Student: | Priscilla Lo |
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Office: | CoRE 434, Wright-Rieman A204 |

School: | Rutgers University |

E-mail: | plo@reu.dimacs.rutgers.edu |

Project: | Chromatin Folding |

Gene expression often entails bringing together two distant genomic sites. For example, in eukaryotes DNA wraps around histones forming nucleosomes which are then combined to form chromatin. The string of nucleosomes forms loops of hundreds of base pairs which is a mechanism influencing gene expression. This chromatin architecture is controlled by the spacing and position of the nucleosomes on the DNA which is determined by DNA linker length. By studying the interactions between nucleosomes through covariance analysis, we can determine the influence of nucleosome spacing on chromatin fibers. These results will help us understand the mechanical and physical properties of chromatin.

- Week 1: After meeting with the other participants in the program, I determined some of the distances betweeen nucleosomes from data I had previously been working with. On Friday, I gave a presentation on what I will be working on over the summer.
- Week 2: Currently, I am using data sets of chromatin containing 11 nucleosomes. Using the parameters describing the rotation and translation between nucleosome pairs which I calculated last summer, I obtained matrices with the mean and standard deviations for the angle of rotation and spherical coordinates describing the axis of rotation between a nucleosome pair (i,j) for the different linker lengths.
- Week 3: The parameters describing the interaction between a nucleosome pair (i,j) consists of the angle of rotation and spherical coordinates describing the axis of rotation and translation between a nucleosome pair. Using the circular mean for the angles calculated in these parameters, I determined the covariance matrix of the parameters for the different pairs of nucleosomes.
- Week 4: I made arrays with the covariance matrix of the parameters for different pairs of nucleosomes. This consisted of the covariance matrices obtained through the circular mean and not using the circular mean. We want to compare the results to see any differences in covariance when the circular mean is used in our calculation. Also, I began obtaining the covariance matrices using the circular mean method and the means of the parameters on data sets of chromatin containing 20 nucleosome.
- Week 5: I revised the code for obtaining the covariance matrices using the circular mean method and finished obtaining the covariance matrices for the data sets containing 20 nucleosomes with linker lengths ranging from 15bp to 80bp. Also, I made plots for the means of each parameter to determine how the properties of the parameters change as the number of linker lengths increase.
- Week 6: From the global covariance matrices, the eigenvectors and eigenvalues for each of the 6x6 parameters covariance matrix for a nucleosome pair (i,j) were obtained. Different plots were made to analyze the distributions of the first eigenvalues obtained inorder to determine.
- Week 7: I worked on my second presentation for the results I obtained so far. I began working on obtaining the nucleosome frames from the parameters that I calculated because this can then be used to build 3D models describing the motion of one nucleosome frame with respect to the other.
- Week 8: I built 3D graphics describing the relationship between a pair of nucleosomes. This was done through rebuilding the nucleosome frame i+1 by using a given frame i and the mean parameters between a nucleosome pair i and i+1. With the use of the eigenvectors, I was able to rebuild more frames and visually see the motion of the nucleosome.

- My Mentors