Omid Oftadeh

The paper introduces SIGMA, a framework that reconstructs receptor-to-transcription factor signaling by integrating prior knowledge and omics data to uncover metabolic reprogramming mechanisms.

This paper introduces SubNetX, a computational algorithm that extracts and assembles balanced reaction subnetworks for producing target biochemicals, and demonstrates its application to eight secondary metabolites and one non-natural chemical.

This paper introduces TOptKnock, a thermodynamically constrained version of OptKnock, to find optimal gene knockouts that couple growth and byproduct production while considering metabolite concentrations.

The paper introduces ReMIND, a framework to reconstruct microbial community interaction networks based on metabolic capabilities, applied to uranium-reducing communities and honeybee gut microbiomes.

This paper introduces rETFL, a computational method to predict metabolic and expression responses in recombinant organisms, showing its ability to model growth reduction, trade-offs, overflow metabolism, and pathway impacts.

This paper develops yETFL, an extended ME-model for Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics, demonstrating its predictive ability for growth rate, essential genes, and overflow metabolism.

The paper introduces a novel spatio-temporal method for modeling biological systems and demonstrates its effectiveness in analyzing electrical stimulation effects on mesenchymal stem cells.