained settings. Through a computational trade-off analysis, we demonstrate that logarithmic encoding maximizes spatial resource efficiency among power consumption and computational accuracy. By leveraging synthetic gene circuits as a model for efficient computation, this study provides a platform for the convergence of synthetic biology and bio-inspired electronic design.