Enhancing the throughput of design-build-test-learn cycle for the future perspective of synthetic biology in plants (review)

Plant synthetic biology is an emerging area that integrates engineering principles with biology to develop unique plant-based systems for a variety of applications, from biofuel production to crop improvements. This technology has the potential to transform agriculture, promote sustainable development, and address global concerns such as food security, climate change, and renewable energy. The Design-Build-Test-Learn (DBTL) cycle is essential in synthetic biology as it provides a systematic framework for planning, developing, testing, and refining synthetic biological systems. It enables researchers to iteratively tune the performance of biological circuits, making it a vital tool for creating complex biological systems with predictable and reliable behavior. However, it will confront constraints at each of its stages, including inefficient design processes, restricted supply of genetic parts, technical challenges in developing and managing biological systems, and difficulties in correctly monitoring system performance. To overcome bottlenecks in the DBTL cycle, various strategies can be employed, such as developing advanced computational tools for efficient design, expanding the genetic parts toolbox, improving the precision and scalability of genome editing techniques, and implementing high-throughput screening methods to accurately measure system performance. In this review article we will discuss the recent advances for improving the performance of DBTL cycle to overcome the bottlenecks.