Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

Abstract

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.

Measuring Transcription Rate Changes via Time-Course 4-Thiouridine Pulse-Labelling Improves Transcriptional Target Identification

“…Half-lives of c-MYC, MYCN, cyclin and RPLP0 were measured by transcriptional inhibition through actinomycin D (Sigma-Aldrich) followed by quantitative PCR (adapted from Refs. [19] and [20]). …”

Problems, challenges and promises: perspectives on precision medicine

“…Many of the bioinformatic approaches applied to precision medicine are currently concerned with diagnosis and treatment response prediction by molecular stratification of patients using genetic and more generally omic profiling. However, computational mathematical models are being generated to predict mode-of-action and responses-to-treatments (perturbations) not just at the molecular level but across all levels of biological organisation, including molecular, gene regulatory networks, signal transduction pathways and metabolic networks, cell populations, tissue level and whole organism models [10,[35][36][37][38]. In addition, models are being generated to account for pharmacokinetics and pharmacodynamics to analyse drug action, and even human-population level pharmacogenomics models of disease risk [10,39,40].…”

“…Mechanistic understanding of disease should facilitate the in silico identification of drugs approved for treatment of one condition which would be beneficial for other previously unrelated conditions, by revealing common deregulated network features and vulnerable nodes/targets. Similarly, modelling disease related signalling networks will rationalise the identification of synergistic drug combinations, by enabling the computational simulation of all conceivable drug treatment combinations across a given network [37].…”

“…Perhaps the most common approach currently being utilised is network-based, where individualised data are incorporated into pre-constructed physiological signalling networks [35,37]. When these networks are constructed as dynamic mechanistic models, simulations can then be performed to assess how the patient's specific data alters the output (for example, pathway activation status, transcriptional regulation or resulting phenotypes) of the network and what treatment options may return the diseased network back to a physiologically normal condition [37]. The…”

“…While this resource does not currently provide predictions regarding drug treatments, it is the type of large-scale integrated network tool which could be coupled to the predictive power of mechanistic models, like those described by Tortolina et al [37].…”