Vassily Hatzimanikatis A

The paper develops genome-scale metabolic models for a synthetic soil microbial community to understand metabolic interactions and community function.

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

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 study shows that pyocyanin disrupts airway epithelial regeneration, alters tissue architecture, and promotes chronic Pseudomonas aeruginosa infection.

This paper presents a framework for adapting generative neural networks to build kinetic models for different organisms and physiological contexts by exploring latent space.

This paper introduces NIS, a method to simplify genome-scale metabolic models for comparing metabolic capabilities across organisms, revealing conserved and divergent patterns in metabolism.

This study introduces iMSEA, a computational framework that identifies metabolic state shifts in BRCA1-deficient cancers using omics data and validates findings with experimental assays.

This study builds kinetic models of ovarian cancer metabolism to identify metabolic vulnerabilities and drug targets in BRCA1-deficient cells.

The paper uses kinetic models to guide metabolic engineering of S. cerevisiae strains, resulting in improved p-coumaric acid production with minimal impact on growth rate.

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 study investigates how Pseudomonas aeruginosa adapts to the airway environment by balancing biofilm formation for colonization and antibiotic tolerance, revealing trade-offs that contribute to chronic infections.

This paper introduces RENAISSANCE, a generative machine learning framework that accurately characterizes intracellular metabolic states by parameterizing kinetic models using omics data and experimental observations.

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 a framework for rational strain design in E. coli that minimizes phenotype perturbation using mixed-integer linear programming and kinetic models to improve anthranilate production.

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.

BridgIT+ predicts protein-reaction associations by leveraging enzyme promiscuity and sequence profiles, outperforming existing methods on E. coli and predicted reactions.

This study shows that a variant of IL-2 (IL-2v) induces a stem-like, metabolically active T-cell state with improved therapeutic potential in adoptive cell therapy.

Standard-GEM provides a template for reproducible genome-scale metabolic model development following FAIR principles and software best practices.

The paper introduces SKiMpy, a Python package for efficiently building and analyzing large-scale kinetic models in biological systems.

NICEgame identifies and resolves metabolic gaps in genome-scale models by suggesting alternative reactions and genes, improving gene essentiality predictions in E. coli.

This paper introduces REKINDLE, a deep learning framework that generates kinetic models of metabolic networks with dynamic properties, demonstrating its efficiency and versatility through three studies.

The paper develops a framework to analyze optimal enzyme operation modes under varying conditions, showing that enzyme efficiency depends on reactant concentrations and mechanism type.

ARBRE predicts biosynthetic pathways to industrially important aromatic compounds using a comprehensive reaction network and computational tools.

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 method to find metabolic pathways in large networks by using atom-conserving substrate-product pairs and graph search algorithms.

This paper introduces a constraint-based framework, Network Response Analysis (NRA), to improve metabolic engineering by incorporating physiological constraints and genome editing limitations.

The paper systematically reduces a genome-scale metabolic model of E. coli to study how model complexity affects metabolic sensitivity coefficients and shows consistent metabolic engineering conclusions across different model sizes.

The paper develops a computational model to simulate diauxie in E. coli, showing that proteome allocation and regulatory networks optimize growth under resource constraints.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.