# BIOL398-03/S13:Class Journal Week 13

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## Contents |

## Reflection

Kevin Matthew McKay 01:44, 17 April 2013 (EDT)

## Laura Terada

- Look over the excel workbook input file. How might you change the network to add more genes/transcription factors?
- In the Excel spreadsheet, you can add more columns/rows to add more genes/transcription factors to run.

- How might you run a computer experiment to examine the deletion of a gene?
- Instead of 1, you could input a value of 0 to show that there is a deletion. This change will show how the deletion affects other genes/transcription factors.

- Return to the chemostat. In concept, without worrying about creating matlab code, how might you use the least squares technique to get parameters from the ter Schure et al experiments?
- We can use a line of best fit in the ter Schure et all data to use the least squares technique.

- Read over the Vu and Vhoradsky paper in this week's reading list. Is the method of the paper different from that in class? If so, in what ways?
- Although the Vu and Vhoradsky paper used a nonlinear differential equation model of gene expression and our class used a similar sigmoidal model, we did not perform a polynomial fit. Vu and Vhoradsky used a polynomial fit as an approximation of real expression profiles because the true profiles are obscured by experimental errors.

## Week 13 Ashley Rhoades

- How might you change the network to add more genes/transcription factors?
- Add more transcription factors

- How might you run a computer experiment to examine the deletion of a gene?
- Delete a gene or alter its transcription to zero to look at the effect on the other genes.

- Return to the chemostat. In concept, without worrying about creating matlab code, how might you use the least squares technique to get parameters from the ter Schure et al experiments?
- You could look a best fit line of the ter Schure data to estimate the parameters

- Read over the Vu and Vhoradsky paper in this week's reading list.Is the method of the paper different from that in class? If so, in what ways?
- Vu and Vhoradsky set limit of parameters to 500.They looked at cyclin networks.

## Salman Ahmad

- Look over the excel workbook input file. How might you change the network to add more genes/transcription factors?
- More genes and transcription factors can be added to the excel file very easily. All you would have to do is add more columns and rows to the table and add the new genes and transcription factors.

- How might you run a computer experiment to examine the deletion of a gene?
- If a gene is deleted it will have no ability to up or down regulate any other gene. The easiest way to examine the deletion of a gene is to delete it from the model.

- Return to the chemostat. In concept, without worrying about creating matlab code, how might you use the least squares technique to get parameters from the ter Schure et al experiments?
- You would have to use the least square technique to fit a line through their data. If you are able to get the lines to be similar, then you would have an estimate of what the parameters were for their experiment.

- Read over the Vu and Vhoradsky paper in this week's reading list. Is the method of the paper different from that in class? If so, in what ways?
- In the Vu and Vhoradsky paper they look at 40 cell cycle regulated genes. The method for calculating is not that different from the one we went over in class. In this paper the methods are being used on a greater scale. "The procedure was applied to 40 yeast cell cycle regulated target genes and 184 potential regulators" In the paper there were also comparisons between linear and non-linear modeling.

Salman Ahmad 18:13, 18 April 2013 (EDT)

## James P. McDonald

- How might you change the network to add more genes/transcription factors?
- You can add more genes/transcription factors in the network in the excel file by adding additional rows and columns with new genes or transcription factors.

- How might you run a computer experiment to examine the deletion of a gene?
- If you add a 0 in place of a 1 it represents the deletion of that particular gene. You could also delete the gene from the excel file. You can then run the experiment with this deletion to see its effect.

- Return to the chemostat. In concept, without worrying about creating matlab code, how might you use the least squares technique to get parameters from the ter Schure et al experiments?
- The least squares technique relies on an approximation and data fitting, therefore placing a line of best fit into the ter Schure data can allow you to approximate the parameters of the experiment.

- Read over the Vu and Vhoradsky paper in this week's reading list. Is the method of the paper different from that in class? If so, in what ways?

## Matthew E. Jurek Week 13

- How might you change the network to add more genes/ transcription factors?
- Adding more transcription factors relates to last weeks assignment, when additional transcription factors were added to the network before generating a network map. Likewise, YEASTRACT could be used to explore additional transcription factors. These factors could be added to the matrix, and if they had relevance to the network (based on either 0 or 1 within the matrix) they could be added.

- How might you run a computer experiment to examine the deletion of a gene?
- All of the genes are listed within the spreadsheet. To examine the deletion of a gene, the spreadsheet would have to be manipulated. An easy way to do this would involve deleting the gene from the spreadsheet and observing its impact on the rest of the model.

- Return to the chemostat. In concept, without worrying about creating matlab code, how might you use the least squares technique to get parameters from the ter Schure et al experiments?
- The least squares technique normalizes the data for a better fit. Looking at the ter Schure et al papers, almost all the figures exhibit a linear trend either up or down. Because of this, a linear best-fit line could help when attempting to approximate parameters.

- Read over the Vu and Vhoradsky paper in this week's reading list. Is the method of the paper different from that in class? If so, in what ways?
- Vu and Vhoradsky utilized a similar method to our own, however, they are working with a larger gene pool. Their modeling is not much different than ours, aside from the fact that they used polynomial fit. Part of their reason for using this was because the data they used was only available as averages, whereas we initially began with raw data before scaling it.

**Matthew E. Jurek 20:57, 18 April 2013 (EDT)**: