Nathan E. Lewis

The paper uses SEC-seq to analyze metabolic and secretory strategies in CHO cells, finding that high IgG-producing clones use distinct translation-focused metabolic states.

EvoMut identifies oxidation-prone protein residues and evaluates their mutation feasibility to design oxidation-resistant proteins.

This study generates glycoengineered recombinant alpha1-antitrypsin with in vitro and in vivo activities comparable to plasma-derived A1AT, offering a sustainable therapeutic alternative.

The paper maps protein interactions involved in IL-2 secretion in human T cells, identifying key components of the secretory pathway and their dynamic regulation.

LeGenD predicts N-glycan structures and their abundance using lectin-binding patterns and AI, trained on glycoprofiles from recombinant proteins.

The paper develops human ME-Models that integrate gene expression and metabolism, showing improved growth-rate predictions and biological plausibility in cancer cell lines.

The paper investigates the phenotypic and transcriptional effects of glycoengineering in CHO cells, identifying distinct clusters and molecular mechanisms associated with different glycosyltransferase modifications.

This study investigates how glycan structures affect GM-CSF bioactivity by engineering glycoforms in Chinese hamster ovary cells and testing their effects on TF-1 cell proliferation.

Tensor-cell2cell v2 jointly analyzes protein- and metabolite-mediated cell-cell communication over time, revealing coordinated dynamics in brain organoid development.

MCBO provides a standardized ontology for mammalian cell bioprocessing data to enable harmonization and querying of complex biomanufacturing datasets.

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 introduces FcBAR, a method to detect protein-protein interactions in situ for recombinant proteins, and identifies factors that enhance rituximab production.

The paper identifies protein interactors that enhance the secretion of HCV E1E2 vaccine candidates in glycoengineered CHO cells using a multi-omics approach.

The paper investigates lineage commitment bias in human and murine hematopoietic stem cells, finding that variation in myeloid and lymphoid engraftment is stochastic and not due to cell-intrinsic bias.

The paper introduces scCellFie, a framework to infer metabolic activities from single-cell and spatial transcriptomics data, revealing organ-specific metabolic programs and disease-associated alterations.

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 study shows that glycoengineering of HCV E2 glycoprotein affects antigen biochemical properties and immune response, with some glycans improving neutralization potency.

This study eliminates the Warburg Effect in mammalian cells by knocking out lactate dehydrogenase and feedback regulators, maintaining growth rate while reducing lactate production.

iLipidome analyzes lipidomics data using biosynthetic networks to improve statistical power and interpretability, identifying genetic causes of lipid changes and potential therapeutic targets.

The paper trains a hybrid neural network to predict N-glycosylation events and structures from protein sequences using glycosite-flanking data, demonstrating its ability to predict glycan composition and abundance.

The paper identifies a relationship between protein structure and glycan structure, introducing a glycoimpact matrix to predict glycosylation patterns and validate it through disease variants and engineered antibodies.

This paper uses a Boltzmann model to predict glycan structures from lectin binding data, achieving 88% accuracy for N-glycans and 87% for O-glycans in a test set.

This paper integrates LIANA and Tensor-cell2cell to analyze cell-cell communication across multiple samples, enabling robust and flexible identification of communication programs.

LipidSIM infers lipid biosynthesis perturbations from lipidomics data using a low-parameter Markov model, demonstrating its utility in studying disease-related lipidome changes.

COSMIC-dFBA predicts cell density, antibody titer, and metabolite concentrations in bioreactors by integrating machine learning with genome-scale metabolic models.

The paper introduces secCellFie, a tool that infers secretory and metabolic pathway activity from omic data, demonstrating its application in immune cells, NAFLD hepatokine secretion, and antibody production in Chinese Hamster Ovary cells.

This study creates a single-cell gene expression atlas of C. elegans, identifying cell types, housekeeping genes, and intercellular interactions using scRNA-seq and ChIP-seq data.

GlyCompareCT preprocesses glycomics data to address sparsity and non-independence, improving statistical power for downstream analysis.

The paper evaluates algorithms for generating context-specific metabolic models using gene expression data and identifies guidelines for selecting the best models.