Southern California Conference for Undergraduate Research (SCCUR) 2025, California State University Channel Islands, Camarillo, CA
November 22, 2025

GRNsight v7: a web application for visualizing gene regulatory networks and protein-protein interactions
Ngoc K. Tran, Milka Zekarias, Cecilia J. Zaragoza, A'Kaia Phelps, Amelie Dinh, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application for visualizing models of gene regulatory networks (GRNs) and protein-protein physical interaction networks (PPIs) either uploaded by the user or obtained from our backend yeast database. Models are displayed as graphs where genes/proteins are rectangular nodes with edges between them. GRNs have directed edges to indicate regulatory relationships (activation or repression), and PPIs have undirected edges to indicate physical binding. For v7, we improved our PPI support, transitioned our database loading scripts from YeastMine to AllianceMine, enhanced viewport functionality, and migrated continuous integration (CI) to GitHub Actions. As was previously implemented for GRNs, users can now color nodes in PPIs with expression data, either provided by the user or from our backend database. Different datasets can be displayed on the top and bottom of the nodes. For database loading, our scripts were ported to AllianceMine because YeastMine was discontinued. New graph functionality has been implemented that allows users to restrict the graph to viewport so that they can drag, zoom, and move the graph with the nodes staying within the application’s bounding box. Finally, the project’s CI pipeline has been migrated from Travis CI to GitHub Actions, integrating workflows for automated builds, unit tests, and linting to streamline development processes and improve maintainability. Ongoing efforts prioritize bug resolution, user interface enhancements, and improved documentation. GRNsight is now positioned to comprehensively address GRNs and PPIs, offering a unified platform for visualizing diverse molecular interaction models. GRNsight is available at https://dondi.github.io/GRNsight/.

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Pomona College Department of Biology Seminar, Claremont, California
September 26, 2025

From the lac operon to the spaghettiome, what can we learn from modeling gene regulatory networks on a medium scale?
Kam D. Dahlquist

Southern California Systems Biology Conference, UC Irvine
March 16, 2024

Dynamical systems modeling and visualization of yeast cold shock gene regulatory networks
Kam D. Dahlquist

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Joint Bioinformatics Community Conference and Bioinformatics Open Source Conference (virtual)
July 19-21, 2020

New features for GRNsight: a web application for visualizing models of small- to medium-scale gene regulatory networks
Kam D. Dahlquist, John David N. Dionisio, Mihir Samdarshi, Nicole A. Anguiano, Anindita Varshneya, Eileen J. Choe, Yeon-Soo Shin, Alexia M. Filler, John L. Lopez, Edward B. Bachoura, Justin Kyle T. Torres, Kevin B. Patterson, Ona O. Igbinedion

Abstract: GRNsight is an open source web application for visualizing small- to medium-scale models of gene regulatory networks (GRNs; Dahlquist et al. 2016, https://doi.org/10.7717/peerj-cs.85). It was originally conceived of as a companion application to GRNmap (Gene Regulatory Network Modeling And Parameter estimation), an open source MATLAB software package that uses ordinary differential equations to model the dynamics of medium-scale GRNs (http://kdahlquist.github.io/GRNmap/). GRNmap uses a penalized least squares approach to estimate mRNA production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on user-provided gene expression data and mRNA degradation rates. GRNsight accepts GRNmap- or user-generated Excel workbooks containing an adjacency matrix representation of the GRN, as well as SIF and GraphML files, and automatically lays out the graph of the GRN model. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter.

Since the last presentation of GRNsight at BOSC in 2016, the code has been refactored to the model-view-controller paradigm, a number of new features have been developed to enhance data visualization, and the user interface has been redesigned to accommodate the new feature set. A grid layout for nodes has been added. The viewport is now independent of the underlying bounding box of the graph, and users can resize the viewport, move the graph within it, and zoom in and out. The display of edge weight numerical values can be toggled on and off, and the user can manually set the edge weight normalization factor and gray color threshold to facilitate comparison between graphs. A color blind-friendly palette has been adopted. Nodes can now be colored, displaying a mini “heat map” where each vertical stripe represents a timepoint in the user-inputted expression data. In beta, a backend PostgreSQL database containing four public yeast datasets allows users to color nodes without having to upload their own data. The database is hosted by the Amazon Web Services Relational Database Service from which GRNsight performs dynamic queries in response to user requests. We also created pop-up gene information webpages. When a user right-clicks on a node, a webpage appears that is dynamically populated with data from the JASPAR, NCBI Gene, UniProt, Ensembl, and Saccharomyces Genome Databases. Initially implemented for budding yeast, five other species are available in beta. GRNsight is developed by undergraduates with faculty oversight. The latter two features were prototyped as part of an open source/open science pedagogy in a Biological Databases course and completed as senior capstone projects (e.g., see https://xmlpipedb.cs.lmu.edu/biodb/fall2017/index.php/Main_Page).

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Loyola Marymount University CMSI 402 Presentation
May 1, 2020

An Expression Database for Node Coloring and Expansion of Species for Gene Pages for GRNsight
Alexia Filler, Kevin Patterson

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12^th Annual Loyola Marymount University Undergraduate Research Conference (virtual)
April 6, 2020

An Expression Database for Node Coloring and Expansion of Species for Gene Pages for GRNsight
Alexia M. Filler, Kevin B. Patterson, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model GRNs. Using a GRNmap output workbook, GRNsight automatically visualizes the network as a graph. GRNsight uses the model output to color-code the edges based on the activation and repression relationships between the transcription factors and colors the nodes based on time course gene expression data. We have added a feature to store expression data in a database, so that users of GRNsight can view and interact with already-existing expression data pulled from the Dahlquist lab and other publicly available datasets. The database is hosted by Amazon Web Services (AWS) Relational Database Service (RDS) and uses a PostgreSQL engine, from which GRNsight performs dynamic queries in response to user requests. Additionally, we have expanded the availability of gene-specific data to a greater number of species. The feature allows user to specify the species of interest either based on a GRNmap workbook or direct user input, and save it for the remainder of the user's session. With the species indicated, the gene page can query the appropriate databases and return information relevant only to species-specific genes. GRNsight is available at https://dondi.github.io/GRNsight/

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11^th Annual Loyola Marymount University Undergraduate Research Conference
March 23, 2019

New Layouts, Data Types, and Architecture for GRNsight 3: a Web Application for Visualizing Gene Regulatory Networks
Mihir Samdarshi, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight accepts input files in the Excel, SIF, and GraphML formats and then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges. Activation is represented with red edges, repression is represented with blue edges, and weight magnitudes below a certain threshold are represented with gray edges to denote weak activation or repression relationships. GRNsight now contains features to facilitate network comparison. Nodes on each graph show a heat map corresponding to gene expression levels over time; these nodes can be overlaid with up to two different data sets. The graphs generated by GRNsight may now be automatically laid out in a grid. When a user right-clicks on a gene, data requests are made to five public databases and a web page is dynamically populated with the data returned. Finally, a number of under the hood improvements were made to GRNsight’s architecture. Most notably, GRNsight was made to follow a strict Model-View-Controller (MVC) paradigm. MVC consolidates the application’s state into a single object, from which presentation and interaction are derived. GRNsight version 3.1.3 is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.

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Department of Biology, Loyola Marymount University, Los Angeles, California
September 14, 2018

From the lac operon to the spaghettiome, what can we learn from modeling gene regulatory networks on a medium scale?
Kam D. Dahlquist

10^th Annual Loyola Marymount University Undergraduate Research Conference
March 24, 2018

Extending the Visualization Capabilities of GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Eileen J. Choe, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which control expression of mRNA and protein from genes. GRNsight produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges, with colors, end markers, and thicknesses corresponding to the sign and magnitude of activation or repression. GRNsight is designed to accept Excel workbooks from GRNmap, a MATLAB GRN modeling program, and lay out the network graph specified by an adjacency matrix contained within one of the worksheets. However, the GRNmap output contains additional worksheets which store data about the GRN and results of the GRNmap simulations that have not been displayed or visualized by GRNsight. To address this, we have redesigned the Excel spreadsheet importer to parse and store data from the entire GRNmap output workbook in a standardized JavaScript Object Notation (JSON) data structure. The complete data extraction from the GRNsight output includes experimental and simulated temporal expression data for each gene in the network. We have developed a coloring framework to visualize this expression data in the form of a stacked heat map overlay of the nodes which compares the experimental and simulated gene expression levels. Node coloring allows GRNsight users to better derive insights from the data that model these networks. The GRNsight application http://dondi.github.io/GRNsight/ and code https://github.com/dondi/GRNsight are available under the open source BSD license.

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New Graph Layouts for GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Yeon-Soo Shin, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight imports data from an adjacency matrix in either an Excel workbook, SIF, or GraphML file, then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges, with colors, end markers, and thicknesses corresponding to the sign and magnitude of activation or repression. Visual issues regarding edge thickness and end markers have been adjusted and test files created. The new GRNsight graph now displays self-regulatory arrows with consistent positions regardless of the node size, edge thickness, or whether it is weighted. The GRNsight layout depends on the force graph engine from D3. We updated GRNsight from D3 version 3 to version 4, allowing the developers to utilize new D3 functionalities. We have implemented block layout where the nodes align into a grid alphabetically by node label. This graph layout feature will allow the users to organize and align the nodes with a simple button interface instead of manually dragging them as before. This will facilitate easier side-by-side comparison of the data from different GRN models. Alternatively, we plan to explore a hierarchical layout option as well. GRNsight is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.

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Quantitative and Computational Biosciences Seminar, UCLA
March 3, 2018

Dynamical Systems Modeling and Visualization of Gene Regulatory Networks: What Can We Learn from Networks on the “Medium” Scale?
Kam D. Dahlquist

Frank R. Seaver College Professorial Lecture, Loyola Marymount University, Los Angeles, California
February 23, 2018

The Process is the Product: Systems Biology within an Open Science Ecosystem
Kam D. Dahlquist

Southern California Conference for Undergraduate Research, California State Polytechnic University, Pomona
November 2017

GRNsight v2: a Web Application for Visualizing Models of Gene Regulatory Networks
Eileen J. Choe, Yeon-Soo Shin, Edward B. Bachoura, John David N. Dionisio, Kam D Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges, with colors, end markers, and thicknesses corresponding to the sign and magnitude of activation or repression. After GRNsight v1’s release, feedback from users motivated improvements and new features. In GRNsight v2, the graph bounding box is separated from the viewport and expands as far as required for the graph to converge to a steady state, allowing the force graph parameters to be fully applied. The ability to zoom and scroll the graph within the viewport was also added. New analysis features include options for showing edge weight values, customizing the edge thickness normalization factor, and setting the threshold for coloring the edges gray. The addition of features in GRNsight v2 increased the number of possible combinations of functions a user can perform on GRNsight. Thus, we developed an automatic client-side test generator which produces a systematic testing manual. On the server side, syntactic tests for malformed Simple Interaction Format (.sif) files were written, which report end user-targeted errors if an incorrectly formatted SIF file is imported into GRNsight. Future work includes developing a framework to visualize the gene expression over time through coloring the nodes in the network graph. The node coloring feature coupled with the visual and analysis features in GRNsight v2 will provide a more complete visual representation of the GRN, allowing GRNsight users to better derive insights from them. The GRNsight application http://dondi.github.io/GRNsight/ and code https://github.com/dondi/GRNsight are available under the open source BSD license.

WE17: Society for Women Engineers (SWE) Collegiate Competition, Austin, Texas
October 28, 2017

GRNsight v2: a Web Application for Visualizing Models of Small Gene Regulatory Networks
Eileen J. Choe, Nicole A. Anguiano, Anindita Varshneya, Mihir Samdarshi, Yeon-Soo Shin, Edward B. Bachoura, John David N. Dionisio, and Kam D. Dahlquist

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Loyola Marymount University CMSI 402 Presentation
May 5, 2017

GRNsight
Nicole A. Anguiano

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9^th Annual Loyola Marymount University Undergraduate Research Conference
March 25, 2017

Design and Layout Improvement to GRNsight v2.0: a Web Application and Service for Visualizing Small- to Medium-Scale Gene Regulatory Networks
Anindita Varshneya, Nicole A. Anguiano, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing models of small- to medium-scale gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight automatically lays out GRN data contained in either an unweighted or weighted network graph based on an Excel workbook containing an adjacency matrix, a Simple Interaction Format (SIF) text file, or a GraphML (XML) file. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign and magnitude of an edge weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. New visual features include the ability to show and hide edge weights, resize the bounding box, zoom into the visualization, and change graph layouts. New analysis features include the ability to adjust the normalization factor of the graph so that the thicknesses of the edges will be on the same scale for two different graphs, facilitating comparison, and the computation of graph statistics, such as betweenness centrality for nodes. Users can access these new features via easily accessible menu and sidebar options. GRNsight follows open and test-driven development best practices, where a testing framework consisting of over 160 automated unit tests ensures that the program is running as expected. GRNsight is freely available at http://dondi.github.io/GRNsight/; code is available under the open source BSD license at https://github.com/dondi/GRNsight.

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National Center for Ecological Analysis and Synthesis, Santa Barbara, California
March 8, 2017

GRNmap and GRNsight: Open Source Software for Dynamical Systems Modeling and Visualization of Medium-Scale Gene Regulatory Networks
Kam D. Dahlquist

Loyola Marymount University Department of Biology Seminar Series
September 16, 2016

GRNmap and GRNsight: Using the power of genomics, mathematics,and open source visualization software to understand gene regulatory networks in yeast
Kam D. Dahlquist

Bioinformatics Open Source Conference (BOSC) 2016, Orlando, FL
July 9, 2016

GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Nicole A. Anguiano, Juan S. Carrillo, Trixie Anne M. Roque, Anindita Varshneya, Mihir Samdarshi, and Chukwuemeka Azinge

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Over a period of several years, our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. The large number of developers and time span of development led to a code base that was difficult to revise and adjust. We therefore brought the code under version control in a GitHub repository and refactored the script-based software with global variables into a function-based package that uses an object to carry relevant information from function to function. This modular approach allows for cleaner, less ambiguous code and increased maintainability. We standardized the format of the input and output Excel workbooks, making them more readable. We also added an optimization diagnostics output worksheet which includes both the actual and theoretical minimum least squared error overall, and the mean squared errors for the individual genes. The MATLAB compiler was used to create an executable that can be run on any Windows machine without the need of a MATLAB license, increasing the accessibility of our program. Finally, we have implemented test-driven development, creating unit tests for all new features to speed up debugging and to prevent future code regressions. We are improving the test coverage of previous code.

GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. From the early stages, GRNsight has had a unit testing framework using Mocha and the Chai assertion library to perform test-driven development where unit tests are written before new functionality is coded. This framework consists of over 160 automated unit tests that examine over 450 test files to ensure that the program is running as expected. Error and warning messages inform the user what happened, the source of the problem, and possible solutions.

Together, the life cycle of these two programs illustrate the differences between the cultures of mathematics and computing, the challenges and benefits of bringing an existing code base up to open development standards (GRNmap), and the advantages of starting a project using best practices from the beginning (GRNsight). Our goal is to facilitate reproducible research.

Click here to view the slides on F1000Research.

Experimental Biology 2016, San Diego, CA
April 4, 2016

GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Nicole A. Anguiano, Juan S. Carrillo, Monica V. Hong, Kristen M. Horstmann, Kayla C. Jackson, K. Grace Johnson, Tessa A. Morris, Trixie Anne M. Roque, Mihir Samdarshi, Anindita Varshneya, Natalie E. Williams, and Kevin W. Wyllie

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and five strains individually deleted for the transcription factors, Cin5, Gln3, Hap4, Hmo1, Zap1, of budding yeast, Saccharomyces cerevisiae BY4741. Model predictions fit the experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight open source code and application are available from http://dondi.github.io/GRNsight/index.html.

5^th Annual Southern California Systems Biology Conference
January 31, 2015

GRNmap and GRNsight: Open Source Software for Dynamical Systems Modeling and Visualization of Medium-Scale Gene Regulatory Networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Nicole A. Anguiano, Juan S. Carrillo, Nicholas A. Rohacz, Katrina Sherbina, Britain J. Southwick, and Anindita Varshneya

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. Parameters for a 21-gene network were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and four transcription factor deletion strains of budding yeast, Saccharomyces cerevisiae. Model predictions fit experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight code and application are available from http://dondi.github.io/GRNsight/index.html. This work was partially supported by NSF award 0921038.

Loyola Marymount University CMSI 402 Presentation
May 8, 2014

GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Britain Southwick

Loyola Marymount University 6^th Annual Undergraduate Research Symposium
March 29, 2014

GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Britain Southwick, Nicole Anguiano, John David N. Dionisio, Kam D. Dahlquist, Ben G. Fitzpatrick

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them, which govern the level of expression of mRNA and protein from those genes. GRNs can be mathematically modeled and simulated by applications such as GRNmap, a MATLAB program that estimates the parameters and performs forward simulations of a differential equations model of a GRN. Computer representations of GRNs, such as the models output by GRNmap, are tabular spreadsheets that are not easily interpretable. Ideally, GRNs should be displayed as diagrams (graphs) detailing the regulatory relationships (edges) between each gene (node) in the network. To address this need, we developed GRNsight. GRNsight is an open source web application for visualizing mathematical models of GRNs. It allows users to upload spreadsheets generated by GRNmap and uses the information in these spreadsheets to automatically create and display a graph of the GRN model. The application colors the edges and adjusts their thickness based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Finally, GRNsight then allows the user to modify the graph in order to define the best visual layout for the network. Most of GRNsight is written in JavaScript. HTTP requests are handled using Node.js and the Express framework. Graphs are generated through D3.js, a JavaScript data visualization library.

Institute for Quantitative and Computational Biology Retreat, University of Californina, Los Angeles
September 22, 2025

Sensitivity analysis of GRNmap and new features for GRNsight: open source software for dynamical systems modeling and visualization of small-scale gene regulatory networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Ngoc K. Tran, Nikki C. Chun, Cecilia J. Zaragoza, A'Kaia L. Phelps, Amelie T. Dinh, Milka Y. Zekarias

Abstract
A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. GRNmap is an open source MATLAB package that uses ordinary differential equations to model dynamics of small-scale GRNs. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data and then performs forward simulations of model dynamics. While the model has been successfully used to understand networks of 15-20 genes, we wanted to closely examine how it works on a smaller scale to determine parameter sensitivity. All 21 possible 'toy' networks of 3 nodes and 4 edges were created based on the simulated expression data outputted when known parameters were run through a forward simulation. Then the simulated data was used to estimate the parameters again. Comparison of the known to estimated parameters showed that estimating production rates in addition to weights and thresholds reduced the accuracy of the results. The model was also sensitive to the direction and magnitude of the weight parameters for networks with the same connectivity. GRNsight is an open-source web application for visualizing such models of GRNs, automatically displaying a graph where edges are color-coded based on the activation and repression relationships, and nodes are color-coded with time course gene expression data. Backend gene expression and gene regulatory network databases allow users to generate a network, color the notes with expression data, and export an Excel workbook for modeling in GRNmap.

Click here to view the poster (also presented at IMSB 2025 below).

ISMB/ECCB 2025, Liverpool, England
July 20-25, 2025

Sensitivity analysis of GRNmap and new features for GRNsight: open source software for dynamical systems modeling and visualization of small-scale gene regulatory networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Ngoc K. Tran, Nikki C. Chun, Cecilia J. Zaragoza, A'Kaia L. Phelps, Amelie T. Dinh, Milka Y. Zekarias

Abstract
A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. GRNmap is an open source MATLAB package that uses ordinary differential equations to model dynamics of small-scale GRNs. The program estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data and then performs forward simulations of model dynamics. While the model has been successfully used to understand networks of 15-20 genes, we wanted to closely examine how it works on a smaller scale to determine parameter sensitivity. All 21 possible 'toy' networks of 3 nodes and 4 edges were created based on the simulated expression data outputted when known parameters were run through a forward simulation. Then the simulated data was used to estimate the parameters again. Comparison of the known to estimated parameters showed that estimating production rates in addition to weights and thresholds reduced the accuracy of the results. The model was also sensitive to the direction and magnitude of the weight parameters for networks with the same connectivity. GRNsight is an open-source web application for visualizing such models of GRNs, automatically displaying a graph where edges are color-coded based on the activation and repression relationships, and nodes are color-coded with time course gene expression data. Backend gene expression and gene regulatory network databases allow users to generate a network, color the notes with expression data, and export an Excel workbook for modeling in GRNmap.

Click here to view the poster and here to download the video.

Loyola Marymount University 17^th Annual Undergraduate Research Symposium
March 21, 2025

Database, Display, and DevOps Improvements for GRNsight 7.2, a Web Application for Visualizing Gene Regulatory and Protein-Protein Interaction Network Models
Ngoc K. Tran, Cecilia J. Zaragoza, A'Kaia Phelps, Amelie Dinh, Milka Zekarias, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them, which govern the expression level of mRNA and protein from genes. GRNsight can now also display protein-protein physical interaction (PPI) networks. Whereas GRNs have directed edges, PPI networks have undirected edges to indicate protein binding. We have continued to integrate PPI networks so that users can display node coloring on a PPI with a choice of top or bottom datasets of expression data. GRNsight reads user-uploaded Microsoft Excel workbooks and automatically displays a graph. Users without their own data can use GRNsight's back-end PostgreSQL database to select a GRN or PPI based on yeast data from AllianceMine. Database loading scripts were ported to AllianceMine because YeastMine was discontinued. New graph functionality has been implemented that allows users to restrict the graph to viewport so that they can drag, zoom, and move the graph with the nodes staying within the application’s bounding box. On the DevOps side, the project’s continuous integration/development pipeline has been migrated from Travis CI to GitHub Actions, integrating workflows for automated builds, unit tests, and linting to streamline development processes and improve maintainability. Ongoing efforts prioritize bug resolution, user interface enhancements, and improved documentation. GRNsight is now positioned to comprehensively address GRNs and PPIs, offering a unified platform for visualizing diverse molecular interaction models. GRNsight is available at http://dondi.github.io/GRNsight/.

Click here to view the poster.

Loyola Marymount University 16^th Annual Undergraduate Research Symposium
March 15, 2024

Propagating Protein-Protein Interaction Network Support into GRNsight 7.0, a Web Application for Visualizing Gene Regulatory Network Models
Ngoc K. Tran, Cecilia J. Zaragoza, A'Kaia Phelps, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them, which govern the expression level of mRNA and protein from genes. GRNsight reads user-uploaded Microsoft Excel adjacency matrices or SIF files and automatically displays a graph. Users without their own data can use GRNsight's back-end PostgreSQL database to select a GRN based on the Saccharomyces Genome Database (SGD). Besides GRN data, we have recently incorporated protein-protein physical interaction (PPI) data from SGD so that users can also visualize this type of network. Due to this new type of network, the rest of the application needed updates in order to fully support PPIs. For example, PPI networks have undirected edges vs. directed edges for GRNs. Gene and protein labels also differ. Notable additions include a PPI demo graph, allowing users to visualize a graph automatically without inputting specific data. This new demo exclusively showcases interactions involving yeast mitochondrial protein Aim32p, offering users a detailed exploration of its network connections. Furthermore, GRNsight now incorporates functionality to detect the network type of the imported file, distinguishing between GRNs and PPIs. Ongoing development efforts prioritize bug resolution, user interface enhancements, and improved documentation. In response to user needs, GRNsight is now positioned to comprehensively address GRNs and PPIs, offering a unified platform for visualizing diverse molecular interaction models. GRNsight is available at http://dondi.github.io/GRNsight/.

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The Allied Genetics Conference (TAGC24), Metro Washington DC
March 6-10, 2024

Extending the GRNsight application for visualizing small-to-medium gene regulatory networks to incorporate physical protein-protein interaction data from SGD
Kam D. Dahlquist, Onariaginosa O. Igbinedion, Ahmad R. Mersaghian, Ngan N. Tran, Deepa V. Dabir, John David N. Dionisio

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is an open source MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on time course gene expression data. GRNsight is an open source web application for visualizing small-to-medium scale GRNs, especially models produced by GRNmap. GRNsight reads the Excel input and output workbooks from GRNmap and automatically displays the model data as a graph with colored nodes (expression data) and edges (estimated regulatory weights). We recently implemented a backend PostgreSQL database for GRNsight, populated with five public gene expression datasets and regulatory network data from the Saccharomyces Genome Database (SGD). A user can select genes to include in the GRN, and GRNsight will automatically layout the network using regulatory connections from SGD. The user can then select one of the time course gene expression datasets with which to color the nodes and export to a properly-formatted GRNmap input workbook. GRNmap can then be used to estimate the regulatory weights (activation vs. repression and magnitude of the relationship). Although GRNsight was originally designed for GRNs, it has the general applicability for displaying any appropriately-sized, weighted or unweighted network with directed edges. Previously, GRNsight was limited to only displaying graphs with directed edges and was unable to display protein-protein physical interaction (PPI) networks, as they are represented by undirected graphs. Thus a protein-protein physical interaction network mode was created to visualize these models in GRNsight. Additionally, a backend database was added to store PPI data obtained from SGD, so that users can generate a PPI network within GRNsight the same way they can generate a GRN. The graphical user interface was updated to be able to toggle between these two graph modes, enabling comparison of these two types of data. GRNmap is available at http://kdahlquist.github.io/GRNmap/; GRNsight is available at http://dondi.github.io/GRNsight.

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Loyola Marymount University 15^th Annual Undergraduate Research Symposium
March 17, 2023

Improved Functionality of GRNsight 6.0: a Web Application for Visualizing Gene Regulatory Network Models
Ahmad R. Mersaghian, Sarron A. Tadesse, Ngan N. Tran, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on user-provided expression data. GRNsight reads Microsoft Excel input and output workbooks from GRNmap and automatically displays the model data as a graph. Graph edges are color-coded based on the activation and repression relationships between the transcription factors, and nodes are color-coded with time course gene expression data. The addition of backend gene expression and gene regulatory network databases allows users with no data of their own to generate a network, color the notes with expression data, and export an Excel workbook for modeling in GRNmap. Network data was obtained from the Saccharomyces Genome Database via the Yeastmine search and retrieval interface. A new gene expression dataset from Apweiler et al. (2012) was integrated into the GRNsight database and user interface. Developer documentation for the databases was improved, and the Application Programming Interface (API) and Data Access Layer (DAL) modules were refactored and expanded to improve separation of concerns. In parallel, the ongoing development of GRNsight is focused on fixing bugs and adding enhancements to the user interface and graph layout. GRNsight is freely available at http://dondi.github.io/GRNsight/.

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Yeast Genetics Meeting, University of California, Los Angeles
August 17–21, 2022

Integration of SGD regulatory and expression data into the GRNmap and GRNsight applications for modeling and visualizing small-to-medium gene regulatory networks
Kam D. Dahlquist, Onariaginosa O. Igbinedion, Ahmad R. Mersaghian, Sarron A. Tadesse, John David N. Dionisio

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is an open source MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on time course gene expression data. GRNsight is an open source web application for visualizing small-to-medium scale GRNs, especially models produced by GRNmap. GRNsight reads the Excel input and output workbooks from GRNmap and automatically displays the model data as a graph with colored nodes (expression data) and edges (estimated regulatory weights). A limitation for GRNmap has been the manual creation of input Excel workbooks which is time-consuming, error-prone, and dependent upon the user having their own network and expression data. To address this, we have implemented a backend PostgreSQL database for GRNsight, populated with five public gene expression datasets (Apweiler et al. 2012, GSE33098; Barreto et al. 2012, GSE24712; Dahlquist et al. 2018, GSE83656; Kitagawa et al. 2002, GSE9336; Thorsen et al. 2007, GSE6068) and regulatory network data from the Saccharomyces Genome Database (SGD). A user can now select genes to include in the GRN, and GRNsight will automatically layout the network using regulatory connections from SGD. The user can then select one of the time course gene expression datasets with which to color the nodes and export to a properly-formatted GRNmap input workbook. GRNmap can then be used to estimate the regulatory weights (activation vs. repression and magnitude of the relationship). A GRNmap executable is available for users who do not have access to a MATLAB license. Finally, the GRNmap output can be loaded back into GRNsight to visualize the results. Closing this loop for automating and validating the creation of GRNmap input workbooks speeds up the rate of research, enabling the comparison of models of different GRNs with the same expression data source or the same GRN with different expression data sources. GRNmap is available at http://kdahlquist.github.io/GRNmap/; GRNsight is available at http://dondi.github.io/GRNsight.

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West Coast Biological Sciences Undergraduate Research Conference
April 9, 2022

A New Gene Expression Dataset for GRNsight: a Web Application for Visualizing Gene Network Models
Ahmad R. Mersaghian, Sarron A. Tadesse, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on user-provided expression data. GRNsight reads Microsoft Excel input and output workbooks from GRNmap and automatically displays the model data as a graph. Graph edges are color-coded based on the activation and repression relationships between the transcription factors, and nodes are color-coded with time course gene expression data provided by the user or retrieved from the backend database. To expand the potential research scope, a new expression dataset was chosen to be added to the GRNsight database. Apweiler et al. (2012) measured gene expression changes over time in Saccharomyces cerevisiae due to adding glucose to the media. To clean this dataset for inclusion in the database, we needed to remove duplicate data and assign the correct ID types. To further analyze the data with GRNmap, we clustered with STEM to identify significant expression profiles and inferred the gene regulatory network with YEASTRACT. After running GRNmap, we visualized the results of the model using GRNsight. In parallel, ongoing development of GRNsight is focused on fixing bugs and adding enhancements to the user interface and graph layout. GRNsight is freely available at http://dondi.github.io/GRNsight.

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Loyola Marymount University 14^th Annual Undergraduate Research Symposium
March 18, 2022

New Databases and Export Features for GRNsight: a Web Application for Visualizing Models of GRNs
Onariaginosa O. Igbinedion, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on user-provided expression data. A limitation for GRNmap is the manual creation of input Excel workbooks which is time-consuming and can be error-prone. GRNsight reads these input and output workbooks from GRNmap and automatically displays the model data as a graph with colored nodes and edges. We have updated the existing back-end GRNsight database to be compatible with other species in addition to Saccharomyces cerevisiae. We cleaned up the database schema and added appropriate types to the data so that proper sorting is achieved when retrieving expression data. We expanded the user interface for GRNsight’s export-to-Excel functionality and connected it to the database, so that users can select which particular expression data sheets to export. Furthermore, we are storing all of the interactions between regulatory genes in the database so that GRNsight users can specify which genes belong in their GRN of interest. In conjunction with the updated expression data export feature, this will allow users to fully automate and validate the creation of GRNmap input workbooks, speeding up the rate of research. GRNsight is available at http://dondi.github.io/GRNsight/.

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A New Gene Expression Dataset for GRNsight: a Web Application for Visualizing Gene Network Models
Ahmad R. Mersaghian, Sarron A. Tadesse, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open-source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap is a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN based on user-provided expression data. GRNsight reads Microsoft Excel input and output workbooks from GRNmap and automatically displays the model data as a graph. Graph edges are color-coded based on the activation and repression relationships between the transcription factors, and nodes are color-coded with time course gene expression data provided by the user or retrieved from the backend database. To expand the potential research scope, a new expression dataset was chosen to be added to the GRNsight database. Apweiler et al. (2012) measured gene expression changes over time in Saccharomyces cerevisiae due to adding glucose to the media. To clean this dataset for inclusion in the database, we needed to remove duplicate data and assign the correct ID types. To further analyze the data with GRNmap, we clustered with STEM to identify significant expression profiles and inferred the gene regulatory network with YEASTRACT. After running GRNmap, we visualized the results of the model using GRNsight. In parallel, ongoing development of GRNsight is focused on fixing bugs and adding enhancements to the user interface and graph layout. GRNsight is freely available at http://dondi.github.io/GRNsight/.

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Loyola Marymount University 13^th Annual Undergraduate Research Symposium (virtual)
March 19-20, 2021

More Robust Testing of Data and UI for GRNsight: a Web Application for Visualizing Models of GRNs
Onariaginosa O. Igbinedion, Ian M. Green, Ahmad R. Mersaghian, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is an open source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model GRNs. GRNsight reads Microsoft Excel input and output workbooks from GRNmap and automatically displays the model data as a graph. Graph edges are color-coded based on the activation and repression relationships between the transcription factors, and nodes are color-coded with time course gene expression data. A new export-to-Excel function enables GRNsight to be used as a input workbook format validator for GRNmap, which GRNmap lacks. Toward this end, we improved the import and export functionality of GRNsight by adding 28 new tests to the testing suite, while simultaneously consolidating and debugging existing tests. This increased user awareness of specific errors and warnings in the import and export process, allowing them to fix problems with Excel workbooks before attempting to run GRNmap. Additionally, we revised the User Interface (UI) to fix graph edge display bugs, as well as viewport selection and movement issues. Along the way, we updated and streamlined the testing documentation for conducting further manual UI tests. Finally, we updated antiquated code libraries to take advantage of new features. GRNsight is freely available at http://dondi.github.io/GRNsight/.

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11^th Annual Loyola Marymount University Undergraduate Research Conference
March 23, 2019

A Dynamic Gene Page Feature for GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
John L. Lopez, Alexia M. Filler, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model GRNs. Using a GRNmap output workbook, GRNsight automatically lays out the network graph through a force simulation or arranges the nodes in a grid. GRNsight uses pointed and blunt arrowheads on the edges and color-codes and adjusts edge thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Nodes are colored based on experimental or simulated time course gene expression data found in the GRNmap workbook. We have also added a feature that gives users the option to export data to an GRNmap-formatted Excel workbook. Additionally, we implemented a dynamic gene information webpage. When a user right-clicks on a node in the graph, a pop-up webpage sends a series of data requests to the JASPAR, NCBI Gene, UniProt, Ensembl, and Saccharomyces Genome Databases. The webpage is populated with data returned regarding the gene’s description, protein sequence, DNA binding motif frequency matrix and sequence logo, and Gene Ontology terms. This new feature enables users to access information about the genes to facilitate biological interpretation of the GRN data, and is available in GRNsight version 3.1.0 at http://dondi.github.io/GRNsight.

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8^th Annual Southern California Systems Biology Conference
February 2019

New Layouts, Data Types, and Architecture for GRNsight 3: a Web Application for Visualizing Gene Regulatory Networks
Mihir Samdarshi, John L. Lopez, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight accepts input files in the Excel, SIF, and GraphML formats and then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges. Activation is represented with red edges, repression is represented with blue edges, and weight magnitudes below a certain threshold are represented with gray edges to denote weak activation or repression relationships. GRNsight now contains features to facilitate network comparison. Nodes on each graph show a heat map corresponding to gene expression levels over time; these nodes can be overlaid with up to two different data sets. The graphs generated by GRNsight may now be automatically laid out in a grid. When a user right-clicks on a gene, data requests are made to five public databases and a web page is dynamically populated with the data returned. Finally, a number of under the hood improvements were made to GRNsight’s architecture. Most notably, GRNsight was made to follow a strict Model-View-Controller (MVC) paradigm. MVC consolidates the application’s state into a single object, from which presentation and interaction are derived. GRNsight version 3.1.3 is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.
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Yeast Genetics Meeting, Stanford University, California
August 22-26, 2018

Mathematical modeling of small gene regulatory networks reveals key regulators and network properties important for controlling the early response to cold shock in Saccharomyces cerevisiae
Kam D. Dahlquist, Ben G. Fitzpatrick, Brandon J. Klein, Margaret J. O’Neil, Lauren M. Kelly

Abstract: A gene regulatory network (GRN) is a group of transcription factors that control the level of expression of genes encoding other transcription factors. Dynamics of GRNs illustrate how expression in the network changes over time. GRNmap, a MATLAB software package (http://kdahlquist.github.io/GRNmap/), uses differential equations to model the dynamics of small-scale GRNs. The software estimates production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on microarray data. Microarray data was obtained from a cold shock experiment where wild-type budding yeast, Saccharomyces cerevisiae, and five strains from which the transcription factors Cin5, Gln3, Hap4, Hmo1, and Zap1 were individually deleted were subjected to cold shock at 13°C for 15, 30, and 60 minutes. Six related GRNs, which ranged from 14-17 transcription factors (nodes) and 25-35 regulatory relationships (edges), were constructed using the microarray data and the YEASTRACT database. GRNmap was then used to estimate production rates, expression thresholds, and regulatory weights for each of these GRNs, followed by visualization of the results with the GRNsight software (http://dondi.github.io/GRNsight/). Forward simulation of the model showed a good fit to the experimental data, particularly in comparison to 30 random networks with the same genes and number of edges. Systematic analysis of edges revealed repeated motifs in the database-derived networks. These motifs included a coherent type I feed forward loop (FFL), an activating regulatory chain, and symmetrical incoherent type I FFLs terminating on the paralogs Yhp1 and Yox1. Multiple regression analysis of the database-derived and random networks demonstrated that model fit to experimental expression data was repeatedly correlated with the eigenvector centrality of individual transcription factors. Better model fit to expression data was observed for transcription factors with high eigenvector centrality, such as Gcn4 and Yhp1. A consolidated network containing 15 transcription factors and 34 edges was constructed from conserved motifs and high eigenvector centrality transcription factors. The consolidated network outperformed all random networks and all but one database-derived network. The consolidated network also featured appreciable overlap with the general environmental stress response, with Hmo1 activating Msn2/Msn4 directly and Yhp1 indirectly.

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Beta Beta Beta Biological Honor Society's Pacific District Convention, Concordia University, Irvine, California
March 2018

Data Comparison Features and Development Tool Improvements for GRNsight: a Web App for Visualizing Gene Regulatory Networks
Mihir Samdarshi, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight accepts input files in the Excel, SIF, and GraphML formats and then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges. Activation is represented with magenta edges, repression is represented with cyan edges, and weight magnitudes below a certain threshold are represented with gray edges to denote weak activation or repression relationships. To better assess the relative strengths of the regulatory relationships, we added a slider that will adjust the gray threshold based on the magnitude of the weight. To facilitate comparison of graphs with different ranges of weight magnitudes, we added the ability to adjust weight normalization factors that control the thicknesses of the edges. Under the hood, the testing framework was refactored into semantic vs. syntactic tests of the validity of input files. Our development tool chain now also includes automatic test coverage calculation (currently 79%), linting for improved code style and readability, and continuous integration for quality assurance. Underlying third-party software libraries including Express, Node-XLSX, and D3 were updated to maintain compatibility and take advantage of new features. GRNsight is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.

Click here to view the poster, which was also presented at the LMU Undergraduate Research Symposium. Mihir was awarded Honorable Mention for the John C. Johnson Award for Excellence in Student Research for this poster presentation.

10^th Annual Loyola Marymount University Undergraduate Research Conference
March 24, 2018

Data Comparison Features and Development Tool Improvements for GRNsight: a Web App for Visualizing Gene Regulatory Networks
Mihir Samdarshi, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight accepts input files in the Excel, SIF, and GraphML formats and then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges. Activation is represented with magenta edges, repression is represented with cyan edges, and weight magnitudes below a certain threshold are represented with gray edges to denote weak activation or repression relationships. To better assess the relative strengths of the regulatory relationships, we added a slider that will adjust the gray threshold based on the magnitude of the weight. To facilitate comparison of graphs with different ranges of weight magnitudes, we added the ability to adjust weight normalization factors that control the thicknesses of the edges. Under the hood, the testing framework was refactored into semantic vs. syntactic tests of the validity of input files. Our development tool chain now also includes automatic test coverage calculation (currently 79%), linting for improved code style and readability, and continuous integration for quality assurance. Underlying third-party software libraries including Express, Node-XLSX, and D3 were updated to maintain compatibility and take advantage of new features. GRNsight is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.

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Southern California Conference for Undergraduate Research, California State Polytechnic University, Pomona
November 2017

Improved Visual Performance and Enhanced Test Files for Different File Formats for GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Yeon-Soo Shin, Eileen J. Choe, Edward B. Bachoura, John David N. Dionisio, Kam D Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight imports data from an adjacency matrix in either an Excel workbook or a SIF or GraphML file, then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges, with colors, end markers, and thicknesses corresponding to the sign and magnitude of activation or repression. GRNsight visualizations can be modified through manually dragging nodes or adjusting sliders that change the force graph parameters. Previous visual issues regarding edge thickness have been adjusted along with corresponding test files to check for any visually unappealing faults. The new GRNsight graph now displays self-regulatory arrows with consistent positions regardless of the node size, edge thickness, or whether it’s weighted. On the server side, syntactic tests for the importable GraphML File Format (.graphml) were written, which return end user-targeted error messages to the user if an incorrectly formatted GraphML file is imported into GRNsight. GRNsight follows open source and test-driven development best practices. The exhaustive testing framework consists of over 160 automated unit tests to ensure that the program runs as expected. We are currently working on changing the embedded D3 code from version 3 to version 4 and planning on applying this for the future release. GRNsight is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.

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9^th Annual Loyola Marymount University Undergraduate Research Conference
March 25, 2017

Improved data interoperability for GRNsight: a web application for visualizing models of gene regulatory networks
Mihir Samdarshi, Yeon-Soo Shin, Edward B. Bachoura, Eileen J. Choe, Nicole A. Anguiano, Anindita Varshneya, John David N. Dionisio, Kam D. Dahlquist

Abstract: GRNsight is a web application and service for visualizing small- to medium-scale gene regulatory networks (GRNs). A GRN consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNsight imports data from an adjacency matrix in an Excel workbook, then produces weighted or unweighted network graphs by representing genes as nodes and regulatory connections as edges, with colors, end markers, and thicknesses corresponding to the sign and magnitude of activation or repression. GRNsight visualizations can be modified through manually dragging nodes or adjusting sliders that change the force graph parameters. GRNsight follows open source and test-driven development best practices. The exhaustive testing framework consists of over 160 automated unit tests to ensure that the program is running as expected. To increase data interoperability, new input and export file types are accepted and produced by GRNsight, including Simple Interaction Format (SIF) text files and GraphML XML files. The addition of these file types required the refactoring of the test suite, separating syntactic tests for the different file formats from semantic tests that apply to all files. Consequently, there is more complete error and warning message coverage across different file types, providing valuable feedback to the user. GRNsight is freely available at http://dondi.github.io/GRNsight/; the code is available under the open source BSD license at https://github.com/dondi/GRNsight.
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Bioinformatics Open Source Conference (BOSC) 2016 and Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, FL
July 8-12, 2016

GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks
Kam D. Dahlquist, Ben G. Fitzpatrick, John David N. Dionisio, Nicole A. Anguiano, Juan S. Carrillo, Tessa A. Morris, Anindita Varshneya, Natalie E. Williams, K. Grace Johnson, Trixie Anne M. Roque, Kristen M. Horstmann, Mihir Samdarshi, Chukwuemeka E. Azinge, Brandon J. Klein, Margaret J. O’Neil1

Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our open source MATLAB software package, GRNmap (http://kdahlquist.github.io/GRNmap/), uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in budding yeast, Saccharomyces cerevisiae, for the wild type strain and strains deleted for the transcription factors Cin5, Gln3, Hap4, Hmo1, and Zap1, giving biological insights into this process. GRNsight is an open source web application for visualizing such models of gene regulatory networks (http://dondi.github.io/GRNsight/index.html). GRNsight accepts GRNmap- or user-generated Excel spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. This work was partially supported by NSF award 0921038.

Click here to view the poster on F1000Research. Click here to view poster from GRNsight on SlideShare.

Experimental Biology 2016, San Diego, CA
April 4, 2016

Test-driven development improves GRNsight: a web application for visualizing models of gene regulatory networks
Anindita Varshneya, Mihir Samdarshi, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick

Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model the dynamics of GRNs. GRNsight automatically lays out the network graph based on GRNmap output spreadsheets. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. We have now implemented an exhaustive unit testing framework using Mocha and the Chai assertion library to perform test-driven development where unit tests are written before new functionality is coded. This framework consists of over 135 automated unit tests that examine about 450 test files to ensure that the program is running as expected. Error and warning messages have a three-part framework that informs the user what happened, the source of the problem, and possible solutions. For example, GRNsight returns an error when the spreadsheet is formatted incorrectly or the maximum number of nodes or edges is exceeded. The completion of the testing framework marks the close of development for version 1 (the current release stands at version 1.12). In version 2.0 of GRNsight, a new feature will be implemented that colors the nodes (genes) based on the expression data. The user will have the choice to display the experimental data or the data produced by the forward simulation feature in GRNmap. GRNsight is available at http://dondi.github.io/GRNsight/.

Loyola Marymount University 8^th Annual Undergraduate Research Symposium
March 19, 2016

Test-driven development improves GRNsight: a web application for visualizing models of gene regulatory networks
Anindita Varshneya, Mihir Samdarshi, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick

Abstract: GRNsight is an open source web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model the dynamics of GRNs. GRNsight automatically lays out the network graph based on GRNmap output spreadsheets. GRNsight uses pointed and blunt arrowheads, and colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. Visualizations can be modified through manual node dragging and sliders that adjust the force graph parameters. We have now implemented an exhaustive unit testing framework using Mocha and the Chai assertion library to perform test-driven development where unit tests are written before new functionality is coded. This framework consists of over 160 automated unit tests that examine over 450 test files to ensure that the program is running as expected. Error and warning messages inform the user what happened, the source of the problem, and possible solutions. The completion of the testing framework marks the close of development for version 1 (the current release stands at version 1.12). In version 2.0 of GRNsight, a new feature will be implemented that colors the nodes (genes) based on expression data provided by GRNmap. GRNsight is available at http://dondi.github.io/GRNsight/.

40^th Annual West Coast Biological Sciences Undergraduate Research Conference
April 25, 2015

Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Nicole Anguiano, Anindita Varshneya, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick

Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRN). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN, can mathematically model GRNs. The general graph visualization tools that exist for displaying GRNs did not fulfill our needs, so we created GRNsight. GRNsight automatically lays out the network graph based on spreadsheets uploaded from GRNmap. GRNsight colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. GRNsight uses pointed and blunt arrowheads to indicate activation and repression, respectively. Visualizations can be modified through manual node dragging and sliders that adjust the force layout parameters. Since version 1.0, GRNsight has acquired significant user interface improvements, bug fixes, extended robustness to errors, and aesthetic refinements. Demo spreadsheets are now available for users who do not have their own models. GRNsight's current release is version 1.10, with beta version 1.11 available for testing new features. GRNsight is available at http://dondi.github.io/GRNsight/.

Loyola Marymount University 7^th Annual Undergraduate Research Symposium
March 21, 2015

Improvements to GRNsight: a Web Application for Visualizing Models of Gene Regulatory Networks
Nicole Anguiano, Anindita Varshneya, Kam D. Dahlquist, John David N. Dionisio, and Ben G. Fitzpatrick

Abstract: GRNsight is a web application and service for visualizing models of gene regulatory networks (GRN). A gene regulatory network consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. GRNs can be mathematically modeled by GRNmap, a MATLAB program that performs parameter estimation and forward simulation of a differential equations model of a GRN. The general graph visualization tools that exist for displaying GRNs did not fulfill our needs, so we created GRNsight. GRNsight automatically lays out the network graph based on spreadsheets uploaded from GRNmap. It is written in JavaScript, with diagrams facilitated by D3.js. Node.js and the Express framework handle server-side functions. GRNsight’s diagrams are based on D3.js’s force graph layout algorithm, which was then extensively customized to support the specific needs of GRN visualization. GRNsight colors the edges and adjusts their thicknesses based on the sign (activation or repression) and magnitude of the GRNmap weight parameter. GRNsight uses pointed and blunt arrowheads to indicate activation and repression, respectively. Visualizations can be modified through manual node dragging and sliders that adjust D3.js’s force layout parameters. Since version 1.0, GRNsight has acquired significant user interface improvements, bug fixes, extended robustness to errors in file formats, and aesthetic refinements, particularly for edge markers. Demo spreadsheets are now available for users who do not have their own models. GRNsight's current release is version 1.8, with beta version 1.9 available for testing new features. GRNsight is available at http://dondi.github.io/GRNsight/.