In this study, we explore how the combination of synthetic biology, neuroscience modeling, and neuromorphic electronic systems offers a new approach to creating an artificial system that mimics the natural sense of smell. We argue that a co-design approach offers significant advantages in replicating the complex dynamics of odor sensing and processing. We propose a hybrid system of synthetic sensory neurons that provides three key features: (a) receptor-gated ion channels, (b) interface between synthetic biology and semiconductors and (c) event-based encoding and computing based on spiking networks. Our approach is validated using simulation-based modeling of the complete sensing and processing pipeline. This research seeks to develop a platform for ultra-sensitive, specific, and energy-efficient odor detection, with potential implications for environmental monitoring, medical diagnostics, and security.