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INVESTIGATION ON MODULARITY AND DYNAMICS IN SIGNALING NETWORKS

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Abstract
Although there have been many studies revealing that dynamic robustness of a
biological network is related to its modularity characteristics, no proper tool exists to
investigate the relation between network dynamics and modularity. Accordingly, I
developed a novel Cytoscape app called MORO, which can conveniently analyze the
relationship between network modularity and robustness. I employed an existing
algorithm to analyze the modularity of directed graphs and a Boolean network model
for robustness calculation. In particular, to ensure the robustness algorithm’s
applicability to large-scale networks, I implemented it as a parallel algorithm by using
the OpenCL library. A batch -mode simulation function was also developed to verify
whether an observed relationship between modularity and robustness is conserved in a
large set of randomly structured networks. The app provides various visualization
modes to better elucidate topological relations between modules, and tabular results of
centrality and gene ontology enrichment analyses of modules. I tested the proposed
app to analyze large signaling networks and showed an interesting relationship
between network modularity and robustness. My app can be a promising tool which
efficiently analyzes the relationship between modularity and robustness in large
signaling networks.
Secondly, biological networks consisting of molecular components and
interactions are represented by a graph model. There have been some studies based on
that model to analyze a relationship between structural characteristics and dynamical
behaviors in signaling network. However, little attention has been paid to changes of
modularity and robustness in mutant networks. Therefore, I investigated the changes
of modularity and robustness by edge-removal mutations in three signaling networks.
I first observed that both the modularity and robustness increased on average in the
mutant network by the edge-removal mutations. However, the modularity change was
negatively correlated with the robustness change. This implies that it is unlikely that
both the modularity and the robustness values simultaneously increase by the edgeremoval mutations. Another interesting finding is that the modularity change was
positively correlated with the degree, the number of feedback loops, and the edge
betweenness of the removed edges whereas the robustness change was negatively
correlated with them. I note that these results were consistently observed in randomly
structure networks. Additionally, I identified two groups of genes which are inciden t
to the highly -modularity-increasing and the highly -robustness-decreasing edges with
respect to the edge-removal mutations, respectively, and observed that they are likely
to be central by forming a connected component of a considerably large size. The
gene-ontology enrichment of each of these gene groups was significantly different
from the rest of genes. Finally, I showed that the highly -robustness-decreasing edges
can be promising edgetic drug-targets, which validates the usefulness of my analysis.
Taken together, the analysis of changes of robustness and modularity against edgeremoval mutations can be useful to unravel novel dynamical characteristics
underlying in signaling networks.
Author(s)
쯔엉 꽁 도안
Issued Date
2017
Awarded Date
2018-02
Type
Dissertation
URI
https://oak.ulsan.ac.kr/handle/2021.oak/6235
http://ulsan.dcollection.net/common/orgView/200000006632
Affiliation
울산대학교
Department
일반대학원 전기전자컴퓨터공학과
Advisor
Yung Keun Kwon
Degree
Doctor
Publisher
울산대학교 일반대학원 전기전자컴퓨터공학과
Language
eng
Rights
울산대학교 논문은 저작권에 의해 보호받습니다.
Appears in Collections:
Computer Engineering & Information Technology > 2. Theses (Ph.D)
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