Iván Domenzain

Model-Driven Elucidation of Lactose and Galactose Metabolism via Oxidoreductive Pathway in Sungouiella intermedia for Cell Factory Applications

This paper investigates lactose and galactose metabolism in Sungouiella intermedia using a genome-scale metabolic model and experimental data to identify its potential as a cell factory for producing galactitol and tagatose from industrial side-streams.

Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast

The paper presents a computational pipeline to predict gene engineering targets for increased production of 102 chemicals in yeast using enzyme-constrained models.

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

The paper upgrades the GECKO method to create enzyme-constrained models for yeast and human, revealing common metabolic adaptations under stress.

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The paper develops a hybrid modeling framework integrating Boolean and enzyme-constrained networks to study the interplay between nutrient signaling and metabolism in yeast, revealing new insights into their regulatory effects.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

This study benchmarks the accuracy and precision of intensity-based protein quantification methods in yeast using mass spectrometry.

Stress-Induced Expression is Enriched for Evolutionarily Young Genes in Diverse Budding Yeasts

The paper identifies stress-induced genes in budding yeasts and finds they are enriched for young, non-essential genes that evolve rapidly.

RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN 2.0 is a toolbox for metabolic network reconstruction, demonstrating its ability to generate accurate GEMs for Streptomyces coelicolor and improve existing models.