Giovanni Stracquadanio

APEX automates recombinant protein production in E. coli, enabling high-throughput and reproducible expression with minimal setup.

The paper demonstrates the design of synthetic genome modules for programmable silencing in yeast using dCreSIR, a system that controls gene expression through loxPsym sites.

The paper introduces a cost-effective high-throughput long-read sequencing protocol for validating DNA assembly fidelity in biofoundries using Oxford Nanopore technology.

This paper synthesizes the largest eukaryotic yeast chromosome, synIV, and investigates how its 3D structure affects gene regulation and transcription.

The paper uses CRISPR D-BUGS and chromosome swapping to identify and resolve genetic interactions in synthetic chromosomes, revealing unexpected links between transcription, inositol metabolism, and tRNA abundance.

The paper develops CUBSEQ, a meta-analysis pipeline to estimate codon usage bias in Escherichia coli using RNA-seq data from 6,763 samples, identifying 81 highly expressed genes with distinct codon usage patterns.

The paper synthesizes a shorter yeast chromosome and studies its fusion with other chromosomes, revealing new 3D structural interactions and Red1 binding patterns.

The paper develops a statistical mechanics model called SPI to analyze recombination patterns in synthetic yeast chromosomes generated by SCRaMbLE, finding a random walk scaling exponent between 0.45 and 0.6.

The paper debugs a synthetic yeast chromosome and uses laboratory evolution to identify genomic rearrangements that suppress growth defects caused by synthetic modifications.

The paper constructs a synthetic yeast chromosome XI and demonstrates how non-coding DNA modifications affect cell fitness, enabling on-demand extrachromosomal circular DNA formation.

The paper designs human Sphingosine-1-phosphate lyase variants using a temporal Dirichlet variational autoencoder to improve therapeutic properties for nephrotic syndrome type 14.

The paper introduces an algorithm for DNA molecule design and assembly that optimizes tradeoffs between design requirements and manufacturing constraints, showing scalability and near-optimal results.

This paper introduces SBM-GNN, a graph neural network model that identifies cancer driver genes and pathways from omic data, outperforming existing methods and revealing key cancer-related processes.

The paper introduces a statistical framework for analyzing geneset networks and implements it in an open-source tool called PyGNA.

The paper identifies heritable genetic variants in cancer genes that associate with both cancer risk and anthropometric traits in a large cohort.

This study uses a new method to analyze gene-level heritability in cancer, identifying genes that explain significant heritability and are involved in tumorigenesis.