Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

about

The emerging role of systems biology for engineering protein production in CHO cells.Quantum Modeling: A Bridge between the Pumping and Signaling Functions of Na/K-ATPase

P2860

Q46453776-0B2111BF-15D4-4088-BD66-A63599FB50E7 Q58792509-CC118395-F168-4E40-9B7D-7C3EDDC5CF1E

P2860

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

Item

http://www.wikidata.org/entity/Q46106677

description

2016 nî lūn-bûn

@nan

2016年の論文

@ja

2016年学术文章

@wuu

2016年学术文章

@zh

2016年学术文章

@zh-cn

2016年学术文章

@zh-hans

2016年学术文章

@zh-my

2016年学术文章

@zh-sg

2016年學術文章

@yue

2016年學術文章

@zh-hant

name

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@en

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@nl

type

Item

label

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@en

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@nl

prefLabel

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@en

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

@nl

P1476

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model

@en

P2093

Philipp N Spahn

http://www.w3.org/2001/XMLSchema#string

P2860

Tight control of gene expression in mammalian cells by tetracycline-responsive promoters

Challenges of glycosylation analysis and control: an integrated approach to producing optimal and consistent therapeutic drugs

RNA-guided gene activation by CRISPR-Cas9-based transcription factors

ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering

Fc glycans terminated with N-acetylglucosamine residues increase antibody resistance to papain.

Double knockdown of alpha1,6-fucosyltransferase (FUT8) and GDP-mannose 4,6-dehydratase (GMD) in antibody-producing cells: a new strategy for generating fully non-fucosylated therapeutic antibodies with enhanced ADCC

Glycosylation of therapeutic proteins: an effective strategy to optimize efficacy

Emerging principles for the therapeutic exploitation of glycosylation.

Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds.

Metabolic engineering of monoclonal antibody carbohydrates for antibody-drug conjugation.

P31

scholarly article

P356

10.1002/BIOT.201600489

http://www.w3.org/2001/XMLSchema#string

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Nathan E. Lewis

Bjørn Voldborg

Stefan Kol

Anders Holmgaard Hansen

P577

2016-12-28T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

27860290

http://www.w3.org/2001/XMLSchema#string

P921

glycosylation

P932

5293603

http://www.w3.org/2001/XMLSchema#string

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

about

P2860

P2860

Predictive glycoengineering of biosimilars using a Markov chain glycosylation model.

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P31

P356

P433

P478

P50

P577

P698

P921

P932

P356

P1433

P1476

P2093

P2860

P31

P356

P433

P478

P50

P577

P698

P921

P932