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The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data.

Samaga R, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Klamt S - PLoS Comput. Biol. (2009)

Bottom Line: By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions.Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2.Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

ABSTRACT
The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.

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Comparison of the discretized data with predictions from the logical model.A Primary human hepatocytes (data from Alexopoulos et al, in preparation). B HepG2 cells (the horizontal line separates the first (top) from the second (bottom) dataset for HepG2 cells; see also text). Each row represents one treatment and the readouts are shown in the columns. Light green: predicted correctly, “on”; dark green: predicted correctly, “off”; light red: predicted “on”, measured “off”; dark red: predicted “off”, measured “on”, black: data points where the measured species is inhibited are not considered.
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pcbi-1000438-g008: Comparison of the discretized data with predictions from the logical model.A Primary human hepatocytes (data from Alexopoulos et al, in preparation). B HepG2 cells (the horizontal line separates the first (top) from the second (bottom) dataset for HepG2 cells; see also text). Each row represents one treatment and the readouts are shown in the columns. Light green: predicted correctly, “on”; dark green: predicted correctly, “off”; light red: predicted “on”, measured “off”; dark red: predicted “off”, measured “on”, black: data points where the measured species is inhibited are not considered.

Mentions: For each measured scenario we computed the binary network response of our model and compared it with the discretized data (Figure 8). We note that the comparison of the measured “ups and downs” with the dependency matrix (performed in the previous section) and the comparison of the discretized data with the predicted logical response are naturally correlated. However, they do not lead necessarily to exactly the same results. An example: assume you have an input stimulus (ligand L) which may activate a target species S via two independent pathways, one of both leading over an intermediate species A for which we have an inhibitor I. If we compare the scenario “stimulation with L and adding inhibitor I” against “stimulating with L” via dependency analysis we would expect a decrease in the (non-discretized) activation level of S since the inhibited species A is an activator for S. However, the phosphorylation state of S might show no significant change in the dependency analysis (i.e. leads to a “gray entry” as in Figures 6 and 7) due to the alternative pathway not affected by the inhibitor. In contrast, if the two pathways from L to S are OR-connected in the logical model, the latter would still predict S to be “on”. Another difference in the data analysis based on dependency matrix vs. logical model is that the former compares species states obtained from two different experiments (e.g. experiment with/without inhibitor) whereas the logical model gives for each experiment one (independent) prediction for each species.


The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data.

Samaga R, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Klamt S - PLoS Comput. Biol. (2009)

Comparison of the discretized data with predictions from the logical model.A Primary human hepatocytes (data from Alexopoulos et al, in preparation). B HepG2 cells (the horizontal line separates the first (top) from the second (bottom) dataset for HepG2 cells; see also text). Each row represents one treatment and the readouts are shown in the columns. Light green: predicted correctly, “on”; dark green: predicted correctly, “off”; light red: predicted “on”, measured “off”; dark red: predicted “off”, measured “on”, black: data points where the measured species is inhibited are not considered.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2710522&req=5

pcbi-1000438-g008: Comparison of the discretized data with predictions from the logical model.A Primary human hepatocytes (data from Alexopoulos et al, in preparation). B HepG2 cells (the horizontal line separates the first (top) from the second (bottom) dataset for HepG2 cells; see also text). Each row represents one treatment and the readouts are shown in the columns. Light green: predicted correctly, “on”; dark green: predicted correctly, “off”; light red: predicted “on”, measured “off”; dark red: predicted “off”, measured “on”, black: data points where the measured species is inhibited are not considered.
Mentions: For each measured scenario we computed the binary network response of our model and compared it with the discretized data (Figure 8). We note that the comparison of the measured “ups and downs” with the dependency matrix (performed in the previous section) and the comparison of the discretized data with the predicted logical response are naturally correlated. However, they do not lead necessarily to exactly the same results. An example: assume you have an input stimulus (ligand L) which may activate a target species S via two independent pathways, one of both leading over an intermediate species A for which we have an inhibitor I. If we compare the scenario “stimulation with L and adding inhibitor I” against “stimulating with L” via dependency analysis we would expect a decrease in the (non-discretized) activation level of S since the inhibited species A is an activator for S. However, the phosphorylation state of S might show no significant change in the dependency analysis (i.e. leads to a “gray entry” as in Figures 6 and 7) due to the alternative pathway not affected by the inhibitor. In contrast, if the two pathways from L to S are OR-connected in the logical model, the latter would still predict S to be “on”. Another difference in the data analysis based on dependency matrix vs. logical model is that the former compares species states obtained from two different experiments (e.g. experiment with/without inhibitor) whereas the logical model gives for each experiment one (independent) prediction for each species.

Bottom Line: By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions.Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2.Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.

ABSTRACT
The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.

Show MeSH