Model Identifier
BIOMD0000000940
Short description

Aurora Kinase B and ZAK interaction model

Equivalent of the stochastic model used in "Network pharmacology model predicts combined Aurora B and ZAK inhibition in MDA-MB-231 breast cancer cells" by Tang et. al. 2018. The only difference is cell division and partitioning of the components, which are available in the original model for SGNS2.
Format
SBML (L2V4)
Related Publication
  • Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer. Click here to expand
  • Jing Tang, Prson Gautam, Abhishekh Gupta, Liye He, Sanna Timonen, Yevhen Akimov, Wenyu Wang, Agnieszka Szwajda, Alok Jaiswal, Denes Turei, Bhagwan Yadav, Matti Kankainen, Jani Saarela, Julio Saez-Rodriguez, Krister Wennerberg, Tero Aittokallio
  • NPJ systems biology and applications , 0/ 2019 , Volume 5 , pages: 20 , PubMed ID: 31312514
  • 1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
  • Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.
Contributors
Submitter of the first revision: Krishna Kumar Tiwari
Submitter of this revision: Lucian Smith
Curator: Lucian Smith
Modeller: Krishna Kumar Tiwari

Metadata information

is (2 statements)
BioModels Database BIOMD0000000940
BioModels Database MODEL2004230001

isEncodedBy (1 statement)
BioModels Database MODEL2004230001

isDescribedBy (1 statement)
PubMed 31312514

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (3 statements)
Gene Ontology signaling
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Pharmacology

occursIn (1 statement)
Brenda Tissue Ontology breast cancer cell line

Curation status
Curated
Modelling approach(es)
ordinary differential equation model

Connected external resources

Visualisation of this model on Menelmacar platform