Federica Eduati

DIALECT: DNA-Programmable Synthetic Cells Communicate with Mammalian Cells through Synthetic Receptors

DIALECT enables synthetic cells to communicate with mammalian cells via DNA-programmed ligand release and engineered receptors, achieving specific signaling and logic gate functions.

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

The paper introduces FICUS, a framework that integrates omics data with logic models to identify shared signaling mechanisms across patient subgroups.

Informing agent-based models with spatial data using convolutional autoencoders

This paper presents a framework to optimize agent-based models using spatial data by comparing imaging data and model outputs in a shared latent space, showing improved reproduction of spatial features.

Active learning-guided mechanistic modeling of CXCL9 regulation in pancreatic cancer

This study uses active learning and mechanistic models to identify regulators of CXCL9 in pancreatic cancer cells and prioritize drug combinations.

Mathematically mapping the network of cells in the tumor microenvironment

The paper introduces a mathematical model, RaCInG, to infer cell-cell interaction networks in the tumor microenvironment using bulk RNA-seq data, revealing patient-specific features and immune response associations.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

This paper develops a signature, iHet, to capture tumor microenvironment heterogeneity linked to immune activity and evasion using transcriptomic data from lung cancer.

Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties

This study uses an agent-based model to simulate prostate cancer growth and ADT effects, revealing CRPC development and immunomodulatory interactions in the tumor microenvironment.

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

This study develops an agent-based model of prostate cancer progression, showing that stochastic interactions and systematic factors influence tumor development and clinical outcomes.

easier: interpretable predictions of antitumor immune response from bulk RNA-seq data

The paper introduces easier, an R/Bioconductor package that predicts antitumor immune response from bulk RNA-seq data, outperforming existing methods in four cancers.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

The paper develops patient-specific signaling pathway models using microfluidic screenings and logic-based modeling to predict personalized cancer therapies.