📄 Category

synthetic biology

55 papers

Synthetic biology is like engineering for life, where scientists design and build new biological parts or rewire existing ones to solve real-world problems. Instead of just studying how nature works, this field asks what we can create, from bacteria that produce biofuels to smart materials that heal themselves. It sits at the exciting intersection of biology, engineering, and computer science, turning the code of life into something we can read, edit, and program.

At Gist.Science, we bring you the very latest discoveries in this rapidly evolving space directly from bioRxiv. We process every new preprint in this category as soon as it appears, offering both plain-language explanations for the curious mind and detailed technical summaries for researchers. This ensures you never miss a breakthrough, regardless of your background or how deep you want to dive into the science.

Below are the newest preprints in synthetic biology, carefully curated and summarized just for you.

📄 synthetic biology

Continuous target-specific mutagenesis and rapid gene evolution by diversity-generating retroelements in Escherichia coli

This paper demonstrates that diversity-generating retroelements (DGRs) can be engineered in *Escherichia coli* and coupled with horizontal gene transfer to create a simple, programmable system for the continuous, iterative, and position-specific mutagenesis of target genes, enabling the rapid evolution of proteins such as pyrrolysyl-tRNA synthetase for biotechnological applications.

Liu, Y., Gu, Y., Agam, G., Rehm, F., Spinck, M., Chin, J., Holliger, P.2026-07-22✓ Author reviewed ⓘ
📄 synthetic biology

Pharmacologically regulated bioorthogonal stabilization domain for regulation of CAR T cells

This study presents a clinically compatible, reversible strategy for controlling CAR T cell function using human estrogen receptor-based degron domains that enable precise pharmacological ON-switch activation via 4-hydroxytamoxifen and OFF-switch degradation via the FDA-approved PROTAC ARV-471, thereby enhancing the safety and precision of cellular immunotherapies.

Rihtar, E., Fink, T., Belak, M., Udvanc, R., Jerala, R.2026-07-16
📄 synthetic biology

LUstiGE, Light responsive Ustilago maydis Gene Expression: Optogenetic control of morphogenesis and pathogenesis in the corn fungal pathogen Ustilago maydis

This study establishes blue-light-inducible optogenetic switches in the corn fungal pathogen *Ustilago maydis* to achieve precise, reversible, and dynamic control over gene expression, thereby enabling the manipulation of cell morphology, filamentous invasion, and tumor formation for advanced research into pathogen-host interactions and biotechnological applications.

Tang, K., Müller, M. D., Hüsemann, L., Zuo, W., Rybecky, A., Heucken, N., Postma, J., van Wijlick, L., Doehlemann, G. (…)2026-07-09
📄 synthetic biology

Barcoded-Plasmid DNA library construction for recording cell lineage trees enabled by a Scalable and modular Biofoundry-based Automated Robotic Pipeline

This paper presents a scalable, modular, and automated robotic pipeline using the CyBio FeliX platform and SPRI-based magnetic bead technology to efficiently purify high-quality plasmid DNA, successfully enabling the construction and validation of a 528-plasmid lentiviral library for mammalian cell lineage tracing.

Tassinari, E., Ives, L., Hawkins, E., Annese, D., Fonseca, S., Lan, Y., Haerty, W., Wojtowicz, E., Grandellis, C.2026-07-08
📄 synthetic biology

Reprogramming a Protein Ligase for Genetic Code Expansion

By grafting the anticodon-binding domain of lysyl-tRNA synthetase onto the protein ligase EpmA, researchers engineered a chimeric enzyme (chEpmA) that successfully reprograms a protein ligase into a versatile aminoacyl-tRNA synthetase capable of charging tRNAs with diverse non-canonical substrates, including {beta}-amino acids and various post-translational modifications, to expand the genetic code.

Gallo, G., Sieber, A., Hellwig, M., Fuerst, M. J. L. J., Lassak, J. M.2026-07-08
📄 synthetic biology

TaxoFormer: Hierarchical Transformer for Predicting the Full Taxonomic Lineage of Protein Sequences

The paper introduces TaxoFormer, a hierarchical Transformer architecture that utilizes a structured tokenization scheme to losslessly represent the massive NCBI phylogenetic tree, enabling a simple generative model to accurately predict full protein taxonomic lineages and learn meaningful phylogenetic representations from 188 million sequences.

Parsa, M., Azimian, K., Wei, K. Y.2026-06-09