Using machine learning to enhance and accelerate synthetic biology

Abstract

Engineering synthetic regulatory circuits with precise input–output behavior—a central goal in synthetic biology—remains encumbered by the inherent molecular complexity of cells. Non-linear, high-dimensional interactions between genetic parts and host cell machinery make it difficult to design circuits using first-principles biophysical models. We argue that adopting data-driven approaches that integrate modern machine learning (ML) tools and high-throughput experimental approaches into the synthetic biology design/build/test/learn process could dramatically accelerate the pace and scope of circuit design, yielding workflows that rapidly and systematically discern design principles and achieve quantitatively precise behavior. Current applications of ML to circuit design are occurring at three distinct scales: 1) learning relationships between part sequence and function; 2) determining how part composition determines circuit behavior; 3) understanding how function varies with genomic/host-cell context. This work points toward a future where ML-driven genetic design is used to program robust solutions to complex problems across diverse biotechnology domains.