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| | =Markov Chain Model For Flipping= | | =Markov Chain Model For Flipping= |
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| − | The false positive programs will tell how many false positives there are in Adelman’s graph without two edges (12) and the other is for the original graph with 14 edges. To develop the program we first made a list of all the different ways to make each number represented in the path and numbered them. We also, numbered each of the edges. we took the numbered edges and made vectors of the ways that each number can be represented at the top of the program.
| + | One of the first projects we worked on was to develop a mathematical model of graphs for the pancake problem. These graphs were used to find the percent of plasmids that solved problem based on the number of flips. We also developed graphs with biases to see if the length of the flips effect the probability of making the flip. In some of the graphs we saw bouncing because from a starting point you can only get to a solution by an odd number of flips or an even number of flips. If you get to a point on an even number of flips you can never get to an odd number. When the graph shows convergence, regardless whether the number of flips is even or odd you will have a chance to get to one of the solutions. The convergence occurs at .25 because there are 2/8 chances of getting to the solution after a high number of flips which reduces to ¼. We also did graphs using bias wieghts where it takes more flips for the graph to have the bouncing behavior. The biologist suspect that the length of the flips effects the probability of making the flips. Then you would compare mathematical graph to the biologist graph to see the bias. |
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| − | First we made a loop to make sure that MATLAB picks up at least one representation of each number. Then the perms function is used to arrange all the representations of each number except the promoter in all the different ways and call it matrix g.Then you want to find all the rows where the last number of each row of the matrix ends with the number that represents the terminator. Then using the ord function all the different ways of putting the two promoters in the front of each row is developed. Now the program changes the label of the numbers from there number representation number to there edge number where the label goes from 1 to 2 and transposes them. The way that the program lays out the numbers need to be transposed again so we called that matrix sl. Now an if statements were made for the total number of repeats for each row equal to the number of repeats next to each other. The counter is used to find the total number of repeats that are next to each other. For all the number of edges left after all of the repeats are taken out we replaced those with zeros. Now we had to make a vector of ones that will be from 1 to the length of sl. What we call usl is where we get rid of one of the repeated numbers that’s next to each other because one number shows the back half and front half of the gene. Now the new usl is relabeled and includes the numbers for the edges with the promoter at the beginning of each and the terminator at the end of each with the zeros replacing the edges that are after the terminator.
| + | MATLAB programs that we developed using Markov Chains |
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| − | If there is a row in matrix g that does not end with a terminator then we use the command elseif and add a terminator to each of those rows. Since there are two terminators then the program goes through the process one time with the first terminator and another time with the other terminator making these rows longer than if they originally had a terminator at the end. After this whole process all the rows start with a promoter and end with a terminator and have zeros after the terminator is represented.
| + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fliplength.m 1.Flip Length] |
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| − | At the end of the program we print out the total number of arrangements of false positives before the zeros after the terminator are replaced with the extra edges. Also, the rows that are not repeated are printed out. Now the program finds all the rows that have terminators starting in column 8 to 12. We found how many ways we can arrange the remaining edges depending on where the terminator is and take that number and multiply by the amount of times the terminator is in each spot and add them together for each column number. This number gives the number of false positives for a Hamiltonian path.
| + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/pureflip.m 2.Pure Flip] |
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| − | MATLAB programs used to find the false positives:
| + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/biasweighter.m 3.Bias Weighter] |
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpalmostadelman.m 1. Adelman's False Positives with 12 nodes]
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpadelman.m 2. Adelman's False Positives with 14 nodes]
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpweighter.m 3. Counter Program for Adelman's False Positives]
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| | <br> | | <br> |
| | = True Positives / False Positives = | | = True Positives / False Positives = |
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| − | '''Markov Chains'''
| + | MATLAB programs used to find the false positives: |
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| − | One of the first projects we worked on was to develop a mathematical model of graphs for the pancake problem. These graphs were used to find the percent of plasmids that solved problem based on the number of flips. We also developed graphs with biases to see if the length of the flips effect the probability of making the flip. In some of the graphs we saw bouncing because from a starting point you can only get to a solution by an odd number of flips or an even number of flips. If you get to a point on an even number of flips you can never get to an odd number. When the graph shows convergence, regardless whether the number of flips is even or odd you will have a chance to get to one of the solutions. The convergence occurs at .25 because there are 2/8 chances of getting to the solution after a high number of flips which reduces to ¼. We also did graphs using bias wieghts where it takes more flips for the graph to have the bouncing behavior. The biologist suspect that the length of the flips effects the probability of making the flips. Then you would compare mathematical graph to the biologist graph to see the bias.
| + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpalmostadelman.m 1. Adelman's False Positives with 12 nodes] |
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| | + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpadelman.m 2. Adelman's False Positives with 14 nodes] |
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| − | MATLAB programs that we developed using Markov Chains
| + | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fpweighter.m 3. Counter Program for Adelman's False Positives] |
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/fliplength.m 1.Flip Length]
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/pureflip.m 2.Pure Flip]
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| − | [http://gcat.davidson.edu/iGEM07/For_Missouri_Western/biasweighter.m 3.Bias Weighter]
| + | =Possion Model For the Number of Plasmids= |
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| | Using this statistical method we used to make a chart of the probability of finding true positives based on the number of plasmids. | | Using this statistical method we used to make a chart of the probability of finding true positives based on the number of plasmids. |
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| − | =Possion Model For the Number of Plasmids=
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One of the first projects we worked on was to develop a mathematical model of graphs for the pancake problem. These graphs were used to find the percent of plasmids that solved problem based on the number of flips. We also developed graphs with biases to see if the length of the flips effect the probability of making the flip. In some of the graphs we saw bouncing because from a starting point you can only get to a solution by an odd number of flips or an even number of flips. If you get to a point on an even number of flips you can never get to an odd number. When the graph shows convergence, regardless whether the number of flips is even or odd you will have a chance to get to one of the solutions. The convergence occurs at .25 because there are 2/8 chances of getting to the solution after a high number of flips which reduces to ¼. We also did graphs using bias wieghts where it takes more flips for the graph to have the bouncing behavior. The biologist suspect that the length of the flips effects the probability of making the flips. Then you would compare mathematical graph to the biologist graph to see the bias.
Using this statistical method we used to make a chart of the probability of finding true positives based on the number of plasmids.
