Software and Datasets



 INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) algorithm predicts how antibiotics prescribed in combinations will inhibit bacterial growth. INDIGO leverages genomics and drug-interaction data in the model organism – E. coli, to facilitate the discovery of effective combination therapies in less-studied pathogens, such as M. tuberculosis. [Download INDIGO algorithm]  (Ref: Chandrasekaran et al. Molecular Systems Biology 2016)


MAGENTA (Metabolism And GENomics-based Tailoring of Antibiotic regimens) predicts the impact of pathogen environment on antibiotic potency. MAGENTA uses chemogenomic data to predict synergistic or antagonistic drug-interactions in different metabolic environments. [Download MAGENTA software and datasets]   (Ref: Cokol, Li and Chandrasekaran, Plos Computational Biology 2018)


GEMINI (Gene Expression and Metabolism Integrated for Network Inference) directly connects regulatory interactions to observable phenotypes and allows rapid assessment of inferred regulatory interactions using a metabolic network [GEMINI algorithm implementation and Data]  (Ref: Chandrasekaran and Price, Plos Computational Biology 2013) [GEMINI tutorial data]



ASTRIX (Analyzing Subsets of Transcriptional Regulators Influencing eXpression) uses gene expression data to identify regulatory interactions between transcription factors and their target genes. Ref: Chandrasekaran et al, PNAS, 2011. [Download ASTRIX algorithm and example data]

Dynamic flux activity (dfa)

The DFA approach uses genome-scale metabolic network models to infer the activity of metabolic reactions from time-course metabolomics data. This approach was shown to accurately predict metabolic differences between different embryonic- and induced- pluripotent stem cells. Ref: Chandrasekaran et al, Cell Reports, 2017. [Download DFA source code; Tutorial data]


PROM (Probabilistic Regulation of Metabolism) enables the quantitative integration of regulatory and metabolic networks to build genome-scale integrated metabolic–regulatory models [Download PROM models for E. coli and Mycobacterium tuberculosis; latest version of the PROM algorithm]   (Ref: Chandrasekaran and Price, PNAS 2010)


The epigenome-scale metabolic network model (EGEM) predicts the impact of metabolic alterations on histone acetylation, a central epigenome modification that impacts gene expression. Data from Shen et al, Genome Biology, 2019. [Download Metabolism-Epigenome model]