Jürgen Zanghellini

Hybrax simplifies hybrid bioprocess modeling by providing a framework that automates model setup and data handling, enabling reproducible and reusable models.

The paper evaluates machine learning predictors of enzyme turnover numbers (kcat) by testing their impact on downstream metabolic models, finding that accuracy varies significantly and that validation must consider system-level effects.

ContiDesigner is a tool for designing continuous fermentation cascades that identifies optimal operating conditions to maximize productivity.

RegulomeXplorer analyzes subcellular proteomics data to visualize drug effects on protein abundance across compartments and generate mode of action hypotheses.

This paper introduces a geometric framework to analyze microbial community interactions by computing elementary composition/exchange fluxes (ECXs) that represent distinct ecological interaction types.

The paper introduces a geometric framework to analyze microbial community metabolism, linking interaction types to the structure of metabolic spaces and identifying isotypic and anisotypic mutualism.

This study uses multi-omics analysis and GSMMs to identify metabolic bottlenecks in HEK293 cell AAV production, revealing HIF1alpha's role in pseudohypoxia and its impact on viral yield.

The paper presents iCHO3K, a comprehensive metabolic model of Chinese Hamster cells, and uses it to analyze metabolic changes in lactate-free cells, identifying structural insights into metabolic rewiring.

This paper uses dynamic control flux-balance analysis to optimize 2,3-butanediol production in E. coli, achieving high titer and productivity through simulation and experimental validation.

OptFed improves product-to-biomass yield in fed-batch bioprocessing by using dynamic nonlinear modeling to optimize feed rate and reactor temperature.

The paper introduces logistic PCA (LPCA) to compare genome-scale metabolic models by identifying mechanistic differences in metabolic pathways across diverse datasets.

MeXpose quantifies metal bioaccumulation at single-cell resolution using LA-ICP-TOFMS, revealing Co accumulation in human skin cells and its correlation with DNA damage.

The paper introduces specXplore, an interactive dashboard for exploring mass spectral data, enabling structural hypothesis generation through visualization and spectral similarity analysis.

The paper develops a mechanistic model to examine how ribosome composition affects growth rate, finding that RNA instability and protein protection influence optimal ribosome composition.

This study characterizes metabolic rewiring in Komagataella phaffii under glucose-limited near-zero growth conditions using a genome-scale metabolic model and cultivation data.

This paper improves dynamic flux balance analysis for fed-batch processes by reducing KKT constraints and adapting the algorithm to predict optimal process length and feeding profiles.

The paper introduces homologueDiscoverer, an R package for detecting and managing homologue series in LC-MS data through interactive plots and local databases.

The paper introduces a normalization method, MIX, that combines pharmacokinetic and statistical approaches to correct size effects in metabolomic time series data, improving biomarker detection.

The paper identifies and annotates heterogeneous multimeric metabolite ions in LC-MS data using labeled and unlabeled Pichia pastoris extracts and demonstrates their presence in public and additional datasets.

PeakBot is a machine learning tool for detecting chromatographic peaks in LC-HRMS data, achieving high accuracy in distinguishing peaks from background signals.

This paper introduces elementary growth modes and vectors for next-generation metabolic models, providing an unbiased understanding of cellular growth and resource allocation without relying on fixed biomass composition.

This study improves intracellular flux predictions in CHO cells by incorporating maintenance energy, enhancing accuracy for TCA cycle fluxes.

This study examines how measurement errors and sampling frequency affect exchange rate accuracy and subsequent FBA predictions in CHO cells.

The paper introduces a metric to quantify metabolic robustness in genome-scale models and finds no correlation between robustness and environmental flexibility.