Deep Learning for Advancing Synthetic Biology in Plants

Plant synthetic biology (SynBio) represents a rapidly evolving field focused on optimizing crop traits and bioproduction by applying engineering methodologies, particularly through the design and implementation of genetic circuits for precise modifications. This emerging platform applies cyclic design principles to engineer novel biological components, devices, and systems, aiming to enhance yield, nutritional content, nutrient uptake, and stress resilience. Nowadays, deep learning has demonstrated significant strengths in learning and design, offering new approaches to study the mechanisms underlying complex biological phenomena in plants. This chapter investigates the integration of deep learning methodologies with emerging advancements in SynBio in plants, focusing on approaches such as genome editing and design, gene regulatory networks, phenotype prediction, protein structure prediction, and metabolic pathway engineering. This chapter also delineates key challenges that pose significant barriers to the continued advancement of deep learning and synthetic biology.