Abstract
Summary
GeneEx is an interactive web-app that uses an ODE-based mathematical modeling approach to simulate, visualize and analyze gene regulatory circuits (GRCs) for an explicit kinetic parameter set or for a large ensemble of random parameter sets. GeneEx offers users the freedom to modify many aspects of the simulation such as the parameter ranges, the levels of gene expression noise and the GRC network topology itself. This degree of flexibility allows users to explore a variety of hypotheses by providing insight into the number and stability of attractors for a given GRC. Moreover, users have the option to upload, and subsequently compare, experimental gene expression data to simulated data generated from the analysis of a built or uploaded custom circuit. Finally, GeneEx offers a curated database that contains circuit motifs and known biological GRCs to facilitate further inquiry into these. Overall, GeneEx enables users to investigate the effects of parameter variation, stochasticity and/or topological changes on gene expression for GRCs using a systems-biology approach.
Availability and implementation
GeneEx is available at https://geneex.jax.org. This web-app is released under the MIT license and is free and open to all users and there is no mandatory login requirement.
Supplementary information
Supplementary data are available at Bioinformatics online.
1 Introduction
Gene regulatory circuits (GRCs) are networks of interacting genes that underpin nearly all biological processes. To deepen our understanding of the structure and function of GRCs, it has become imperative to develop computational methods and tools that can quickly and robustly model their dynamics (Alon, 2006). Here, we developed one such tool: Gene Circuit Explorer (GeneEx), an interactive web-app that uses an ODE-based mathematical modeling approach to simulate, visualize and analyze GRCs (Huang et al., 2017; Kohar and Lu, 2018). GeneEx allows for users to then quantitatively compare the simulated gene expression data with uploaded experimental data. GeneEx also hosts a database of circuit motifs and putative biological GRCs to enable further investigation of these topologies.
2 Gene Circuit Explorer
The minimum input to GeneEx is a circuit topology that specifies all the regulators (source), their regulated gene (target) and the type of regulation (activation/inhibition). GeneEx is highly customizable and allows users to (i) input, import and modify the circuit topologies (the Circuit tab); (ii) simulate gene expression trajectories for a model with a specific parameter set (the GeneVyuha tab); (iii) simulate gene expression patterns for an ensemble of models with randomly generated parameter sets (the RACIPE tab); (iv) compare the simulations with experimental data (the Validate tab); (v) explore circuit motifs or known biological circuits (the Database tab); (vi) ask questions or discuss any feature additions (the Forum tab).
2.1 Circuit
The Circuit tab allows the user to detail a circuit for analysis. A circuit can be either built manually or uploaded as a text file. The circuit is then displayed as both a network diagram and as a searchable and editable table (Fig. 1B). Furthermore, users can import a circuit from the GeneEx database as well.
Fig. 1.
GeneEx features. (A) GeneEx implementation architecture. Outline of GeneEx functionalities showing: (B) circuit visualization and modification, (C) time series simulations and parametric perturbations of a single model, (D) RACIPE simulations of the circuit
2.2 GeneVyuha
The GeneVyuha (Vyuha—a Sanskrit word that means ‘pattern/formation with a specific function’) tab simulates the circuit using a randomly generated parameter set and displays the time dynamics of the circuit (Fig. 1C). Users can also input their own set of model parameters obtained through statistical analysis of experimental data or through other parameter estimation methods. Users can investigate how individual parameters and/or the noise level affect the temporal evolution of gene expression by precisely controlling all the parameter values. Users may also vary any of the parameters uniformly over a range to generate the parameter perturbation diagrams of the circuit.
2.3 RACIPE
The RACIPE tab uses a recently developed mathematical modeling algorithm, named random circuit perturbation (RACIPE), to generate an ensemble of models with distinct random kinetic parameters (Huang et al., 2017). Our previous studies have shown that the gene expression from random models form robust and functionally related clusters (Huang et al., 2017, 2018; Katebi et al., 2020; Kohar and Lu, 2018; Ramirez et al., 2020). GeneEx provides a convenient platform for determining how different parameters and/or noise levels affect the prevalence and stability of various gene expression clusters that emerge from the RACIPE analysis.
2.4 Validate
The Validate tab allows users to quantitatively compare RACIPE-simulated gene expression data with their uploaded experimental data to investigate whether the GRC simulations capture the essential features of the experimental data.
2.5 Database
The Database tab offers curated circuit motifs and known biological GRCs to encourage additional exploration and sharing of their simulations. The database will be expanded over time and users have the option to upload their circuits.
3 Design and implementation
GeneEx is designed for both the systems biology and bioinformatics communities and contains modular design patterns for maximal flexibility (Fig. 1). The user interface of GeneEx is provided by shiny package (Chang et al., 2016) in R (R Core Team, 2018) and the backend consists of our R/Bioconductor (Huber et al., 2015) package sRACIPE (Kohar and Lu, 2018) which relies on various R packages, such as Rcpp (Eddelbuettel and François, 2011), data.table (Dowle et al., 2017), tidyverse (Wickham and Grolemund, 2016), visNetwork (Almende et al., 2016) and so on. We extend the commonly used S4 SummarizedExperiment class data structure from Bioconductor (Huber et al., 2015) to store the GRC, the parameters and the simulated gene expressions. The stochastic and ordinary differential equations are numerically integrated in C++ using the R bindings provided by Rcpp. Comprehensive documentation (see Supplementary Information), tutorial, workflow examples and discussion forum are available and hosted at https://geneex.jax.org and the code for app is available at https://github.com/lusystemsbio/geneex.
4 Conclusion
GeneEx is a user-friendly tool that enables the integration of systems-biology-based modeling methods with experimental data for a variety of research needs. A potentially popular application of GeneEx would be to help augment or refine established GRCs by comparing their simulated expression with experimental gene expression patterns.
Funding
The development of the web-app is supported by a startup fund from The Jackson Laboratory and by the National Institutes of Health under Award Number [P30CA034196] and [R35GM128717]. Work at the Center for Theoretical Biological Physics was sponsored by the National Science Foundation NSF Grant PHY-1427654.
Conflict of Interest: none declared.
Supplementary Material
Contributor Information
Vivek Kohar, The Jackson Laboratory, Bar Harbor, ME 04609, USA.
Danya Gordin, The Jackson Laboratory, Bar Harbor, ME 04609, USA; Center for Theoretical Biological Physics; Department of Bioengineering.
Ataur Katebi, The Jackson Laboratory, Bar Harbor, ME 04609, USA; Center for Theoretical Biological Physics; Department of Bioengineering.
Herbert Levine, Center for Theoretical Biological Physics; Department of Bioengineering; Department of Physics, Northeastern University, Boston, MA 02115, USA.
José N Onuchic, Center for Theoretical Biological Physics; Department of Physics and Astronomy; Department of Chemistry; Department of Biosciences, Rice University, Houston, TX 77005, USA.
Mingyang Lu, The Jackson Laboratory, Bar Harbor, ME 04609, USA; Center for Theoretical Biological Physics; Department of Bioengineering.
References
- Almende B.V. et al. (2016. version 2. http://datastorm-open.github.io/visNetwork/.
- Alon U. (2006) An Introduction to Systems Biology : Design Principles of Biological Circuits. Chapman and Hall/CRC, Boca Raton, FL. [Google Scholar]
- Chang W. et al. (2016) 0.13. 2. URL: http://CRAN. R-project. org/package= shiny.
- Dowle M. et al. (2017) Data.Table: Extension of Data. Frame. R package version, 1(6).
- Eddelbuettel D., François R. (2011) RCPP: seamless R and C++ integration. J. Stat. Softw., 40, 1–18. [Google Scholar]
- Huang B. et al. (2017) Interrogating the topological robustness of gene regulatory circuits by randomization. PLoS Comput. Biol., 13, e1005456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huang B. et al. (2018) RACIPE: a computational tool for modeling gene regulatory circuits using randomization. BMC Syst. Biol., 12, 74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huber W. et al. (2015) Orchestrating high-throughput genomic analysis with bioconductor. Nat. Methods, 12, 115–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Katebi A. et al. (2020) Random parametric perturbations of gene regulatory circuit uncover state transitions in cell cycle. iScience, 23, 101150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kohar V., Lu M. (2018) Role of noise and parametric variation in the dynamics of gene regulatory circuits. NPJ Syst. Biol. Appl., 4, 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ramirez D. et al. (2020) Toward modeling context-specific EMT regulatory networks using temporal single cell RNA-Seq data. Front. Mol. Biosci., 7, 54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- R Core Team. (2018) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Wickham H., Grolemund G. (2016) R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc., Sebastopol, CA.
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