Anne Richelle

This paper introduces a probabilistic framework using Bayesian inference to improve metabolic rate estimation from sparse concentration data in bioprocesses.

The paper develops a hybrid modeling framework for predicting CHO cell culture digital twins by integrating ODE models, metabolic constraints, and machine learning trained on Bayesian-estimated metabolic rates.

The paper presents secRecon, a comprehensive secretory pathway reconstruction, and uses it to identify secretory differences in plasma cells and CHO cells, highlighting potential targets for improving secretion capacity.

The paper develops a metabolomics-driven framework to systematically reduce genome-scale metabolic models, validated on CHO cell cultures to improve dynamic bioprocess simulation.

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.

ImmCellFie infers cell metabolic capabilities from transcriptomic or proteomic data using genome-scale models and provides interactive analysis tools.

The paper constructs an HMO biosynthesis network using multi-omics integration to predict glycosyltransferases and propose candidate genes for HMO synthesis pathways.

The paper introduces a framework to predict how changes in omics data affect metabolic functions in mammalian cells using curated metabolic tasks and genome-scale networks.

StanDep improves context-specific metabolic models by using transcriptomic variability to identify core reactions, increasing inclusion of housekeeping and lowly expressed reactions.

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.

The paper evaluates how different decisions in transcriptomic data integration affect the identification of active reactions in biochemical networks across tissues.

This study improves context-specific metabolic models by integrating data-inferred cell functions, reducing model variability across algorithms and better capturing biological variability.