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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O6 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O6. COMBINING TREE-BASED AND DYNAMICAL SYSTEMS FOR THE INFERENCE OF GENE REGULATORY NETWORKS Vân Anh Huynh-Thu 1* & Guido Sanguinetti 2,3 . GIGA-R & Department of Electrical Engineering and Computer Science, University of Liège 1 ; School of Informatics, University of Edinburgh 2 ; SynthSys – Systems and Synthetic Biology, University of Edinburgh 3 . * vahuynh@ulg.ac.be INTRODUCTION Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Current approaches can be broadly divided into model-based and model-free approaches, and face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability, and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system; these are clearly interpretable and can be used for predictions, however they rely on strong assumptions and are typically very demanding computationally. Here, we aim to bridge the gap between model-based and model-free methods by proposing a hybrid approach to the GRN inference problem, called Jump3 (Huynh-Thu & Sanguinetti, 2015). Our approach combines formal dynamical modelling with the efficiency of a nonparametric, tree-based method, allowing the reconstruction of GRNs of hundreds of genes. METHODS Gene expression model. At the heart of the Jump3 framework, we use the on/off model of gene expression (Ptashne & Gann, 2002), where the rate of transcription of a gene can vary between two levels depending on the activity state μ of the promoter of the gene. The expression x of a gene is modelled through the following stochastic differential equation: dx i = (A i μ i (t) + b i – λ i x i )dt + σdω(t), where subscript i refers to the i-th target gene. Here, the promoter state μ i (t) is a binary variable (the promoter is either active or inactive) that depends on the expression levels of the transcription factors (TFs) that bind to the promoter. A i , b i and λ i are kinetic parameters, and the term σdω(t) represents a white noise-driving process with variance σ 2 . Network reconstruction with jump trees. Recovering the regulatory links pointing to gene i amounts to finding the genes whose expression is predictive of the promoter state μ i . To achieve this goal, we propose a procedure that learns, for each target gene i, an ensemble of decision trees predicting the promoter state μ i at any time t from the expression levels of the candidate regulators at the same time t. However, standard tree-based methods cannot be applied here since the output μ i (t) is a latent variable. We therefore propose a new decision tree algorithm called “jump tree”, which splits the observations by maximising the marginal likelihood of the dynamical on/off model. The learned tree-based model is then used to derive an importance score for each candidate regulator, computed as the sum of the likelihood gains that are obtained at all the tree nodes where this regulator was selected to split the observations. The importance of a candidate regulator j is used as weight for the putative regulatory link of the network that is directed from gene j to gene i. RESULTS & DISCUSSION We evaluated Jump3 on the networks of the DREAM4 In Silico Network challenge (Prill et al., 2010). For each network topology, two types of simulated expression data were used: data simulated using the on/off model (toy data) and the time series data that was provided in the context of the DREAM4 challenge. We compared Jump3 to other GRN inference methods: two model-free methods, which are time-lagged variants of GENIE3 (Huynh-Thu et al., 2010) and CLR (Faith et al., 2007) respectively; two model-based methods, namely Inferelator (Greenfield et al., 2010) and TSNI (Bansal et al., 2006), and G1DBN (Lèbre, 2009), a method based on dynamic Bayesian networks. Areas Under the Precision-Recall curves (AUPRs) obtained for size-100 networks are shown in Table 1. Jump3 yields the highest AUPR in the case of the toy data. As expected, its performance decreases when the networks are inferred from the DREAM4 data, due to the mismatch between the on/off model and the one used to simulate the data. However, Jump3 still outperforms the other methods. Toy DREAM4 Jump3 0.272 ± 0.060 0.187 ± 0.058 GENIE3-lag 0.114 ± 0.010 0.176 ± 0.056 CLR-lag 0.088 ± 0.008 0.169 ± 0.047 Inferelator 0.069 ± 0.006 0.144 ± 0.036 TSNI 0.020 ± 0.003 0.042 ± 0.010 G1DBN 0.104 ± 0.024 0.114 ± 0.043 TABLE 1. Comparison of network inference methods (mean AUPR and standard deviation). We also applied Jump3 to gene expression data from murine bone marrow-derived macrophages treated with interferon gamma (Blanc et al., 2011). Several of the hub TFs in the predicted network have biologically relevant annotations. They include interferon genes, one gene associated with cytomegalovirus infection, and cancerassociated genes, showing the potential of Jump3 for biologically meaningful hypothesis generation. REFERENCES Bansal M et al. Bioinformatics 22, 815-822 (2006). Blanc M et al. PLoS Biol 9, e1000598 (2011). Faith JJ et al. PLoS Biol 5, e8 (2007). Greenfield A. PLoS ONE 5, e13397 (2010). Huynh-Thu VA & Sanguinetti G. Bioinformatics 31, 1614-1622 (2015). Huynh-Thu VA et al. PLoS ONE 5, e12776 (2010). Lèbre S. Stat Appl Genet Mol Biol 8, Article 9 (2009). Prill RJ et al. PLoS ONE 5, e9202 (2010). Ptashne M & Gann A. Genes and Signals. Cold Harbor Spring Laboratory Press (2002). 26
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O7 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O7. MODELING THE REGULATION OF Β-CATENIN SIGNALLING BY WNT STIMULATION AND GSK3 INHIBITION Annika Jacobsen 1 , Nika Heijmans 2 , Reneé van Amerongen 2 , Folkert Verkaar 3 , Martine J. Smit 3 , Jaap Heringa 1 & K. Anton Feenstra 1 *. 1 Centre for Integrative Bioinformatics (IBIVU), VU University Amsterdam, The Netherlands; 2 Van Leeuwenhoek Centre for Advanced Microscopy and Section of Molecular Cytology, Swammerdam Institute for Life Sciences, University of Amsterdam, The Netherlands; 3 Division of Medicinal Chemistry, VU University Amsterdam, The Netherlands. *k.a.feenstra@vu.nl The Wnt/β-catenin signaling pathway is crucial for stem cell self-renewal, proliferation and differentiation. Hyperactive Wnt/β-catenin signaling caused by genetic alterations plays an important role in oncogenesis. In our newly developed Petri net model, GSK3 inhibition leads to significantly higher pathway activation (high β-catenin levels) compared to WNT stimulation, which is confirmed by TCF/LEF luciferase reporter assays experimentally. Using this validated model we can now simulate changes in Wnt/β-catenin signaling resulting from different mutations found in breast and colorectal cancer. We propose that this model can be used further to investigate different players affecting Wnt/β-catenin signaling during oncogenic transformation and the effect of drug treatment. WNT/Β-CATENIN Wnt/β-catenin signaling is important for stem cell maintenance and developmental processes and is highly conserved in all multicellular organisms (1, 2). The pathway regulates the expression of specific target genes by changing the levels of the transcriptional co-activator, β-catenin which activates the TCF/LEF transcription factors. Wnt/β-catenin signaling is active in stem cells located in Wnt rich environments. APC and AXIN are key proteins of the destruction complex, which targets β-catenin for destruction. Mutations in APC, AXIN and β-catenin play important roles in oncogenesis (2, 3). To better understand its role in oncogenesis, we here create a Petri net (PN) model of the Wnt/β-catenin signaling pathway, that uses available coarse-grained data, such as binary interactions and semiquantitative protein levels. Using this model and validating experiments we show how different strengths of Wnt stimulation and GSK3 inhibition activate signaling over time. PETRI-NET MODELLING We built a PN model of Wnt/β-catenin signaling describing the logic of known (inter)actions, cf. our previous work (5). In a PN, a place represents an entity (e.g. gene), a transition indicates the activity occurring between the places (e.g. gene expression), and these are connected by directed edges called arcs that represent their interactions (e.g., activation of gene expression by a protein). TRANSCRIPTION AND PROTEIN ASSAYS TCF/LEF transcription was measure by TOPFLASH reporter activity at several time points and at different concentrations of Wnt3a stimulation and GSK3 inhibition by CHIR99021. Active and total β-catenin (CTNNB1) levels were measured by Western blot. VALIDATED ACTIVATION & INHIBITION We simulate the model with initial Wnt and GSK3 token levels ranging from 0 to 5 to represent addition of Wnt and inhibition of GSK3. Figure 1 shows the four different β- catenin responses for Wnt addition (purple) and GSK3 inhibition (green). At low GSK3 levels, β-catenin linearly increases, but at high GSK levels β-catenin remains low. At high Wnt levels, β-catenin shows a transient response, with the peak height increasing with Wnt levels. The increase of β-catenin is due to sequestration of AXIN to the cell membrane, which inactivates the destruction complex. Increase in β-catenin activates transcription of AXIN2 which triggers the negative feedback. FIGURE 1. Pathway response for different levels of Wnt and activity of GSK3. When adding Wnt, the pathway transiently activates but GSK3 inhibition permanently activates. TCF/LEF reporter assay validation experiments for both perturbations show that transcriptional activity of TCF/LEF is both dosage and time dependent, corresponding well for GKS3 inhibition. Wnt3a stimulation, on the other hand, does activate expression, but we do not observe the β-catenin dosage or time effect predicted by our model. Measuring β-catenin by Western blot reveals a consistent increase upon pathway activation, however protein levels and changes are on the border of experimental sensitivity. In conclusion, our Petri net model recapitulates much of the known behavior of the Wnt/β-catenin pathway upon Wnt stimulation and GSK3 inhibition, and hints at subtleties in the mechanism that will help us gain further understanding in the role of this pathway in development and oncogenesis. REFERENCES 1. Clevers & Nusse (2012) Cell. 149:1192-1205 2. Holstein (2012) Cold Spring Harb Perspect Biol. 4:a007922 3. MacDonald, Tamai & He (2009) Dev Cell. 17:9-26 4. Klaus & Birchmeier (2008) Nat. Rev. Cancer. 8:387-398 5. Bonzanni et al., (2009) Bioinformatics. 25:2049-2056 27
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