bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/225318

Modeling Dynamic Transcriptional Circuits with CRISPRi

Clamons, S. E.·Murray, R. M.
View original source ↗Export metadata ↓

Abstract

Targeted transcriptional repression with catalytically inactive Cas9 (CRISPRi) promises to reproduce the functions of traditional synthetic transcriptional circuits, but with better orthogonality, programmability, and extensibility. However, CRISPRi lacks obvious cooperativity-a feature classically considered critical for several classic gene regulatory circuits. We use a simple dynamical model of CRISPRi to show that it can be used to build repressilators, toggle switches, and incoherent feed-forward loops. We also show that the function some of these circuits are expected to be sensitive to several key parameters, and we provide specifications for those parameters. Our modeling reveals key engineering requirements and considerations for the construction of dynamic CRISPRi circuits, and provides a roadmap for building those circuits.

Explore related subjects

synthetic biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Clamons, S. E., Murray, R. M.. 2017-11-27. Modeling Dynamic Transcriptional Circuits with CRISPRi. https://doi.org/10.1101/225318

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Biosensor-guided genome-wide CRISPR screens enable cis,cis-muconic acid overproduction in yeast

Classical pathway metabolic engineering relies on enhancing strain productivity through the introduction of rationally identified genetic perturbations, which while useful, are time-consuming and miss unintuitive targets. Genome-wide CRISPR screens offer an alternative by efficiently sampling genomes, enabling the engineering of novel phenotypes, and are further enhanced when combined with high-throughput strain interrogation methods. Here, using Saccharomyces cerevisiae, we engineered small molecule biosensors into population enrichment tools using zeocin resistance and fluorescence as signal outputs. We then introduced the multi-functional genome-wide CRISPR (MAGIC) library that targets every gene for activation, interference, or deletion, thus creating biosensor-aided MAGIC, or senMAGIC. As a case study, we utilized senMAGIC to identify single genetic determinants that improve productivity of the nylon precursors protocatechuic and cis,cis-muconic acids (PCA and ccM). These were then randomly assembled as triple gRNA-tRNA arrays for multiplexed gene perturbations to identify combinatorial effects. Top gRNA hits were then confirmed using direct genomic integrations in senMAGIC-free production strains, resulting in identifying known as well as novel targets that increase PCA and ccM titres. Our results demonstrate senMAGIC as a robust automation-friendly whole-genome evolution tool and highlight limitations of the method, providing a roadmap for future multifunctional CRISPR screens to optimize host metabolism for efficient bioprocesses.

synthetic biology↗
bioRxiv2026

Synthetic reaction-diffusion patterning in mammalian cells

During development, periodic patterns arise through reaction-diffusion (RD) circuits involving local activation and long-range inhibition (LALI). Natural systems are complex, typically entangling regulation and transport in the same morphogens. It remains unclear what minimal circuits are sufficient for LALI patterning in mammalian cells, and whether they can be implemented in a modular fashion, allowing independent control of regulation and transport. Here, we designed synthetic LALI circuits using three natural and synthetic signaling systems, BMP, Activin, and the SNIPR pathway, and showed that they generate spatial patterns with distinct characteristic spatial scales. Model-guided design allowed flexible morphogen reassignment, with BMP4 acting as either short-range activator or long-range inhibitor. Gene expression noise degraded long-range order but preserved the dominant local spatial scale. Together, these results provide a modular, extensible synthetic framework for engineered patterning in mammalian cells.

synthetic biology↗
bioRxiv2026

Machine learning guided design of strong transcriptional activation domains

Artificial intelligence has accelerated the design of structured proteins, but it remains difficult to design functional intrinsically disordered proteins because of weak structural constraints and poorly defined sequence-to-function relationships1. Activation domains (ADs), transcription factor regions that recruit coactivators, are an excellent test case for the de novo design of intrinsically disordered sequences. ADs are one of the oldest groups of functional intrinsically disordered sequences2 and designed ADs have applications in synthetic biology, biotechnology, and agriculture. We computationally generated 500,000 candidate sequences that resembled known ADs and predicted AD function with extant machine learning models, including three neural networks. From high-scoring sequences, we selected 18,750 for experimental validation in yeast. Surprisingly, >90% of the 16,770 well-measured sequences function as ADs. Sequences predicted to be active by multiple models were stronger, motivating development of a fast and accurate consensus AD predictor, MADRIGAL. A number of our designed ADs retain functionality across fungi, plants, and human cells. This work illustrates a generalizable paradigm for incorporating sequence-to-function models into the design loop to efficiently generate functional intrinsically disordered sequences.

synthetic biology↗