User talk:Anthony J. Wavrin

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m (bgf forgot to add signature to my response to AJW questions.)
Current revision (13:28, 18 April 2013) (view source)
(Week 12 Journal Feedback: gave feedback on shared journal answer)
 
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== Week 12 Journal Feedback ==
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* Thank you for submitting your assignment on time.
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* It was difficult to look at your list of GO terms because you only included the term ID, but not the name of the GO term itself.
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* Recall from the journal club article and the review articles on cold shock, that ribosome biogenesis is known to be up-regulated during cold shock.  Your results are consistent with this.
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* The gene expression profile you picked, #48, consists of a group of 42 genes that follow a pattern of up-regulation during the cold shock timepoints and then no change compared to the t0 control at the recovery time points.  Thus, the Gene Ontology terms associated with this cluster ''also'' are following a pattern of up-regulation followed by not change relative to the control.  In other words, the GO terms have genes associated with them that follow this pattern.  If you need further clarification, please ask me about this in class.
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''— [[User:Kam D. Dahlquist|Kam D. Dahlquist]] 18:25, 17 April 2013 (EDT)''
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=== Week 12 Shared Journal ===
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* For question 4 of the shared journal reflection, be careful in the way you state your answer. The profile p value is a measure of the probability that you would see that many genes that fit that expression profile due to chance. The GO term p value is a measure of the probability that you would see that many genes in that profile associated with that GO term due to chance. Also, the GO terms do not necessarily include transcription factors.  There ''are'' GO terms specific for transcription, but most of the terms relate to other cellular processes.  YEASTRACT was telling us about which transcription factors potentially regulate the genes in the cluster.
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''— [[User:Kam D. Dahlquist|Kam D. Dahlquist]] 13:28, 18 April 2013 (EDT)''
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== Week 1 Journal Feedback ==
== Week 1 Journal Feedback ==
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'''[[User:Ben G. Fitzpatrick|Ben G. Fitzpatrick]] 13:21, 4 February 2013 (EST)'''
'''[[User:Ben G. Fitzpatrick|Ben G. Fitzpatrick]] 13:21, 4 February 2013 (EST)'''
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== Week 2 Journal Feedback ==
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* Thank you for submitting your assignment on time.
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* You did a reasonable job of simulating the systems and discussing state variables and parameters.
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* You file of images is nice; it would be helpful, however, to know all the parameters and initial states.  See Elizabeth's file for a good example.
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* Your graphs are identical for the two cases, making me suspicious that you did not save the images correctly.
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* How might you model the waste as an additional state variable (third DE)? 
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* The concentration gradient is an interesting issue.  We assume the chemostat is well mixed. Having a state variable that depends not only on time but also on the 3-d position in the tank... how would that look?
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'''[[User:Ben G. Fitzpatrick|Ben G. Fitzpatrick]] 13:26, 4 February 2013 (EST)'''

Current revision

Contents

Week 12 Journal Feedback

  • Thank you for submitting your assignment on time.
  • It was difficult to look at your list of GO terms because you only included the term ID, but not the name of the GO term itself.
  • Recall from the journal club article and the review articles on cold shock, that ribosome biogenesis is known to be up-regulated during cold shock. Your results are consistent with this.
  • The gene expression profile you picked, #48, consists of a group of 42 genes that follow a pattern of up-regulation during the cold shock timepoints and then no change compared to the t0 control at the recovery time points. Thus, the Gene Ontology terms associated with this cluster also are following a pattern of up-regulation followed by not change relative to the control. In other words, the GO terms have genes associated with them that follow this pattern. If you need further clarification, please ask me about this in class.

Kam D. Dahlquist 18:25, 17 April 2013 (EDT)

Week 12 Shared Journal

  • For question 4 of the shared journal reflection, be careful in the way you state your answer. The profile p value is a measure of the probability that you would see that many genes that fit that expression profile due to chance. The GO term p value is a measure of the probability that you would see that many genes in that profile associated with that GO term due to chance. Also, the GO terms do not necessarily include transcription factors. There are GO terms specific for transcription, but most of the terms relate to other cellular processes. YEASTRACT was telling us about which transcription factors potentially regulate the genes in the cluster.

Kam D. Dahlquist 13:28, 18 April 2013 (EDT)

Week 1 Journal Feedback

  • Thank you for submitting your assignment on time.
  • There are a few of things that you need to fix on your individual user page and shared journal page. Please make these changes by next week's journal deadline (midnight, February 8) to earn back the points you missed on this assignment.
    1. You need to put the complete street address for your box at LMU.
    2. You have two headings called "Links" on your page because one is coming from your template. You can delete the heading on the actual user page.
    3. On the Week 1 shared journal page, make a link back to your user page at the top of the section where you answered the questions.
  • You are making good use of the summary field, being conscientious to fill it out each time. Keep up the good work!
  • Please feel free to delete the welcome message from OpenWetWare below.

Kam D. Dahlquist 18:42, 30 January 2013 (EST)


You asked: "Hi Dr. Fitzpatrick, What is the most interesting application of math, whether it is simple or complex, you have done or have learned about? Anthony J. Wavrin 00:44, 22 January 2013 (EST)"

My answer: The most interesting thing I've done... hmmm... they are all interesting (heh). At this point I'd have to say my work (in collaboration with a lot of people) on math modeling and analysis of college drinking problems (see my recent paper). The thing that got me into mathematical biology was Murray's models of how leopards get their spots.

Ben G. Fitzpatrick 13:21, 4 February 2013 (EST)

Week 2 Journal Feedback

  • Thank you for submitting your assignment on time.
  • You did a reasonable job of simulating the systems and discussing state variables and parameters.
  • You file of images is nice; it would be helpful, however, to know all the parameters and initial states. See Elizabeth's file for a good example.
  • Your graphs are identical for the two cases, making me suspicious that you did not save the images correctly.
  • How might you model the waste as an additional state variable (third DE)?
  • The concentration gradient is an interesting issue. We assume the chemostat is well mixed. Having a state variable that depends not only on time but also on the 3-d position in the tank... how would that look?

Ben G. Fitzpatrick 13:26, 4 February 2013 (EST)

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