Biological phenomena in the cells can be explained in terms of the interactions among
biological macro-molecules, e.g., DNAs, RNAs and proteins. These interactions can
be modeled by genetic regulatory networks (GRNs). This dissertation proposes to
reverse engineering the GRNs based on heterogeneous biological data sets, including
time-series and time-independent gene expressions, Chromatin ImmunoPrecipatation
(ChIP) data, gene sequence and motifs and other possible sources of knowledge. The
objective of this research is to propose novel computational methods to catch pace
with the fast evolving biological databases.
Signal processing techniques are exploited to develop computationally efficient,
accurate and robust algorithms, which deal individually or collectively with various
data sets. Methods of power spectral density estimation are discussed to identify
genes participating in various biological processes. Information theoretic methods are
applied for non-parametric inference. Bayesian methods are adopted to incorporate several sources with prior knowledge. This work aims to construct an inference system
which takes into account different sources of information such that the absence of some
components will not interfere with the rest of the system.
It has been verified that the proposed algorithms achieve better inference accuracy
and higher computational efficiency compared with other state-of-the-art schemes,
e.g. REVEAL, ARACNE, Bayesian Networks and Relevance Networks, at presence
of artificial time series and steady state microarray measurements. The proposed algorithms
are especially appealing when the the sample size is small. Besides, they are
able to integrate multiple heterogeneous data sources, e.g. ChIP and sequence data,
so that a unified GRN can be inferred. The analysis of biological literature and in
silico experiments on real data sets for fruit fly, yeast and human have corroborated
part of the inferred GRN. The research has also produced a set of potential control
targets for designing gene therapy strategies.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2952 |
| Date | 15 May 2009 |
| Creators | Zhao, Wentao |
| Contributors | Dougherty, Edward R., Serpedin, Erchin |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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