Talk given at the Bioinformatics Open Source Conference (BOSC) on July 9, 2016 in Orlando, Florida, entitled: GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks. 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. Truncated...

1. GRNmap and GRNsight:
Open Source Software for Dynamical Systems
Modeling and Visualization of Medium-Scale
Gene Regulatory Networks
Kam D. Dahlquist, Ph.D.
Department of Biology
Loyola Marymount University
July 9, 2016
Bioinformatics Open Source Conference (BOSC)

2. Open Science
(open process)
Citizen
Science
Open
Source
Code
Open Access
(creative commons)
Reproducible
Research
Research
Integrity
Open Science Ecosystem
Open DataOpen Pedagogy
With thanks to John Jungck

3. HHMI Science Education Alliance
Phage Hunters Advancing Genomics
and Evolutionary Science Program
http://seaphages.org/
BioQUEST Curriculum Consortium
30th Anniversary this year!
http://www.bioquest.org
The Genome Consortium for Active Teaching
NextGen Sequencing Group
http://gcat-seek.weebly.com/
Students Benefit from Open Source and Open Data

4. GRNsight: http://dondi.github.io/GRNsight/
GRNmap: http://kdahlquist.github.io/GRNmap/
Back row (left to right)
Brandon Klein
Mihir Samdarshi
Kevin McGee
Kevin Wyllie
K. Grace Johnson
Kristen Horstmann
Tessa Morris
Front row (left to right)
Maggie O’Neil
Monica Hong
Kam Dahlquist
Anindita Varshneya
Kayla Jackson
Not pictured
John David N. Dionisio
Ben G. Fitzpatrick
Nicole Anguiano
Juan Carrillo
Trixie Anne Roque
Chukwuemeka Azinge
Funding: NSF RUI, Kadner-Pitts Research Grant, LMU SURP,
LMU Honors Program, LMU Rains Research Assistant Program

5. Cold shock microarray
data from wt and TF
deletion strains
Systems Biology Approach to Understanding the
Regulation of the Cold Shock Response in Yeast
Normalization,
statistical analysis,
clustering
Derivation of gene
regulatory networks
from YEASTRACT
Dynamical systems
modeling using
GRNmap
Visualization of
modeling results
using GRNsight
Interpretation,
new questions,
new experiments
0
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Activation
1/w
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1
Repression
1/w
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Dash1
15°C
wt

6. GRNmap: Gene Regulatory Network Modeling and
Parameter Estimation
Weight parameter, w, gives the direction
(activation or repression) and magnitude of
regulatory relationship. 0
0.5
1
Activation
1/w
0
0.5
1
Repression
1/w
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
 
)(
)(exp1
)(
txd
btxw
P
dt
tdx
ii
j
ijij
ii

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


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

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7. GRNsight is written in JavaScript, customizing the D3.js library.
Node.js and the Express framework handle server-side functions.

8. Users Click and Drag to Customize Layout,
Mouse-over Edge Displays Value of Weight Parameter

9. A Tale of Two Open Source Projects. . .
GRNmap
• Developed in MATLAB by
successive math students over
nearly a decade
• Shifted to open development on
GitHub in the last two years
• Free executable now available
• Still in the midst of software
refactoring and “paying off our
technical debt”
• New features arise through
interplay between student
“coding” and “data analysis”
teams
• Goal is reproducible research
GRNsight
• Why “yet another graph layout
tool”?
• Specific use-case of displaying
output from GRNmap directly in
a web application
• Reduced learning curve for
student users
• Do one thing well
• Teach software engineering best
practices while creating a useful
tool
• New features arise through
interplay with GRNmap team,
leverage other open source
tools

10. Summary
• GRNmap models the dynamics of “medium-scale”
gene regulatory networks using differential equations.
• A penalized least squares approach was used successfully to
estimate parameters from yeast cold shock microarray data.
• Can be used with time course gene expression data from any
species.
• GRNsight automatically generates weighted network
graphs from the spreadsheets produced by GRNmap.
• This facilitates visualization of the relative influence of each
transcription factor in controlling the cold shock response.
• Can be used to visualize any small- to medium-scale network in
adjacency matrix format (< 35 nodes, < 70 edges).
• Challenges in bridging the cultures of mathematics
and computing were overcome.
• Shifting a longstanding project to open development and software
engineering best practices, versus...
• Building an open source project with test-driven development,
“standing on the shoulders of giants”.