Abstract
Protein signaling networks are static views of dynamic processes where proteins go through many biochemical modifications such as ubiquitination and phosphorylation to propagate signals that regulate cells and can act as feed-back systems. Understanding the precise mechanisms underlying protein interactions can elucidate how signaling and cell cycle progression occur within cells in different diseases such as cancer. Large-scale protein signaling networks contain an important number of experimentally verified protein relations but lack the capability to predict the outcomes of the system, and therefore to be trained with respect to experimental measurements. Boolean Networks (BNs) are a simple yet powerful framework to study and model the dynamics of the protein signaling networks. While many BN approaches exist to model biological systems, they focus mainly on system properties, and few exist to integrate experimental data in them. In this work, we show an application of a method conceived to integrate time series phosphoproteomic data into protein signaling networks. We use a large-scale real case study from the HPN-DREAM Breast Cancer challenge. Our efficient and parameter-free method combines logic programming and model-checking to infer a family of BNs from multiple perturbation time series data of four breast cancer cell lines given a prior protein signaling network. Because each predicted BN family is cell line specific, our method highlights commonalities and discrepancies between the four cell lines. Our models have a Root Mean Square Error (RMSE) of 0.31 with respect to the testing data, while the best performant method of this HPN-DREAM challenge had a RMSE of 0.47. To further validate our results, BNs are compared with the canonical mTOR pathway showing a comparable AUROC score (0.77) to the top performing HPN-DREAM teams. In addition, our approach can also be used as a complementary method to identify erroneous experiments. These results prove our methodology as an efficient dynamic model discovery method in multiple perturbation time course experimental data of large-scale signaling networks. The software and data are publicly available at https://github.com/misbahch6/caspo-ts.
Highlights
Protein signaling networks are static views of dynamic processes since they respond to stimuli and perturbation
We have applied the caspo time series approach which is a combination of logic programming and model checking, over the time series phosphoproteomic dataset of the HPN-DREAM challenge to learn cell specific Boolean Networks (BNs)
Our analysis suggests that caspo-ts scales to real datasets, outputting networks that are not random with a lower fitness error than the models used by the 178 methods which participated in the HPN-DREAM challenge
Summary
Protein signaling networks are static views of dynamic processes since they respond to stimuli and perturbation. They constitute complex regulatory systems controlled by crosstalk and feedback mechanism. Because these networks are often altered in diseases, discovering the precise mechanisms of signal transduction may provide a better fundamental understanding of disease behavior. A main difficulty in cancer treatment is that different signaling networks fact that cell populations specialize upon treatment and patient responses may be heterogeneous. Computational models of signaling control for different patient groups could guide cancer research towards a better drug targeting system. We propose a methodological framework to discriminate among the regulatory mechanisms of four breast cancer cell lines by building predictive computational models
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