BIOL398-05/S17:Week 14/15

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BIOL398-05: Biomathematical Modeling

MATH 388-01: Survey of Biomathematics

Loyola Marymount University

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This journal entry is due on Thursday, May 4 at midnight PDT (Wednesday night/Thursday morning). NOTE that the server records the time as Eastern Daylight Time (EDT). Therefore, midnight will register as 03:00. NOTE also that the assignment is due Finals Week.

Individual Journal Assignment

  • Store this journal entry as "username Week 14/15" (i.e., this is the text to place between the square brackets when you link to this page).
  • Create the following set of links. (HINT: These links should all be in your personal template that you created for the Week 1 Assignment; you should then simply invoke your template on each new journal entry.)
    • Link to your journal entry from your user page.
    • Link back from your journal entry to your user page.
    • Link to this assignment from your journal entry.
    • Don't forget to add the "BIOL398-05/S17" category to the end of your wiki page.

Homework Partners

Please meet with your partner (either face-to-face or virtually) at least once when preparing this assignment. Even though you may work together to understand the article, your journal assignment must be completed individually. It is not acceptable to do a joint assignment and copy it over to each others' journal page.

  • Lauren, Cameron
  • Nika, Conor
  • Maggie

Electronic Lab Notebook

Complete your electronic notebook that gives the details of what you did for the assignment this week. Your notebook entry should contain:

  1. The purpose: what was the scientific purpose of your investigations?
    • Note that this is different than the learning purpose.
  2. Your workflow or methods: what did you actually do? Give a step by step account.
    • There should be enough detail provided so that you or another person could re-do it based solely on your notebook.
    • You may copy protocol instructions to your page and modify them as to what you actually did, as long as you provide appropriate attribution in the acknowledgments and references section.
    • Take advantage of the electronic nature of the notebook by providing screenshots, links to web pages, etc.
  3. Your results: the answers to the questions in the protocol, plus any other results you gathered. Your results will include some or all of the following: images, plots, data, and files.
    • Note that files left on the Desktop or My Documents or Downloads folders on the Seaver 120 computers will be deleted upon restart of the computers. Files stored on the T: drive will be saved. However, it is not a good idea to trust that they will be there when you next use the computer.
    • Thus, it is a critical skill for data and computer literacy to back-up your data and files in at least two ways:
    • References to data and files should be made within the methods and results section of your notebook, listed above.
    • In addition to these inline links, create a Data and Files section of your notebook to make a list of the files generated in this exercise.
  4. A scientific conclusion: what was your main finding for today's project? Did you fulfill the purpose? Why or why not?
  5. The Acknowledgments section, see below.
  6. The References section, see below.


In this section, you need to acknowledge anyone who assisted you with your assignment, either in person, electronically, or even anonymously without their knowledge.

  1. You must acknowledge your homework partner or team members with whom you worked, giving details of the nature of the collaboration. You should include when and how you met and what content you worked on together. An appropriate statement could be (but is not limited to) the following:
    • I worked with my homework partner (give name and link name to their user page) in class. We met face-to-face one time outside of class. We texted/e-mailed/chatted online three times. We worked on the <details> portion of the assignment together.
  2. Acknowledge anyone else you worked with who was not your assigned partner. This could be Dr. Dahlquist or Dr. Fitzpatrick (for example, via office hours), the TA, other students in the class, or even other students or faculty outside of the class.
  3. If you copied wiki syntax or a particular style from another wiki page, acknowledge that here. Provide the user name of the original page, if possible, and provide a link to the page from which you copied the syntax or style.
  4. If you need to reference content (such as the methods of a protocol) also acknowledge it here and include a formal citation in your References section (see below).
  5. You must also include this statement unless otherwise noted:
    • "Except for what is noted above, this individual journal entry was completed by me and not copied from another source."
  6. Sign your Acknowledgments section with your wiki signature.


  1. In this section, you need to provide properly formatted citations to any content that was not entirely of your own devising. This includes, but is not limited to:
    • methods
    • data
    • facts
    • images
    • documents, including the scientific literature
  2. The references in this section should be accompanied by in text citations on your page that refer to these references.
  3. Do not include citations/references to sources that you did not use.
  4. Generally, you should include a reference to that week's assignment page.
  5. The references should be formatted according to the APA guidelines.
  6. For more detailed instructions on how to cite journal articles, books, or web pages, please see the document Guidelines for Literature Citations in a Scientific Paper that you were given on the first day of class.

Dynamical Systems Modeling of your Gene Regulatory Network

For last week's assignment, you created a Microsoft Excel input workbook for the model. Now you are ready to run the model and analyze the results. The software we will use is called GRNmap, which stands for Gene Regulatory Network Modeling and Parameter Estimation. It is written in MATLAB and can be run from code or run as a stand-alone executable if you don't have MATLAB installed. However, it can only be run in Windows, not on Macs.

  • To run GRNmap from code, you must have MATLAB R2014b installed on your computer.
    1. Download the GRNmap v1.4.4 code from the GRNmap Downloads page.
    2. Unzip the file. (Right-click, 7-zip > Extract here)
    3. Launch MATLAB R2014b.
    4. Open GRNmodel.m, which will be in the directory that you unzipped GRNmap-1.4.4 > matlab
    5. Click the Run button (green "play" arrow).
    6. You will be prompted to select your input workbook.
    7. You will see an optimization diagnostics graphic that shows the progress of the estimation.
    8. When the run is over, expression plots will display.
    9. Output .xlsx and .mat files will be saved in the same folder as your input folder, along with .jpg files containing the optimization diagnostic and individual expression plots. Save these files.
    10. Note that if you need to run GRNmap again, you should not use the same directory for the input file. Currently, GRNmap will overwrite previous output.
    11. Also note that you should run the model on the same computer if you want to compare model runs.

Analyzing the Modeling Results

In class on April 25, we will take a look at the modeling results and discuss how to analyze them. We will discuss:

  • LSE/minLSE ratio
  • MSE's and expression plots for individual genes in relation to their ANOVA p values
  • Visualization of the weighted graph with GRNsight
  • Making bar charts to give a graphical representation of the parameter values.

Based on these analyses, you will propose a some additional in silico experiments that you can do with the model. Some ideas are:

  • For our initial runs, we estimated all three parameters w, P, and b.
    • How do the modeling results change if P is instead fixed and w and b are estimated?
    • How do the modeling results change if b is fixed and w and P are estimated?
    • How do the modeling results change if P and b are fixed, and only w is estimated?
  • For our initial runs, we included all three microarray datasets, wt, Δgln3, and Δhap4.
    • What happens to the results if we base the estimation on just two strains (wt + one deletion strain)?
    • What happens to the results if we base the estimation on just the wt strain data?
  • When viewing the modeling results in GRNsight, you may determine that one or more genes in the network does not appear to be doing much.
    • What happens to the modeling results if you delete this gene from the network and re-run the model (remember you will have to delete references to this gene in all worksheets of the input file).

Final Research Presentation

  • You and your partner together will prepare a 20-30 minute PowerPoint presentation that will present the results of your final project. Please follow these guidelines when creating your presentation. You will need approximately 20-30 slides (1 slide per minute) for your presentation. You will be graded according to this rubric.
    • Upload your slides to the OpenWetware wiki by the Week 14/15 journal assignment deadline (midnight on May 4). Each partner should have a link to the same PowerPoint file. You may make changes to your slides in advance of your presentation, but you will be graded on what you upload by the journal deadline.
  • Your presentation will include the following:
    • Title slide that gives the main take-home message as the title of your presentation, the authors, date, and venue (course number and title).
    • Outline slide that is a summary of take-home messages of your talk (should mirror your conclusion slide)
    • The body of your talk (more details below)
    • Conclusion slide that mirrors your outline
    • Future directions
    • Acknowledgments
    • References

Introduction & Background

The introduction gives the background information necessary to understand the motivation for your project and your research results. The introduction should be in the form of a logical argument that "funnels" from broad to narrow. Include the following:

  • States importance of the problem
    • Why are we studying gene regulation and cold shock?
  • States what is known about the problem
    • Introduce the DNA microarray experiment that was performed.
  • States what is unknown about the problem
    • Little is known about which transcription factors regulate the early response to cold shock
  • States clues that suggest how to approach the unknown
    • Each of the journal club articles that you all presented has a piece of the puzzle that motivates this project
  • States the question the project is trying to address
    • Using the model to estimate the relative contribution of each transcription factor to the regulation of gene expression


  • Describe the entire workflow of this project using a flow chart diagram.
    • Experimental design of the microarray experiment
    • Statistical analysis of the microarray data
    • Clustering and GO term analysis
    • Finding candidate transcription factors with YEASTRACT
    • Generating and paring down the adjacency matrix
    • The differential equation and least squares equation that were used for performing the initial estimation
    • Creating the input workbook and how that relates to those equations
    • Analyzing the modeling results
    • Additional in silico modeling experiments

Results & Discussion

  • Table of ANOVA results from the Week 11 Assignment, discussing the interpretation of the p values.
  • From the STEM analyis, include as figures the overall results (the screenshot showing all of the clusters) and then focus on the ones you interpreted for your journal assignment.
    • Include a table showing the GO results for that cluster (just the narrowed down list of terms that you have interpreted).
    • Discuss what the p values for the cluster and for the GO term list mean.
    • Discuss the biological interpretation of your GO terms.
  • Include a table that lists the transcription factors that you and your partner are working with and their enrichment p value from YEASTRACT (from the Week 12 Assignment). Include just the transcription factors that made it into your final networks.
    • Describe how and why you and your partner chose these transcription factors for your networks.
    • Include a figure of the unweighted networks visualized with GRNsight.
  • Modeling results (from the current assignment), including the LSE/minLSE ratio for each model run. Include the following parameter bar charts
    • Optimized weight parameters (w)
    • Optimized production rates (P)
    • Optimized threshold b parameters
    • Show the individual expression plots for each transcription factor for one of the initial runs, with the MSE and ANOVA values for each gene superimposed. You will want to organize these so that they can be compared easily. For the subsequent runs, compare plots for "interesting" genes with each other.
    • Show the GRNsight visualization of the weighted networks, making sure that the genes are placed in the same relative location as each other an as the unweighted network figure.
  • Interpret the results of the model simulation.
    • Examine the graphs that were output by each of the runs. Which genes in the model have the closest fit between the model data and actual data? Which genes have the worst fit between the model and actual data? Why do you think that is? (Hint: how many inputs do these genes have?) How does this help you to interpret the microarray data?
    • Which genes showed the largest dynamics over the timecourse? In other words, which genes had a log fold change that is different than zero at one or more timepoints. The p values from the Week 11 ANOVA analysis are informative here. Does this seem to have an effect on the goodness of fit (see question above)?
    • Which genes showed differences in dynamics between the wild type and the other strain your group is using? Does the model adequately capture these differences? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
    • Examine the bar charts comparing the weights and production rates between the two runs. Were there any major differences between the runs? Why do you think that was? Given the connections in your network (see the visualization in GRNsight), does this make sense? Why or why not?
    • Finally, based on the results of your entire project, which transcription factors are most likely to regulate the cold shock response and why?
  • How do you interpret the results of the additional in silico experiments you performed with the model in light of the above?
  • Relate the results of your project to the paper you presented for journal club in Week 10
  • What future directions would you take if you were to continue this project?

Shared Journal Assignment

  • Store your shared journal entry in the shared Class Journal Week 14/15 page. If this page does not exist yet, go ahead and create it (congratulations on getting in first :) )
  • Link to your journal entry from your user page.
  • Link back from the journal entry to your user page.
  • Sign your portion of the journal with the standard wiki signature shortcut (~~~~).
  • Add the "BIOL398-05/S17" category to the end of the wiki page (if someone has not already done so).


Reflect back on your learning for this project and for the entire semester and answer the following:

  1. What is the value of combining biological and mathematical approaches to scientific questions?
  2. Looking back on your reflections on the Janovy and Stewart readings from the Week 1 Class Journal, do you have any further insights to share? Have your answers changed to those original reflection questions? Why or why not?
  3. What did you learn in this class?
    • With your head (biological and/or mathematical principles)
    • With your heart (personal qualities and teamwork qualities that make things work or not work)?
    • With your hands (technical skills)?
  4. What lesson will you take away from this class that you will still use a year from now?