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 [1]. 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 [2,3].
This optimization problem can be solved sometimes using formal methods (specially when the models are binary, i.e. Boolean), such as Integer Linear Programming [4] or Answer Set Programming [5]. For continuous formalisms, we rely on heuristics implemented in the tool MEIGO [6] developed jointly with the group of Julio Banga. For large-scale systems, we can use parallel optimization methods such as SaceSS [7].
The aforementioned workflow is performed with our open source tool CellNOpt (CellNetOptimizer) [4]. CellNOpt uses different logic formalisms. These formalisms include Boolean, Fuzzy, Probabilistic, and Ordinary Differential Equation models which are trained against (phosphoproteomic) data. Applications range from cancer [8,9] to multiple sclerosis [10]. We are also exploring the application of logic modelling to single-cell signaling data.
We have also developed PHONEMeS [11], a related tool to build logic models from discovery mass-spectrometry based Phosphoproteomic data. We apply PHONEMeS to various contexts, in particular in cancer.
In addition, we use similar approaches to extract mechanistic insights from multi-omics data: we developed CARNIVAL [12] to train signaling networks from gene expression data using ILP to infer causal paths linking signaling drives with downstream transcripts’ levels. We have scaled CARNIVAL up to multiple types of omic data with COSMOS [13].
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.
References:
- Türei D, Korcsmáros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods. 2016;13: 966–967. doi:10.1038/nmeth.4077
- Eduati F, De Las Rivas J, Di Camillo B, Toffolo G, Saez-Rodriguez J. Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics. 2012;28: 2311–2317. doi:10.1093/bioinformatics/bts363
- Gjerga E, Trairatphisan P, Gabor A, Saez-Rodriguez J. Literature and data-driven based inference of signalling interactions using time-course data. IFAC-PapersOnLine. 2019. pp. 52–57. doi:10.1016/j.ifacol.2019.12.235
- Gjerga E, Trairatphisan P, Gabor A, Koch H, Chevalier C, Ceccarelli F, et al. Converting networks to predictive logic models from perturbation signalling data with CellNOpt. doi:10.1101/2020.03.04.976852
- Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, et al. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. Bioinformatics. 2013;29: 2320–2326. doi:10.1093/bioinformatics/btt393
- Egea JA, Henriques D, Cokelaer T, Villaverde AF, MacNamara A, Danciu D-P, et al. MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics. 2014;15: 136. doi:10.1186/1471-2105-15-136
- Penas DR, Henriques D, González P, Doallo R, Saez-Rodriguez J, Banga JR. A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology. PLoS One. 2017;12: e0182186. doi:10.1371/journal.pone.0182186
- Eduati F, Doldàn-Martelli V, Klinger B, Cokelaer T, Sieber A, Kogera F, et al. Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type–Specific Dynamic Logic Models. Cancer Research. 2017. pp. 3364–3375. doi:10.1158/0008-5472.can-17-0078
- Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, et al. Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Mol Syst Biol. 2020;16: e8664. doi:10.15252/msb.20188664
- Bernardo-Faura M, Rinas M, Wirbel J, Pertsovskaya I, Pliaka V, Messinis DE, et al. Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis. doi:10.1101/541458
- Terfve CDA, Wilkes EH, Casado P, Cutillas PR, Saez-Rodriguez J. Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nat Commun. 2015;6: 8033. doi:10.1038/ncomms9033
- Liu A, Trairatphisan P, Gjerga E, Didangelos A, Barratt J, Saez-Rodriguez J. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. doi:10.1101/541888
- Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, et al. Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses. Molecular Systems Biology. 2021. doi:10.15252/msb.20209730