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A survey of 3,000 global executives suggests that companies that are successful in growing amid uncertainty are not necessarily more strategic in their thinking, but rather have a strong inclination to move quickly. These companies embrace disruption, are unafraid of big change, and prioritize learning and data-driven decision-making. They are action-oriented and prioritize pace over perfection. In uncertain environments, it is important for leaders to act, learn, and adapt quickly, rather than seeking a false sense of certainty. [Extracted from the article]

Cis-regulatory elements (CREs), such as promoters and enhancers, are DNA sequences that regulate the expression of genes. The activity of a CRE is influenced by the order, composition, and spacing of sequence motifs that are bound by proteins called transcription factors (TFs). Synthetic CREs with specific properties are needed for biomanufacturing as well as for many therapeutic applications including cell and gene therapy. Here, we present regLM, a framework to design synthetic CREs with desired properties, such as high, low, or cell type–specific activity, using autoregressive language models in conjunction with supervised sequence-to-function models. We used our framework to design synthetic yeast promoters and cell type–specific human enhancers. We demonstrate that the synthetic CREs generated by our approach are not only predicted to have the desired functionality but also contain biological features similar to experimentally validated CREs. regLM thus facilitates the design of realistic regulatory DNA elements while providing insights into the cis-regulatory code.