Combined logical and data-driven models for linking signalling pathways to cellular response

Abstract

BackgroundSignalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity.ResultsIn this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.ConclusionsWe developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

“…An example of a data-driven approach combined with logical modeling is a method developed by Alexopulos and colleagues that integrates logical models of signaling pathways, optimized with high-throughput phosphoproteomic data, to cellular responses such as growth, death, differentiation, cytokine secretion and gene expression in order to investigate cellular responses in hepatocellular carcinoma (Melas et al. 2011). A multi-linear regression algorithm was employed to generate regression coefficients that serve as weights for the connection of key phosphoproteins with cellular responses.…”

Can case study approaches speed implementation of the NRC report: “Toxicity Testing in the 21st Century: A Vision and a Strategy?”

“…The cascade behavior of these networks (Landers and Spelsberg, 1992) is expected in turn to show both time and dose-dependencies in network function. In addition, high throughput- phosphoproteomic data now provide opportunities to construct extended pathways that link receptor activation with cellular phenotypes (Samaga et al ., 2009; Melas et al ., 2011); more limited evaluation of contributions of mitogen-activated protein kinases (MAPKs) to specific phenotypes after pathway activation have shown the differential control of key signaling nodes within overall networks (Bromberg et al ., 2008a; Zorina et al ., 2010). Integrated systems biology data – gene expression, phosphoprotein alterations, transcription factor analysis – will be needed to provide the dynamic and functional characterization of these assays and their associated toxicity pathways.…”

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation

“…However, their method needs detailed kinetic parameters which may not be available as yet in many cases. A similar study of Melas et al [28] first employed a multi linear regression algorithm to identify correlation-based relationships between signaling proteins and cellular responses (e.g., cytokine releases) and connected them using “non-canonical” edges. Integrating a canonical network of the signaling pathway from prior knowledge, the whole network was then converted into a Boolean model.…”