Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies
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Lorenzo Tortolina2,*, David J. Duffy3,*, Massimo Maffei1,*, Nicoletta Castagnino1,*, Aimée M. Carmody3,*, Walter Kolch3, Boris N. Kholodenko3, Cristina De Ambrosi2, Annalisa Barla2, Elia M. Biganzoli4, Alessio Nencioni1,5, Franco Patrone1,5, Alberto Ballestrero1,5, Gabriele Zoppoli1,5, Alessandro Verri2 and Silvio Parodi1
1 Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Italy
2 Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Italy
3 Systems Biology Ireland, Conway Institute, University College Dublin, Belfield, Dublin, Ireland
4 Unit of Medical Statistics, Biometry and Bioinformatics “Giulio A. Maccacaro”, Department of Clinical Sciences and Community Health, University of Milan, Italy
5 Istituto a Carattere di Ricerca Clinic - Scientifico (IRCCS), Azienda Ospedaliera Universitaria San Martino, Istituto Nazionale Tumori (IST), Genoa, Italy
* These authors contributed equally to this work
Silvio Parodi, email:
Keywords: Colorectal cancer, Target therapies, Signaling-network, Dynamic modeling, Onco-protein inhibitors
Received: December 17, 2014 Accepted: December 28, 2014 Published: December 31, 2014
The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.
We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.
Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.
Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.
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