Methods for logic modeling of signaling networks
Mechanistic and predictive models of signaling networks are powerful tools to understand signal transduction, its deregulation in disease, and drugs’ mode of action. We use logic models, because they can handle large networks and phospho-proteomic and transcriptomic datasets with coverage of up to thousands of proteins and genes. We develop methods and tools based on this formalism and apply them in different contexts.
Regardless of the data and formalism, we translate a generic network to logic model, which is then trained to perturbation dataset (see Fig.). The generic prior knowledge network (PKN), is built based on literature and interaction databases, for which we use our resource Omnipath (Türei, Korcsmáros, and Saez-Rodriguez 2016,Ceccarelli et al. 2020). The PKN describes the possible interactions among the signaling molecules and connects the perturbations to the measured molecular markers. Different formalisms, as described below, build different types of models from the PKN, ranging from Boolean networks to differential equations. Then we train the models to the measured data using optimisation methods. We also have tools to expand the PKN as our knowledge is generally incomplete (Eduati et al. 2012, Gjerga et al. 2019).
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) or Answer Set Programming (Guziolowski et al. 2013). For continuous formalisms, we rely on heuristics implemented in the tool MEIGO (Egea et al. 2014) developed jointly with the group of Julio Banga. For large-scale systems, we can use parallel optimization methods such as SaceSS (Penas et al. 2017).
The aforementioned workflow is performed with our open source tool CellNOpt (CellNetOptimizer) (Saez-Rodriguez et al. 2009; MacNamara et al. 2012; Morris et al. 2011; Morris, Melas, and Saez-Rodriguez 2012(Gjerga et al. 2020 )). CellNOpt uses different logic formalisms. These formalisms include Boolean, Fuzzy, Probabilistic, and Ordinary Differential Equation models (Gjerga et al. 2020) which are trained against (phosphoproteomic) data. Applications range from cancer (Eduati et al. 2017) (Eduati et al. 2020) to multiple sclerosis (Bernardo-Faura et al. 2019) and drug predictions (Tognetti et al. 2020)
We have also developed PHONEMeS (Terfve et al. 2015), a related tool to build logic models from discovery mass-spectrometry based Phosphoproteomic data. We apply PHONEMeS to various contexts, in particular in cancer. We are currently exploring the application of logic modelling to single-cell signaling data (Tognetti et al. 2020).
In addition, we have developed CARNIVAL (Liu, Trairatphisan, Gjerga et al. 2019) , a method to train signaling networks from gene expression data. Here, we use the causal-reasoning paradigm to infer the topology of signaling pathway from downstream transcripts’ levels, using Integer Linear Programming (Melas et al. 2015). An application was shown for the case of IgA nephropathy: a chronic kidney disease (Liu, Trairatphisan, Gjerga et al. 2019) We are now working on scaling CARNIVAL up to multiple types of omic data with the COSMOS (Dugourd, Kuppe, Sciacovelli et al. 2020) framework. This is done in particular in the context of integrating signaling and metabolic data.

Figure 1. General pipeline for training mechanistic models. Perturbation data is used to train a model that is generated from the interaction network (Prior Knowledge Network). Depending on the selected formalism, the appropriate parameterised model is built and the model parameters are tuned using optimisation algorithm until a reasonable fit between the model predictions and measured data is obtained. This way we can obtain mechanistic models tailored to the experimental data.
Selected References:
- Bernardo-Faura, Marti, Melanie Rinas, Jakob Wirbel, Inna Pertsovskaya, Vicky Pliaka, Dimitris E. Messinis, Gemma Vila, et al. 2019 “Prediction of Combination Therapies Based on Topological Modeling of the Immune Signaling Network in Multiple Sclerosis.” https://doi.org/10.1101/541458.
- Francesco Ceccarelli, Denes Turei, Attila Gabor, Julio Saez-Rodriguez, Bringing data from curated pathway resources to Cytoscape with OmniPath, Bioinformatics, 2020, btz968, https://doi.org/10.1093/bioinformatics/btz968.
- Eduati, Federica, Javier De Las Rivas, Barbara Di Camillo, Gianna Toffolo, and Julio Saez-Rodriguez. 2012. “Integrating Literature-Constrained and Data-Driven Inference of Signalling Networks.” Bioinformatics 28 (18): 2311–17. https://doi.org/10.1093/bioinformatics/bts363.
- Eduati, Federica, Victoria Doldàn-Martelli, Bertram Klinger, Thomas Cokelaer, Anja Sieber, Fiona Kogera, Mathurin Dorel, Mathew J. Garnett, Nils Blüthgen, and Julio Saez-Rodriguez. 2017. “Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type–Specific Dynamic Logic Models.” Cancer Research. https://doi.org/10.1158/0008-5472.can-17-0078.
- Federica Eduati, Patricia Jaaks, Jessica Wappler, Thorsten Cramer, Christoph A Merten, Mathew J Garnett, Julio Saez‐Rodriguez. Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies Mol Syst Biol, 2020. 16:e8664 https://doi.org/10.15252/msb.20188664.
- Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Julio Saez-Rodriguez, Literature and data-driven based inference of signalling interactions using time-course data, IFAC-PapersOnLine, 52:26, 2019, pp. 52-57, https://doi.org/10.1016/j.ifacol.2019.12.235.
- Enio Gjerga, Panuwat Trairatphisan, Attila Gabor, Hermann Koch, Celine Chevalier, Francesco Ceccarelli, Aurelien Dugourd, Alexander Mitsos, Julio Saez-Rodriguez. Converting networks to predictive logic models from perturbation signalling data with CellNOpt. bioRxiv 2020.03.04.976852; doi: https://doi.org/10.1101/2020.03.04.976852.
- Guziolowski, Carito, Santiago Videla, Federica Eduati, Sven Thiele, Thomas Cokelaer, Anne Siegel, and Julio Saez-Rodriguez. 2013. “Exhaustively Characterizing Feasible Logic Models of a Signaling Network Using Answer Set Programming.” Bioinformatics 29 (18): 2320–26. https://doi.org/10.1093/bioinformatics/btt393.
- Henriques, David, Miguel Rocha, Julio Saez-Rodriguez, and Julio R. Banga. 2015. “Reverse Engineering of Logic-Based Differential Equation Models Using a Mixed-Integer Dynamic Optimization Approach.” Bioinformatics 31 (18): 2999–3007. https://doi.org/10.1093/bioinformatics/btv314.
- Liu, Anika, Panuwat Trairatphisan, Enio Gjerga, Athanasios Didangelos, Jonathan Barratt, and Julio Saez-Rodriguez. 2019 “From Expression Footprints to Causal Pathways: Contextualizing Large Signaling Networks with CARNIVAL.” https://doi.org/10.1101/541888.
- MacNamara, Aidan, Camille Terfve, David Henriques, Beatriz Peñalver Bernabé, and Julio Saez-Rodriguez. 2012. “State-Time Spectrum of Signal Transduction Logic Models.” Physical Biology 9 (4): 045003. https://doi.org/10.1088/1478-3975/9/4/045003.
- Melas, Ioannis N., Theodore Sakellaropoulos, Francesco Iorio, Leonidas G. Alexopoulos, Wei-Yin Loh, Douglas A. Lauffenburger, Julio Saez-Rodriguez, and Jane P. F. Bai. 2015. “Identification of Drug-Specific Pathways Based on Gene Expression Data: Application to Drug Induced Lung Injury.” Integrative Biology: Quantitative Biosciences from Nano to Macro 7 (8): 904–20. https://doi.org/10.1039/c4ib00294f.
- Morris, Melody K., Ioannis Melas, and Julio Saez-Rodriguez. 2012. “Construction of Cell Type-Specific Logic Models of Signaling Networks Using CellNOpt.” In Methods in Molecular Biology, 179–214. https://doi.org/10.1007/978-1-62703-059-5_8.
- Penas, David R., David Henriques, Patricia González, Ramón Doallo, Julio Saez-Rodriguez, and Julio R. Banga. 2017. “A Parallel Metaheuristic for Large Mixed-Integer Dynamic Optimization Problems, with Applications in Computational Biology.” PloS One 12 (8): e0182186. https://doi.org/10.1371/journal.pone.0182186.
- Terfve, Camille, Thomas Cokelaer, David Henriques, Aidan MacNamara, Emanuel Goncalves, Melody K. Morris, Martijn van Iersel, Douglas A. Lauffenburger, and Julio Saez-Rodriguez. 2012. “CellNOptR: A Flexible Toolkit to Train Protein Signaling Networks to Data Using Multiple Logic Formalisms.” BMC Systems Biology 6 (October): 133. https://doi.org/10.1186/1752-0509-6-133.
- Terfve, Camille D. A., Edmund H. Wilkes, Pedro Casado, Pedro R. Cutillas, and Julio Saez-Rodriguez. 2015. “Large-Scale Models of Signal Propagation in Human Cells Derived from Discovery Phosphoproteomic Data.” Nature Communications 6 (September): 8033. https://doi.org/10.1038/ncomms9033.
- Türei, Dénes, Tamás Korcsmáros, and Julio Saez-Rodriguez. 2016. “OmniPath: Guidelines and Gateway for Literature-Curated Signaling Pathway Resources.” Nature Methods 13 (12): 966–67. https://doi.org/10.1038/nmeth.4077.