Network-based integrated tool for repurposing optimization (NiTRO): An algorithm for combinatorial predictions and rescue of viral-induced metabolic states
收藏资源简介：
This dataset contains the complete implementation of NiTRO (Network-based integrated tool for repurposing optimization), a computational workflow for predicting metabolic state changes in cells infected with pathogenic coronaviruses (SARS-CoV-1, SARS-CoV-2, and MERS-CoV). Repository Contents:- Tissue-specific constraint-based metabolic models for infected and non-infected HEK293T cells- MATLAB scripts implementing the NiTRO algorithm for drug repurposing- Pre-computed metabolic state predictions for different viruses at 24h and 48h post-infection- Supplementary data tables with transcriptomics data and identified metabolic perturbations- Complete documentation and figures from the manuscript Key Features:NiTRO uses flux balance analysis (FBA) to detect viral-induced metabolic changes and performs in-silico double-gene deletions to identify genetic perturbations that can revert infected cell models to healthy-state values. This approach provides a systems-level characterization of metabolic vulnerabilities in infected cells and can identify specific genetic targets and synergistic effects of drugs for repurposing. Methodology:The workflow integrates RNA-seq expression data with the human genome-scale metabolic model Recon3D using the GIMME algorithm to generate tissue-specific constraint-based models. Six infected models (SARS-CoV-1, SARS-CoV-2, and MERS-CoV at 24h and 48h) and two non-infected control models are provided. Applications:This computational framework is applicable to any pathogen and provides actionable insights for rapid drug repurposing in response to emerging viral threats. Corresponding Authors:Jean-Claude Twizere (jt4711@nyu.edu) and Kourosh Salehi-Ashtiani (ksa3@nyu.edu)
