Perturbation Biology: Inferring Signaling Networks in Cellular Systems

Abstract

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

“…Likewise, signaling networks have been reconstructed by stimulating a pathway and perturbing signaling nodes with kinase inhibitors or RNA interference. Protein activities are observed with antibody-based assays, and pathways are recovered de novo (Ciaccio et al, 2015;Fröhlich et al, 2009;Kiani and Kaderali, 2014;Molinelli et al, 2013) or by adapting prior pathway knowledge (Morris et al, 2011). The PHONEMeS method is unique for its ability to handle large-scale phosphoproteomic perturbation data (Terfve et al, 2015).…”

Network Analysis Identifies Crosstalk Interactions Governing TGF-β Signaling Dynamics during Endoderm Differentiation of Human Embryonic Stem Cells

“…Significant care was taken to infer connections from independent repeats of the same experimental condition due to high variability of the system and emphasis was given to most repeated connections across independent repeats. While current analysis indicates robustness of the predictions across independent repeats, verification of such robustness is only possible by running selective perturbation experiments [53,54], which will be considered in our future work.…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…They integrated signaling network pathways, Proteomic data and five phenotypic responses of cell cycle progression after perturbation with two-drug combinations or single drugs. The belief propagation (BP) algorithm was used to search the network models based on probability distributions that represent the set of network models with the lowest error [81]. The authors predicted an effective combination of c-Myc with either BRAF-or MEK-targeted therapies.…”