Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway.
BACKGROUND
Appropriately formulated quantitative computational models can support researchers in understanding the dynamic behaviour of biological pathways and support hypothesis formulation and selection by "in silico" experimentation. An obstacle to widespread adoption of this approach is the requirement to formulate a biological pathway as machine executable computer code. We have recently proposed a novel, biologically intuitive, narrative-style modelling language for biologists to formulate the pathway which is then automatically translated into an executable format and is, thus, usable for analysis via existing simulation techniques.


RESULTS
Here we use a high-level narrative language in designing a computational model of the gp130/JAK/STAT signalling pathway and show that the model reproduces the dynamic behaviour of the pathway derived by biological observation. We then "experiment" on the model by simulation and sensitivity analysis to define those parameters which dominate the dynamic behaviour of the pathway. The model predicts that nuclear compartmentalisation and phosphorylation status of STAT are key determinants of the pathway and that alternative mechanisms of signal attenuation exert their influence on different timescales.


CONCLUSIONS
The described narrative model of the gp130/JAK/STAT pathway represents an interesting case study showing how, by using this approach, researchers can model biological systems without explicitly dealing with formal notations and mathematical expressions (typically used for biochemical modelling), nevertheless being able to obtain simulation and analysis results. We present the model and the sensitivity analysis results we have obtained, that allow us to identify the parameters which are most sensitive to perturbations. The results, which are shown to be in agreement with existing mathematical models of the gp130/JAK/STAT pathway, serve us as a form of validation of the model and of the approach itself.
Guerriero
Maria Luisa
ML
Laboratory for Foundations of Computer Science, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, EH8 9AB, Edinburgh, UK. mguerrie@inf.ed.ac.uk
Dudka
Anna
A
Underhill-Day
Nicholas
N
Heath
John K
JK
Priami
Corrado
C
D000465:Algorithms; D045744:Cell Line, Tumor; D002467:Cell Nucleus / Q000378:metabolism; D019295:Computational Biology; D003198:Computer Simulation; D006023:Glycoproteins / Q000378:metabolism*; D006801:Humans; D053612:Janus Kinases / Q000378:metabolism*; D008954:Models, Biological; D010766:Phosphorylation; D011381:Programming Languages; D021381:Protein Transport; D015203:Reproducibility of Results; D050791:STAT Transcription Factors / Q000378:metabolism*; D015398:Signal Transduction; D013269:Stochastic Processes
D006023:Glycoproteins; D050791:STAT Transcription Factors; C480482:glycoprotein 130, human; D053612:Janus Kinases