Our group studies human cell signalling with the aim to understand what controls different cell responses in different environments, tissues and conditions. We analyse and integrate diverse 'omics' datasets (with emphasis on phosphoproteomics and proteomics), to extract and compare context-specific signalling networks.
The long term aim is to understand the principles of human cell signalling regulation and create predictive and conditional whole-cell signalling models. We will use these models to gain insights into basic cell functions and disease mechanisms, which can aid the design of precise therapeutic approaches and the discovery of reliable biomarkers.
We are interested in taking advantage of the phosphoproteome layer of cell function regulation and integrating it with data from other layers and prior knowledge towards addressing the following questions:
a) How does rewiring human cell signalling networks result in different cell phenotypes?
b) Which are the regulators of this rewiring? What are the molecular mechanisms?
c) How do differences in the genome, or transcriptome layer of cell regulation affect the cell’s signalling networks?
d) What happens to the signalling networks and the cell phenotype when we perturb the network?
e) What are the minimal data points across the layers of cell regulation that we need to measure to be able to predict the cell signalling state and its phenotype?
We are a computational lab and we aim to use any approach that will contribute to addressing our questions. Currently, we use data-driven network reconstruction approaches to infer signalling networks from phosphoproteomics data integrated with other ‘omics’ data and prior knowledge. We then use modelling methods to simulate signal propagation, which allows us to perform in silico perturbations and predict the resulting signalling networks.
We additionally collaborate with experimental groups that specialise in mass spectrometry, high-throughput imaging and cell biology to enrich our input datasets and validate our models towards addressing specific biological questions (e.g. the effect of cell signalling on cell shape and migration, stem cell differentiation, and others).
Our long-term goal is to create a flexible and modular framework that can bring together our community to create an accurate, predictive and accessible Whole Cell Signalling model that can be used for predicting a cell’s signalling state and phenotype given a specific ‘omics’ dataset or profile.
In addition to satisfying our curiosity regarding these fundamental biological questions, our lab is also interested in translational applications of this research, for example through understanding the molecular mechanism of disease development and designing strategies for diagnosis and treatment, or through uncovering patient-specific signalling networks that can be used to guide precision therapies.
Helbig AO, Kofler M, Petsalaki E, et al. (2017) The Fes tyrosine kinase guides CD19 receptor fate in B-cells by shaping regulatory Src phosphorylation networks. bioRxiv 125088
Betts MJ, Wichmann O, Utz M, Andre T, Petsalaki E et al. (2017) Systematic identification of phosphorylation-mediated protein interaction switches. PLoS Comput Biol. 13(3):e1005462
Yachie N, Petsalaki E, et al. (2016) Pooled-matrix protein interaction screens using Barcode Fusion Genetics. Mol. Sys.Biol 12:863
Petsalaki E, et al. (2015) SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets. Nucleic Acids Res. 43:W276-W282
Yang F, et al. (2015) Protein domain-level landscape of cancer-type-specific somatic mutations. PLoS Comput Biol. 11:e1004147
Nott TJ, et al. (2015) Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57:936