Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1532-1827

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer ; volume:122 ; number:2 ; day:10 ; month:12 ; year:2019 ; pages:233-244 ; date:1.2020
British journal of cancer ; 122, Heft 2 (10.12.2019), 233-244, 1.2020

Creator: Berndt, Nikolaus
Egners, Antje
Mastrobuoni, Guido
Vvedenskaya, Olga
Fragoulis, Athanassios
Dugourd, Aurélien
Bulik, Sascha
Pietzke, Matthias
Bielow, Chris
Gassel, Rob van
Damink, Steven W. Olde
Erdem, Merve
Sáez Rodríguez, Julio
Holzhütter, Hermann-Georg
Kempa, Stefan
Cramer, Thorsten

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41416-019-0659-3

URN: urn:nbn:de:101:1-2021011717553030923640

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:35 AM CEST

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Associated

Berndt, Nikolaus
Egners, Antje
Mastrobuoni, Guido
Vvedenskaya, Olga
Fragoulis, Athanassios
Dugourd, Aurélien
Bulik, Sascha
Pietzke, Matthias
Bielow, Chris
Gassel, Rob van
Damink, Steven W. Olde
Erdem, Merve
Sáez Rodríguez, Julio
Holzhütter, Hermann-Georg
Kempa, Stefan
Cramer, Thorsten
SpringerLink (Online service)

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Personalized signaling models for personalized treatments

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Looking beyond the cancer cell for effective drug combinations

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Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

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Other Objects (12)

Modular analysis of signal transduction networks

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Personalized signaling models for personalized treatments

Enablers and challenges of spatial omics, a melting pot of technologies

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Bridging the gap between signalling and metabolism through functional and mechanistic analysis of multi-omic data

Modeling intra- and intercellular communication in the context of human cancer from high-throughput data

Personalized signaling models for personalized treatments

Looking beyond the cancer cell for effective drug combinations

CySBGN: A Cytoscape plug-in to integrate SBGN maps

From gene expression to pathway and transcription factor activities to study chronic liver diseases

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

Modular analysis of signal transduction networks

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Personalized signaling models for personalized treatments

Enablers and challenges of spatial omics, a melting pot of technologies

Multi-scale molecular descriptions of human heart failure using single cell, spatial, and bulk transcriptomics

Bridging the gap between signalling and metabolism through functional and mechanistic analysis of multi-omic data

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Personalized signaling models for personalized treatments

Looking beyond the cancer cell for effective drug combinations

CySBGN: A Cytoscape plug-in to integrate SBGN maps

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Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Personalized signaling models for personalized treatments

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CySBGN: A Cytoscape plug-in to integrate SBGN maps

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Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

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