Tang2019 - Pharmacology modelling of AURKB and ZAK interaction in TNBC

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Model Identifier
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.
Related Publication
  • Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.
  • Tang J, Gautam P, Gupta A, He L, Timonen S, Akimov Y, Wang W, Szwajda A, Jaiswal A, Turei D, Yadav B, Kankainen M, Saarela J, Saez-Rodriguez J, Wennerberg K, Aittokallio T
  • NPJ systems biology and applications , 1/ 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.
Submitter of the first revision: Krishna Kumar Tiwari
Submitter of this revision: Krishna Kumar Tiwari
Modellers: Krishna Kumar Tiwari

Metadata information

isEncodedBy (1 statement)
BioModels Database MODEL2004230001

is (2 statements)
BioModels Database MODEL2004230001
BioModels Database BIOMD0000000940

isDescribedBy (1 statement)
PubMed 31312514

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (3 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Gene Ontology signaling
NCIt NCIT:C16974

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

Curation status


Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

Tang2019.xml SBML L2 V4 file for the model 180.30 KB Preview | Download

Additional files

Tang2019.cps COPASI 4.27(Build217) file 233.70 KB Preview | Download
Tang2019.sedml SEDML file for the model 1.10 KB Preview | Download

  • Model originally submitted by : Krishna Kumar Tiwari
  • Submitted: Apr 23, 2020 9:23:32 PM
  • Last Modified: Apr 28, 2020 1:08:50 AM
  • Version: 3 public model Download this version
    • Submitted on: Apr 28, 2020 1:08:50 AM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Automatically added model identifier BIOMD0000000940
  • Version: 1 public model Download this version
    • Submitted on: Apr 23, 2020 9:23:32 PM
    • Submitted by: Krishna Kumar Tiwari
    • With comment: Import of Tang2019 - Pharmacology modelling of AURKB and ZAK interaction in TNBC

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

: Variable used inside SBML models

Reactions Rate Parameters
=> MAP2K4; ZAK Cell*k_map2k4*ZAK k_map2k4 = 0.2
TP53 => Cell*kd_tp53*TP53 kd_tp53 = 2.0
MAPK13 => Cell*kd_mapk13*MAPK13 kd_mapk13 = 1.4
=> PARP1 Cell*k_parp1 k_parp1 = 0.5
AURKB => Cell*kd_aurkb*AURKB kd_aurkb = 4.5
=> TGFBR1; ZAK Cell*k_tgfbr1*ZAK k_tgfbr1 = 0.5
=> TP53; MAPK14 Cell*k_tp53*MAPK14 k_tp53 = 0.6
=> SRC; CSF1R Cell*k_src*CSF1R k_src = 0.2
MAPK14 => Cell*kd_mapk14*MAPK14 kd_mapk14 = 5.0
=> TP53; BAD Cell*k_tp53*BAD k_tp53 = 0.6
Curator's comment:
(added: 28 Apr 2020, 01:08:25, updated: 28 Apr 2020, 01:08:25)
Model encoded in COPASI 4.27(Build217) and figure generated using Excel. Figure 4C is reproduced in the model using all original value.