Network medicine: a network-based approach to human diseases.

Permanent URL: http://hdl.handle.net/2047/D20193648
Title:
Network medicine : a network-based approach to human diseases
Creator:
Ghiassian, Susan Dina (Author)
Contributor:
Barabási, Albert-László (Advisor)
Chasman, Daniel (Committee member)
Karma, Alain (Committee member)
Vespignani, Alessandro, 1965- (Committee member)
Language:
English
Publisher:
Boston, Massachusetts : Northeastern University, 2015
Date Accepted:
March 2015
Date Awarded:
May 2015
Type of resource:
Text
Genre:
Dissertations
Format:
electronic
Digital origin:
born digital
Abstract/Description:
With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics.

Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology.

Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the statistical methods, detailed data curation processes and additional analyses performed to accomplish the previous parts.
Subjects and keywords:
network medicine
network science
Systems biology -- Mathematical models
Computational biology -- Mathematical models
Biological systems -- Mathematical models
Cells -- Physiology
Proteins -- Diseases
Self-organizing systems
DOI:
https://doi.org/10.17760/D20193648
Permanent Link:
http://hdl.handle.net/2047/D20193648
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