Volume 10 Supplement 4
Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation
© The Author(s) 2016
Published: 23 December 2016
Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways.
A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways.
Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments.
As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design.
One of the major forms of cellular responses to extracellular perturbations is to change the gene expression in response to the cellular signals transmitted by signaling pathways. Diverse stimuli can be converted into a series of intercellular reactions through signal transduction pathways which generate various transcription factor activities, thereby producing different gene expression patterns that result in subsequent cellular behaviors.
Over the past few decades, many studies have presented various computational strategies, such as data-driven, logic-based and biochemical kinetic methods, in modeling signaling pathways or gene regulatory networks separately. Data-driven methods [1–4], which are constructed mainly based on statistical modeling, show great potential when the underlying biological mechanisms are unclear. Logic-based models, such as Boolean Network [5–11] and generalized logic models [12–16] are suitable formalisms for modeling relatively large networks in which the detailed kinetic parameters are not fully available. If the underlying biochemical mechanisms are known, biochemical kinetic modeling [17–20] is a well-established strategy for describing the dynamic sub-cellular systems using a set of mathematical equations. In the field of gene regulation, thermodynamic models have also been successfully applied [21–23] besides the aforementioned methods.
Despite the many models of signaling pathways and gene regulatory network (GRN), it is still a big challenge to link the models of signal transduction with the downstream gene expression regulation. To address this challenge, Peng et al.  used a set of differential equations to do forward simulations of the NF-kB signaling pathway and then used Network Component Analysis [25–27], a data-driven method based on matrix decomposition, to reversely engineer a gene regulatory network (GRN). Then they matched the forward simulations and reverse engineering results and successfully linked the signaling profiles with the subsequent gene expression profiles. However, their method needs detailed kinetic parameters which may not be available as yet in many cases. A similar study of Melas et al.  first employed a multi linear regression algorithm to identify correlation-based relationships between signaling proteins and cellular responses (e.g., cytokine releases) and connected them using “non-canonical” edges. Integrating a canonical network of the signaling pathway from prior knowledge, the whole network was then converted into a Boolean model. Next, they optimized the network against the experimental data using Integer Linear Programming  and identified the pathway activities that induced the diverse cellular responses. Their reconstructed model is able to predict the dynamics of signaling pathways and cellular responses; however, because the biological meaning of the “non-canonical” edges learnt from the data is difficult to interpret, their model can hardly reveal the molecular mechanisms of how signal transduction regulates gene expression.
Here, we present Sig2GRN, a software tool which links the models of signaling pathway with gene regulatory networks (GRNs). A generalized logical model, which we proposed previously in  and is based on network topology, is employed here to capture the dynamical trends of transcription factors in cellular signaling pathways. Then two different models, i.e., a Boolean model and a thermodynamics-based model , are integrated to predict the downstream gene expression patterns based on the predicted transcription factor activities. As a Java plugin for Cytoscape  (version 2.8.3), Sig2GRN is able to simulate the dynamics of the signaling pathways and the subsequent time-series gene expression data. We first provide an overview of Sig2GRN’s core functionalities, and then describe two case studies to illustrate the usage and performance of Sig2GRN.
Methods and implementation
Generalized logical modeling of signaling pathways for predicting transcription factor activities
where A i (or B j ) represents the amount of signals transmitted from the i-th activating (or j-th blocking) parent node upstream of s, and d is the degradation rate (value ∈(0,1)) at each iteration. Using this model, we have successfully predicted the dynamics of a cancer signaling pathway under various perturbations . In this work, we select the simulated transcription factor activities (e.g., the proportion of the concentration of transcription factor in the active form) as the output of the upstream generalized logical mode and use them as the input to the downstream models to further predict the gene expressions as shown in Fig. 1.
Boolean modeling of transcriptional regulation
Once the time-series data of the transcription factor activities (value ∈[0,1] at each simulation iteration) are generated, users can select either a Boolean model  or a thermodynamic model  to predict the subsequent gene expression patterns.
Under the Boolean scenario, the AND logical relation is assigned to the transcription factors that have the same transcriptional regulation type (e.g., activation or inhibition) for a gene, so that the gene will be switched ON (or OFF) when the maximum activity level of activating (or inhibiting) transcription factors surpasses a user-defined threshold (value ∈(0,1)). When both activation and inhibition regulations are present on the same gene, the inhibition is assumed to precede the activation. The simulation result of the Boolean model is a list of 0s and 1s, over the course of time.
Thermodynamic modeling of transcriptional regulation
where [ E] is the gene expression level, N is the number of all possible arrangements of transcription factors attaching to their corresponding binding sites, G is the set of transcription factor arrangements that turn the gene on, n i (n m ) is the number of transcription factor binding sites employed in the i-th (m-th) arrangement, K j and [ TF j ] represent the binding affinity of binding site j and the activity level of the transcription factor corresponding to binding site j, and Q i is the probability of the gene being expressed when the i-th arrangement comprises the binding of both activating and inhibiting transcription factors (Q i =1 when only activating transcription factors are included).
To validate the simulation, we use a dataset in which human TK6 cells were treated with UV light and then the gene expression was measured at three time points, i.e., 4, 8 and 24 hrs . Figures 3 c and 3 f give the experimental data (the ratio of the gene expression levels between UV light treated and control groups) of Bax and Bcl-2 expression over the three time points. These two genes are the overlap between the network (Fig. 2) and the dataset , the dataset has measurements of many other genes which cannot be included in the network and the gene Apaf-1 in the network has no measurements in the dataset. It can be seen from the real data that the expression levels of both Bax and Bcl-2 increase over time when the cells are exposed to UV light; the slope of Bcl-2 curve is smoother and the height of the Bcl-2 curve is lower than that of the Bax curve. This suggests that, to some extent, our simulation tool is able to link the signal transduction with the gene expression regulation through transcription factors.
Case Study 2: apoptotic signaling network treated by different combinations of drugs. Predicting the efficacy of drugs and the design of combination therapy is a major endeavor for biomedical research and pharmaceutical industry. Lee et al.  studied the effects of different combinations of drugs in enhancing cell death in human breast cancer cells (cell line BT20). Here we construct a network based on their experiments and simulate the cell responses under different combinations of drug treatments to evaluate the performance of our simulator.
Input to the simulation in case study 2
DNA damage stimuli
In spite of the promising performance of our computational simulations, limitations have also been noticed. For example, in case study 2, the simulation did not reveal the synergistic effect of the co-treatment by two drugs. Possible reasons include the insufficient prior knowledge of the input networks and an oversimplification of the computational model of the nonlinear regulatory system. Moreover, since the simulation is iterated over discrete time points, it is hard to assign real time to simulation steps, which is a major obstacle for linking the two biological processes (e.g., signal transduction and transcriptional regulation) with different time scales. Techniques of multiscale modeling and simulation will be incorporated into the software in near future.
Computational simulation is an important systems biology approach to the analysis of signaling pathways and gene regulatory networks. In this work, we present a software tool called Sig2GRN which is able to link the cellular signaling pathways with the downstream gene expression regulation. A generalized logical model is used in modeling the upstream signaling pathways, while a Boolean Network and a thermodynamic model are employed in modeling the downstream gene expression based on the simulated activities of transcription factors. We have shown two case studies on simulating the cell responses to the extracellular perturbations and validated the simulations with wet-lab experimental data. As a Cytoscape plugin, Sig2GRN is designed to be extensible so that more computational models of gene regulation (e.g., epigenetic modifications) can be integrated to facilitates studies in systems biology. Compared with existing methods to link signaling pathways with gene regulation, such as in , Sig2GRN is a parameter-free software which requires no kinetic parameters of the pathways, and thus it is still applicable when only insufficient prior knowledge of the underlying mechanisms is available. Moreover, Sig2GRN is able to predict the gene expression time-course data given the perturbations to the signaling pathways, whereas in  the gene expression data are required as the input of their model, which is therefore unable to predict new gene expression patterns.
We would like to thank Ms. Jing Guo, a Ph.D. student at the School of Computer Science and Engineering, Nanyang Technological University, for her help with testing the software.
This article has been published as part of BMC Systems Biology Volume 10 Supplement 4, 2016: Proceedings of the 27th International Conference on Genome Informatics: systems biology. The full contents of the supplement are available online at http://bmcsystbiol.biomedcentral.com/articles/supplements/volume-10-supplement-4.
This project is supported by MOE AcRF Tier 2 Grant ARC39/13 (MOE2013-T2-1-079) and MOE AcRF Tier 1 seed grant on complexity (RGC2/13), Ministry of Education, Singapore.
The publication cost is supported by MOE AcRF Tier 2 Grant ARC39/13 (MOE2013-T2-1-079), Ministry of Education, Singapore.
Availability of supporting data
Software availability: http://histone.scse.ntu.edu.sg/Sig2GRN/.
FZ designed study; acquired, analysed and interpreted data; implemented main experiments; drafted manuscript. RSL implemented the software. JZ conceived the study, participated in conceptualization and discussion, critically reviewed and revised the manuscript and gave final approval for submission. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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