Domitilla Del Vecchio

This paper designs single-cargo synthetic gene circuits that generate two balanced cellular states without relying on copy-number variation.

This study develops a mathematical model to predict synthetic gene circuit function during bacterial stationary phase by measuring transcription and translation dynamics over time.

This paper engineers a reversible genetic memory system in bacteria that maintains two states for up to two weeks while remaining rewritable.

The paper demonstrates that increasing activator expression can reduce target gene expression by displacing repressor from decoy sites, explaining paradoxical gene regulation through competition for genomic binding sites.

This study investigates how DNA methylation and H3K9me3 maintain transgene silencing and shows that targeted DNA demethylation can reverse it in mammalian cells.

This study reveals analog epigenetic memory through graded DNA methylation, showing its temporal stability and role in gene expression regulation.

This study shows that CRISPRa systems experience resource competition between scRNAs, leading to gene repression and non-modular behavior, and introduces predictive modeling tools for multi-module networks.

The paper identifies OCT4 trajectories critical for reprogramming human fibroblasts to iPSCs and develops a synthetic genetic circuit to enforce these trajectories, improving reprogramming efficiency.

This paper compares three reprogramming approaches by modeling the Oct4 gene regulatory network and finds that overexpressing eraser enzymes accelerates and stabilizes reprogramming, with TET outperforming JMJD2 under specific conditions.

This paper designs an incoherent merger network in E. coli to achieve a ratiometric gene expression response that is linear, tunable, and robust to cellular perturbations.

This paper introduces a feedforward controller that compensates for growth rate defects caused by heterologous gene activation in bacteria.

This paper classifies resource competition structures in synthetic gene circuits and shows how their response to competitors and interventions can distinguish between different competition mechanisms.

The paper models how chromatin modification circuits enable epigenetic memory through time-scale separation and bistability in gene expression states.

This paper develops synthetic phosphorylation-based signaling networks in mammalian cells that are tunable, precise, and robust to perturbations.

The paper introduces resource demand and sensitivity as characteristics to predict genetic circuit behavior under resource competition and validates them experimentally.

This paper introduces a dCas9 regulator that reduces competition among sgRNAs in CRISPRi circuits, enabling independent gene repression control.

This paper develops an endoribonuclease-based feedforward loop to improve modularity in genetic systems by reducing resource competition in mammalian cells.

This paper introduces a reduced ODE model that incorporates spatial effects through a binding correction factor (BCF) to better analyze and design genetic circuits in bacteria.

This study models a genetic population controller and finds that it cannot be tuned to be robust to both extracellular disturbances and resource pool perturbations.

This paper designs a quasi-integral feedback controller to maintain gene expression levels despite changes in ribosome availability, showing it prevents expression reduction when a resource competitor is activated.

The paper develops a model to analyze resource competition in CRISPR-mediated gene repression networks and shows how hidden interactions affect network function based on the number of repressors.

This paper proposes a design principle for integral controllers in genetic circuits that mitigates dilution-induced memory loss, demonstrating it with synthetic circuits for gene expression adaptation.

The paper investigates how resource competition affects genetic circuits in E. coli and provides a model to mitigate unintended behaviors by tuning resource demand coefficients.

This study shows that compositional context in synthetic gene networks significantly affects gene expression levels and key regulatory properties.

This paper develops a dynamical model of transcriptional circuitry to explain the low efficiency of induced pluripotent stem cell reprogramming.