ArticleMetabolic engineering2016
A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering.
Article in Metabolic engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed.
- Glycan reachability analysis: A bottleneck-aware framework for inferring tissue-specific glycan biosynthetic potential from transcriptomics.PLoS computational biology · 2026Article
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Glycoengineering of the hepatitis C virus E2 glycoprotein improves biochemical properties and enhances immunogenicity.NPJ vaccines · 2025Article
- Glycoengineering of the hepatitis C virus E2 glycoprotein leads to improved biochemical properties and enhanced immunogenicity.bioRxiv : the preprint server for biology · 2025Article
- Mechanical effect of protein glycosylation on BiP-mediated post-translational translocation and folding in the endoplasmic reticulum.Biophysical reviews · 2025Review
- GlycoMaple: recent updates and applications in visualization and analysis of glycosylation pathways.Analytical and bioanalytical chemistry · 2025Review
- In silico simulation of glycosylation and related pathways.Analytical and bioanalytical chemistry · 2024Review
- Boltzmann Model Predicts Glycan Structures from Lectin Binding.Analytical chemistry · 2024Article
- LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling.bioRxiv : the preprint server for biology · 2024Article
- A Boltzmann model predicts glycan structures from lectin binding.bioRxiv : the preprint server for biology · 2024Article
- LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.Metabolic engineering · 2024Article
- Combinatory glycoengineering of monoclonal antibodies and its application in cancer therapy: a narrative review.Translational cancer research · 2024Review
- Functional characterization and structural basis of a reversible glycosyltransferase involves in plant chemical defence.Plant biotechnology journal · 2023Article
- Glycosylation shapes the efficacy and safety of diverse protein, gene and cell therapies.Biotechnology advances · 2023Review
- CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells.Metabolic engineering · 2023Article
- GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology.Bioinformatics (Oxford, England) · 2022Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Immunoglobulin G N-glycan Biomarkers for Autoimmune Diseases: Current State and a Glycoinformatics Perspective.International journal of molecular sciences · 2022Review
- Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration.Nature communications · 2022Article
- A roadmap for translational cancer glycoimmunology at single cell resolution.Journal of experimental & clinical cancer research : CR · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.