Mechanistic and predictive models of signaling networks are powerful tools to understand signal transduction, its deregulation in disease, and the mode of action of therapies. We use different approaches, and we are particularly focused on logic models since, due to their simplicity, they can handle large networks and phosphor-proteomic datasets with coverage of up to thousands of proteins. We develop methods and tools based on this formalism and apply them in different contexts.
We have developed CellNOpt ( CellNetOptimizer) is a software used for creating models of signal transduction networks using different logic formalisms, including Boolean, Fuzzy, or differential equations (Terfve et al. 2012), that are trained to signaling (typically phosphoproteomic) data. Further information on CellNOpt is available at www.cellnopt.org.
We are currently exploring the application of logic modelling to single-cell signaling data and the integration of signaling and metabolic data.
Because proteins are the key players of signal transduction, we primarily use proteomic data, more precisely phosphoproteomic data as a proxy of the activity status of signaling proteins. However, we have also develop methods to train signaling netowrks with gene expression data, which is more widely available. Here, we use the causal-reasoning paradigm, whereby the activity of a signalign pathway is inferred from the levels of the transcripts downstream of it (Melas et al. 2015).
Models are built by training a generic model to dedicated dataset. This boils down to an optimization problem. This optimization problem can be solved sometimes using formal methods (specially when the models are binary, i.e. Boolean), such as Integer Linear Programming (Melas et al. 2015,Mitsos et al. 2009) or Answer Set Programming (Guziolowski et al. 2013). For continuous formalisms (Fuzzy logic and logic ordinary differential equations), we rely on heuristics implemented in the tool MEIGO (Egea et al., 2014) developed jointly with the group of Julio Banga.