Communication between layers in biological transcriptional networks
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Moe Win and Pamela Silver.
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Chromatin-immunoprecipitation experiments in combination with microarrays (known as ChIP-chip) have recently allowed biologists to map where proteins bind in the yeast genome. The combinatorial binding of different proteins at or near a gene controls the transcription (copying) of a gene and the production of the functional RNA or protein that the gene encodes. Therefore, ChIP-chip data provides powerful insight on how genes and gene products (i.e., proteins, RNA) interact and regulate one another in the underlying network of the cell. Much of the current work in modeling yeast transcriptional networks focuses on the regulatory effect of a class of proteins known as transcription factors (TF). However, other sets of factors also influence transcription, including histone modifications and states (HS), histone modifiers (HM) and remodelers, nuclear processing (NP), and nuclear transport (NT) proteins. In order to gain a holistic understanding of the non-linear process of transcription, our work examines the communication between all five forementioned classes (or layers) of regulators. We use vastly available rich-media ChIP-chip data for various proteins within the five classes to model a multi-layered transcriptional network of the yeast species Saccharomyces cerevisiae.(cont.) Following the introduction in Chapter 1, Chapter 2 describes the non-trivial process of incorporating the different sources of data into a coherent set and normalizing the heterogeneous data to improve biological accuracy. Using the normalized data, Chapter 3 finds biologically meaningful pairwise statistics between proteins, including filtered correlation coefficient, and mutual information p-values. It then combines the p-values of the two complementary approaches in order to increase the reliability of our predictions. Chapter 4 uncovers group-wise relationships between proteins using a novel semi-supervised clustering algorithm that preserves information about elements of a cluster in order to better capture group-wise dependencies. Throughout the theoretical analysis, we confirm various known biological processes and uncover several novel hypotheses. Based on the developed methodology, Chapter 5 builds a multi-layered transcriptional network and quantifies the communication between levels in biological transcriptional networks.
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections."May 2005."Includes bibliographical references (leaves 87-89).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.