You searched for subject:(Biological networks).
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University of Sydney
1.
McGrane, Martin.
Biological Network Distances
.
Degree: 2016, University of Sydney
URL: http://hdl.handle.net/2123/17233 
► Networks of interactions are increasingly used to model biological systems. The patterns of these networks capture a larger, more complex, representation of the whole than…
(more)
▼ Networks of interactions are increasingly used to model biological systems. The patterns of these networks capture a larger, more complex, representation of the whole than any single attribute can. Networks allow the modelling of far more complicated systems, at the expense of more computationally complex analysis. The networks of biological entities share common aspects. They mutate, and they mutate in a similar fashion. These mutations can be accurately measured, but accurately measuring the effect of a mutation on the overall network is beyond current understanding. Tools to find similarities between biological networks exist, but they focus on mapping the parts of one network to those of another. This is very useful and has found important relationships to inspire research, however, it does not address the problem of estimating distances between networks. In this thesis I develop a model of evolution in terms of network structure. This model represents biologically relevant mutations in terms of their effect on the network. With this an estimate of a distance between the biological entities can be found in terms of the number of mutations needed to mutate a network into another, or mutate an unknown ancestor into two known networks. This contribution responds to the need for tools that can use complex biological networks as a basis for estimating distances between organisms. With this we can develop more accurate models of their evolution and better understand their links. With this we can find shared network patterns that let us transfer our knowledge of one system to another. Using this model, I develop implementations to effectively estimate a distance between biological networks. The validity of the implementations are tested on simulated data with a known, evolutionary history. The evolutionary relationship between the protein interaction networks of the most well studied organisms is also shown, validated by the established phylogeny.
Subjects/Keywords: bNED;
biological networks
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APA (6th Edition):
McGrane, M. (2016). Biological Network Distances
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17233
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
McGrane, Martin. “Biological Network Distances
.” 2016. Thesis, University of Sydney. Accessed April 18, 2021.
http://hdl.handle.net/2123/17233.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
McGrane, Martin. “Biological Network Distances
.” 2016. Web. 18 Apr 2021.
Vancouver:
McGrane M. Biological Network Distances
. [Internet] [Thesis]. University of Sydney; 2016. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2123/17233.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
McGrane M. Biological Network Distances
. [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/17233
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Illinois – Chicago
2.
Ma, Chihua.
Visual Analysis Techniques for Dynamic Biological Networks.
Degree: 2018, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/22644
► Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological…
(more)
▼ Due to the complexity of
biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying
biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their
biological data through visual exploration and analysis. In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic
biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple
biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing
biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.
Advisors/Committee Members: Marai, G. Elisabeta (advisor), Kenyon, Robert (advisor), Johnson, Andrew (committee member), Forbes, Angus (committee member), Munzner, Tamara (committee member), Marai, G. Elisabeta (chair).
Subjects/Keywords: Visual Analysis; Dynamic Biological Networks
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APA (6th Edition):
Ma, C. (2018). Visual Analysis Techniques for Dynamic Biological Networks. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22644
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ma, Chihua. “Visual Analysis Techniques for Dynamic Biological Networks.” 2018. Thesis, University of Illinois – Chicago. Accessed April 18, 2021.
http://hdl.handle.net/10027/22644.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ma, Chihua. “Visual Analysis Techniques for Dynamic Biological Networks.” 2018. Web. 18 Apr 2021.
Vancouver:
Ma C. Visual Analysis Techniques for Dynamic Biological Networks. [Internet] [Thesis]. University of Illinois – Chicago; 2018. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10027/22644.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ma C. Visual Analysis Techniques for Dynamic Biological Networks. [Thesis]. University of Illinois – Chicago; 2018. Available from: http://hdl.handle.net/10027/22644
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Victoria
3.
Quee, Graham.
Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks.
Degree: Department of Mathematics and Statistics, 2019, University of Victoria
URL: http://hdl.handle.net/1828/10746
► In models for networks of regulatory interactions in biological molecules, the sigmoid relationship between concentration of regulating bodies and the production rates they control has…
(more)
▼ In models for
networks of regulatory interactions in
biological molecules, the sigmoid relationship between concentration of regulating bodies and the production rates they control has lead to the use of continuous time 'switching' ordinary differential equations (ODEs), sometimes referred to as Glass
networks. These Glass
networks are the result of a simplifying assumption that the switching behaviour occurs instantaneously at particular threshold values. Though this assumption produces highly tractable models, it also causes analytic difficulties in certain cases due to the discontinuities of the system, such as non-uniqueness. In this thesis we explore the use of 'ramp' functions as an alternative approximation to the sigmoid, which restores continuity to the ODE and removes the assumption of infinitely fast switching by linearly interpolating the focal point values used in a corresponding Glass network. A general framework for producing a ramp system from a certain Glass network is given. Solutions are explored in two dimensions, and then in higher dimensions under two different restrictions. Periodic behaviour is explored in both cases using mappings between threshold boundaries. Limitations in these methods are explored, and a general proof of the existence of periodic solutions in negative feedback loops is given.
Advisors/Committee Members: Edwards, Roderick (supervisor).
Subjects/Keywords: Glass Networks; ODE; Gene regulation; Biological Regulation; Biological models; switching networks
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APA ·
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APA (6th Edition):
Quee, G. (2019). Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/10746
Chicago Manual of Style (16th Edition):
Quee, Graham. “Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks.” 2019. Masters Thesis, University of Victoria. Accessed April 18, 2021.
http://hdl.handle.net/1828/10746.
MLA Handbook (7th Edition):
Quee, Graham. “Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks.” 2019. Web. 18 Apr 2021.
Vancouver:
Quee G. Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks. [Internet] [Masters thesis]. University of Victoria; 2019. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1828/10746.
Council of Science Editors:
Quee G. Ramp approximations of finitely steep sigmoid control functions in soft-switching ODE networks. [Masters Thesis]. University of Victoria; 2019. Available from: http://hdl.handle.net/1828/10746
University of Minnesota
4.
Koch, Elizabeth.
Computational analysis of genetic interaction network structures and gene properties.
Degree: PhD, Computer Science, 2017, University of Minnesota
URL: http://hdl.handle.net/11299/190545
► Cellular systems are responsible for many complex tasks, such as carrying out cell cycle phases, responding to intra- and extra-cellular conditions, and resolving errors. Through…
(more)
▼ Cellular systems are responsible for many complex tasks, such as carrying out cell cycle phases, responding to intra- and extra-cellular conditions, and resolving errors. Through analysis of biological networks, researchers have begun to describe how cells coordinate these processes by means of modularity and between-process connections. However, descriptions of this network-based cellular organization often do not incorporate the diverse characteristics and individual behaviors of the genes that compose it. Knowledge of gene properties and their relationships with biological network evolution is crucial for a complete understanding of cellular function, and investigation in this area can lead to general principles of biology that apply to many species. This dissertation will describe analyses of the Saccharomyces cerevisiae (baker’s yeast) genetic interaction network that connect gene topological behavior with various physical, functional, and evolutionary properties of genes. Genetic interactions occur between paired genes whose simultaneous mutations produce unexpected double-mutant phenotypes, which are indicative of a range of functional relationships. Because genetic interactions can be identified genome-wide in high-throughput experiments, their networks are comprehensive and unbiased representations of function to which we can apply computational methods that search for structure-function relationships. We begin by exploring the association between a set of gene properties and gene genetic interaction (GI) degree. Here, we build a decision tree model that sorts genes based on a set of properties, each of which has a correlation with GI degree, and accurately predicts GI degree. We show that our model, trained on S. cerevisiae, is also accurate for a very distant yeast species, Schizosaccharomyces pombe, demonstrating that the rules governing gene connectivity are well conserved. Finally, we used predictions from the model to identify gene modules that differ between the two yeast species. Next, we further characterize hub genes through an investigation of pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects. Pleiotropy has typically been described by counting organism-level phenotypes, but a characterization based on genetic interactions can capture details about cellular processes that are buffered by the cell and never manifest in single mutant cellular phenotypes. For this analysis, we use frequent item set mining to discover GI modules, which we annotate with high-level processes, and use entropy to measure the functional diversity of each gene’s set of containing modules, thus distinguishing between genes whose functional influence is limited to very few bioprocesses and those whose roles are important for varied cellular functions. We identified a number of gene and protein characteristics that differed between genes with high and low pleiotropy and discuss the implications of these results regarding the nature and evolution of pleiotropy.
Subjects/Keywords: Biological networks; Genetic interactions; Genomics; Pleiotropy; Yeast
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APA (6th Edition):
Koch, E. (2017). Computational analysis of genetic interaction network structures and gene properties. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/190545
Chicago Manual of Style (16th Edition):
Koch, Elizabeth. “Computational analysis of genetic interaction network structures and gene properties.” 2017. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021.
http://hdl.handle.net/11299/190545.
MLA Handbook (7th Edition):
Koch, Elizabeth. “Computational analysis of genetic interaction network structures and gene properties.” 2017. Web. 18 Apr 2021.
Vancouver:
Koch E. Computational analysis of genetic interaction network structures and gene properties. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11299/190545.
Council of Science Editors:
Koch E. Computational analysis of genetic interaction network structures and gene properties. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/190545
5.
Westbrook, Alexandra Michon.
Characterization of RNA Genetic Regulators and Synthetic Networks.
Degree: PhD, Chemical Engineering, 2018, Cornell University
URL: http://hdl.handle.net/1813/59541
► A central tenent of synthetic biology is the ability to predictably engineer complex patterns of gene expression. This fined tuned control allows us to reprogram…
(more)
▼ A central tenent of synthetic biology is the ability to predictably engineer complex patterns of gene expression. This fined tuned control allows us to reprogram organisms with sophisticated synthetic behaviors such as producing vital chemicals and drugs and sensing environmental signals. In order to do this we need libraries of highly efficient genetic regulators and proven methods of combining them into
networks. RNA presents the ideal tool to build new genetic
networks because its structural and temporal characteristics allow engineers to construct fast, designable genetic
networks. In this work, we show characterization and optimization of new and existing RNA regulators as well as efforts to create new behaviors with RNA-based genetic
networks. We begin by vastly improving the dynamic range of an existing transcriptional RNA regulator, the pT181 attenuator, by adding translational regulation. This dual control attenuator is successfully used to reduce circuit leak in an RNA-only cascade. In order to expand upon the functionality of the RNA repressors, we design sequesters that allow us to dial down repression. The sequestration effectively creates a threshold which we use to tune the relationship between the input and output of a system. As we construct more complex circuits with diverse parts, modularity becomes essential in order to predict circuit behavior. We explore the modularity of our RNA regulators in combination with clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) in construction of an RNA pulse generator. Finally, we explore the design and implementation two complex circuits: a communication network for delivering complex signals to cells and a control network to reduce noise in
biological systems. We anticipate that the design rules learned and the tools developed here will allow construction of even more sophisticated behaviors as the growing discipline of genetic design matures.
Advisors/Committee Members: Lucks, Julius (chair), Stroock, Abraham Duncan (committee member), Daniel, Susan (committee member).
Subjects/Keywords: RNA; Biological control; Bioengineering; Biological Networks; synthetic biology
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APA (6th Edition):
Westbrook, A. M. (2018). Characterization of RNA Genetic Regulators and Synthetic Networks. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/59541
Chicago Manual of Style (16th Edition):
Westbrook, Alexandra Michon. “Characterization of RNA Genetic Regulators and Synthetic Networks.” 2018. Doctoral Dissertation, Cornell University. Accessed April 18, 2021.
http://hdl.handle.net/1813/59541.
MLA Handbook (7th Edition):
Westbrook, Alexandra Michon. “Characterization of RNA Genetic Regulators and Synthetic Networks.” 2018. Web. 18 Apr 2021.
Vancouver:
Westbrook AM. Characterization of RNA Genetic Regulators and Synthetic Networks. [Internet] [Doctoral dissertation]. Cornell University; 2018. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1813/59541.
Council of Science Editors:
Westbrook AM. Characterization of RNA Genetic Regulators and Synthetic Networks. [Doctoral Dissertation]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59541
Linnaeus University
6.
Köstinger, Harald.
ViNCent – Visualization of NetworkCentralities.
Degree: Physics and Mathematics, 2011, Linnaeus University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793
► In the area of information visualization social or biological networks are visualized ina way so that they can be explored easily and one can…
(more)
▼ In the area of information visualization social or biological networks are visualized ina way so that they can be explored easily and one can get more information about thestructure of the network out of it.
The use of network centralities in the field of network analysis plays an importantrole when it comes to the rating of the relative importance of vertices within the networkstructure based on the neighborhood of them. Such a single network can be renderedeasily by the use of standard graph drawing algorithms. But it is not only the explorationof one centrality which is important. Furthermore, the comparison of two or more of themis important to get some further meaning out of it. When visualizing the comparisonof two or more network centralities we are facing new problems of how to visualizethem in a way to get out the most meaning of it. We want to be able to track all thechanges in the networks between two centralities as well as visualize the single networksas best as possible. In the life sciences centrality measures help scientists to understand theunderlying biological processes and have been successfully applied to different biologicalnetworks.
The aim of the thesis is it to overcome those problems and to come up with a new solutionof how to visualize networks and its centralities. This thesis introduces a new way ofrendering networks including their centrality values along a circular view. Researches canthen be focused on the exploration of the centrality values including the network structure,without dealing with visual clutter or occlusions of nodes. Furthermore, filtering based instatistical data concerning the datasets and centrality values support this.
Subjects/Keywords: centralities; network analysis; visualization; social networks; biological networks; graph drawing
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APA ·
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APA (6th Edition):
Köstinger, H. (2011). ViNCent – Visualization of NetworkCentralities. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Köstinger, Harald. “ViNCent – Visualization of NetworkCentralities.” 2011. Thesis, Linnaeus University. Accessed April 18, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Köstinger, Harald. “ViNCent – Visualization of NetworkCentralities.” 2011. Web. 18 Apr 2021.
Vancouver:
Köstinger H. ViNCent – Visualization of NetworkCentralities. [Internet] [Thesis]. Linnaeus University; 2011. [cited 2021 Apr 18].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Köstinger H. ViNCent – Visualization of NetworkCentralities. [Thesis]. Linnaeus University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-10793
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Notre Dame
7.
Soon-Hyung Yook.
From the Topology to the Dynamics of Complex
Networks</h1>.
Degree: Physics, 2004, University of Notre Dame
URL: https://curate.nd.edu/show/nv935140w9x
► Understanding the mechanisms governing the behavior of complex networks is a prerequisite for characterizing complex systems. Frequently, networks are modelled as unweighted graphs in…
(more)
▼ Understanding the mechanisms governing the
behavior of complex
networks is a prerequisite for characterizing
complex systems. Frequently,
networks are modelled as unweighted
graphs in which each link has the same strength. However, for many
real
networks appearing in
biological, technological and economic
systems, each link has a specific weight, as nodes interact with
each other with different strengths. In order to extend our
understanding of network architecture to such systems, we introduce
several weighted network models and investigate their scaling
properties. In some real systems each node has a fixed geographical
location, forcing some nodes to be connected by physical links of
considerable length, such as routers connected by wires on the
Internet. In such systems, we find that the physical layout of the
underlying network strongly impacts the large-scale properties of
the network. By combining data from several empirical databases and
results from numerical simulations, we uncover the existence of
three universal mechanisms which significantly affect the network’s
global properties. As an application of complex network theory, we
also study the large-scale properties of four yeast-protein
interaction databases, finding quantitative evidence of strong
correlations between the underlying network’s structure and protein
classes and function. To move beyond the topological properties of
networks, we investigate the dynamics of a diffusive system driven
by multiplicative noise, uncovering the relationship between the
fluctuations of the incoming fluxes and the underlying network
topology.
Advisors/Committee Members: Paul J. McGinn, Committee Chair, G. Jones, Committee Member, K. Newman, Committee Member, A.-L. Barabasi, Committee Member, D. Balsara, Committee Member.
Subjects/Keywords: Random Networks; Biological Networks
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APA ·
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APA (6th Edition):
Yook, S. (2004). From the Topology to the Dynamics of Complex
Networks</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/nv935140w9x
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yook, Soon-Hyung. “From the Topology to the Dynamics of Complex
Networks</h1>.” 2004. Thesis, University of Notre Dame. Accessed April 18, 2021.
https://curate.nd.edu/show/nv935140w9x.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yook, Soon-Hyung. “From the Topology to the Dynamics of Complex
Networks</h1>.” 2004. Web. 18 Apr 2021.
Vancouver:
Yook S. From the Topology to the Dynamics of Complex
Networks</h1>. [Internet] [Thesis]. University of Notre Dame; 2004. [cited 2021 Apr 18].
Available from: https://curate.nd.edu/show/nv935140w9x.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yook S. From the Topology to the Dynamics of Complex
Networks</h1>. [Thesis]. University of Notre Dame; 2004. Available from: https://curate.nd.edu/show/nv935140w9x
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
8.
Correia, Fernanda Maria dos Reis Brito e Rodrigues.
Prediction and analysis of biological networks structure and dynamics
.
Degree: 2019, Universidade de Aveiro
URL: http://hdl.handle.net/10773/29200
► Increasing knowledge about the biological processes that govern the dynamics of living organisms has fostered a better understanding of the origin of many diseases as…
(more)
▼ Increasing knowledge about the
biological processes that govern the
dynamics of living organisms has fostered a better understanding of the
origin of many diseases as well as the identification of potential therapeutic
targets.
Biological systems can be modeled through
biological networks,
allowing to apply and explore methods of graph theory in their investigation
and characterization. This work had as main motivation the inference of
patterns and rules that underlie the organization of
biological networks.
Through the integration of different types of data, such as gene expression,
interaction between proteins and other biomedical concepts, computational
methods have been developed so that they can be used to predict and study
diseases.
The first contribution, was the characterization a subsystem of the human
protein interactome through the topological properties of the
networks that
model it. As a second contribution, an unsupervised method using
biological
criteria and network topology was used to improve the understanding of
the genetic mechanisms and risk factors of a disease through co-expression
networks. As a third contribution, a methodology was developed to remove
noise (denoise) in protein
networks, to obtain more accurate models, using
the network topology. As a fourth contribution, a supervised methodology
was proposed to model the protein interactome dynamics, using exclusively
the topology of protein interactions
networks that are part of the dynamic
model of the system.
The proposed methodologies contribute to the creation of more precise,
static and dynamic
biological models through the identification and use of
topological patterns of protein interaction
networks, which can be used to
predict and study diseases.
Advisors/Committee Members: Oliveira, José Luis Guimarães (advisor), Arrais, Joel Perdiz (advisor).
Subjects/Keywords: Biological networks;
Protein interaction networks;
Network topology;
Network modeling;
Computational biology
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APA (6th Edition):
Correia, F. M. d. R. B. e. R. (2019). Prediction and analysis of biological networks structure and dynamics
. (Thesis). Universidade de Aveiro. Retrieved from http://hdl.handle.net/10773/29200
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Correia, Fernanda Maria dos Reis Brito e Rodrigues. “Prediction and analysis of biological networks structure and dynamics
.” 2019. Thesis, Universidade de Aveiro. Accessed April 18, 2021.
http://hdl.handle.net/10773/29200.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Correia, Fernanda Maria dos Reis Brito e Rodrigues. “Prediction and analysis of biological networks structure and dynamics
.” 2019. Web. 18 Apr 2021.
Vancouver:
Correia FMdRBeR. Prediction and analysis of biological networks structure and dynamics
. [Internet] [Thesis]. Universidade de Aveiro; 2019. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10773/29200.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Correia FMdRBeR. Prediction and analysis of biological networks structure and dynamics
. [Thesis]. Universidade de Aveiro; 2019. Available from: http://hdl.handle.net/10773/29200
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – Irvine
9.
Patel Rajesh, Vishal.
Large Scale Integration, Analysis, and Visualization of Biological Data.
Degree: Computer Science, 2014, University of California – Irvine
URL: http://www.escholarship.org/uc/item/2wp2m4n5
► Data from decades of life sciences research and literature is being curated and made available for searching and analysis. While considerable work has been done…
(more)
▼ Data from decades of life sciences research and literature is being curated and made available for searching and analysis. While considerable work has been done to integrate and re-use this data, what is still lacking is a unifying platform that allows new experimental data to leverage all previously published data effectively. Crick is an intelligent and scalable platform for data integration, visualization, and searching for meaningful biological hypothesis. It was built to create an effective way to integrate and analyze experimental data in the context of the vast literature of other biologically relevant information. Crick was designed ground-up to solve some of the most challenging problems in biological data such as entity resolution, size, scale, reliability of the data, visualization of high-dimensional information, etc. Crick has been successfully used to identify molecular mechanism regulating circadian metabolism; to understand the complex coupling of circadian oscillating species; to study pediatric cancers; to analyze the dynamic long-range interactions in the genome; and more.
Subjects/Keywords: Computer science; Bioinformatics; big data in biology; biological data analysis; biological networks; data integration
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APA (6th Edition):
Patel Rajesh, V. (2014). Large Scale Integration, Analysis, and Visualization of Biological Data. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/2wp2m4n5
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Patel Rajesh, Vishal. “Large Scale Integration, Analysis, and Visualization of Biological Data.” 2014. Thesis, University of California – Irvine. Accessed April 18, 2021.
http://www.escholarship.org/uc/item/2wp2m4n5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Patel Rajesh, Vishal. “Large Scale Integration, Analysis, and Visualization of Biological Data.” 2014. Web. 18 Apr 2021.
Vancouver:
Patel Rajesh V. Large Scale Integration, Analysis, and Visualization of Biological Data. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2021 Apr 18].
Available from: http://www.escholarship.org/uc/item/2wp2m4n5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Patel Rajesh V. Large Scale Integration, Analysis, and Visualization of Biological Data. [Thesis]. University of California – Irvine; 2014. Available from: http://www.escholarship.org/uc/item/2wp2m4n5
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Virginia Tech
10.
Zhang, Bai.
Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data.
Degree: PhD, Electrical and Computer Engineering, 2011, Virginia Tech
URL: http://hdl.handle.net/10919/29111
► Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize…
(more)
▼ Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in
biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically dynamic changes in
biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput
biological data, and to demonstrate their applications in systems
biological studies.
The first part (Chapters 2-4) of the dissertation focuses on the dynamic changes taking placing at the
biological network level.
Biological networks are context-specific and dynamic in nature. Under different conditions, different regulatory components and mechanisms are activated and the topology of the underlying gene regulatory network changes. We report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional
networks between two
biological conditions. Further, we formalize and extend the DDN approach to an effective learning strategy to extract structural changes in graphical models using l1-regularization based convex optimization. We discuss the key properties of this formulation and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in
biological networks is the observation that a group of genes involved in certain
biological functions or processes coordinate to response to outside stimuli, producing distinct time course patterns. We apply the echo stat network, a new architecture of recurrent neural
networks, to model temporal gene expression patterns and analyze the theoretical properties of echo state
networks with random matrix theory.
The second part (Chapter 5) of the dissertation focuses on the changes at the DNA copy number level, especially in cancer cells. Somatic DNA copy number alterations (CNAs) are key genetic events in the development and progression of human cancers, and frequently contribute to tumorigenesis. We propose a statistically-principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately detect genomic deletion type, estimate normal tissue contamination, and accordingly recover the true copy number profile in cancer cells.
Advisors/Committee Members: Wang, Yue J. (committeechair), Xuan, Jianhua Jason (committee member), Baumann, William T. (committee member), Wang, Ge (committee member), Lu, Chang-Tien (committee member).
Subjects/Keywords: differential dependency networks; biological networks; echo state networks; DNA copy number changes; structural changes in graphical models
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APA (6th Edition):
Zhang, B. (2011). Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/29111
Chicago Manual of Style (16th Edition):
Zhang, Bai. “Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data.” 2011. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021.
http://hdl.handle.net/10919/29111.
MLA Handbook (7th Edition):
Zhang, Bai. “Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data.” 2011. Web. 18 Apr 2021.
Vancouver:
Zhang B. Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10919/29111.
Council of Science Editors:
Zhang B. Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/29111
Penn State University
11.
Saadatpour Moghaddam, Assieh.
Dynamic Modeling of Biological and Physical Systems.
Degree: 2012, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/15348
► Given the complexity and interactive nature of many biological and physical systems, constructing informative and coherent network models of these systems and subsequently developing efficient…
(more)
▼ Given the complexity and interactive nature of many
biological and physical systems, constructing informative and coherent network models of these systems and subsequently developing efficient approaches to analyze the models is of utmost importance. The combination of network modeling and dynamic analysis enables one to investigate the behavior of the underlying system as a whole and to make experimentally testable predictions about less-understood aspects of the processes involved. This dissertation reports on a combination of theoretical and computational approaches for network-based dynamic analysis of several highly interactive
biological and physical systems. Various dynamic modeling approaches, ranging from Boolean to continuous models, are employed to carry out a systematic analysis of the long-term behavior (attractors) of the respective systems. First, we employ a Boolean dynamic framework to model two
biological systems: the abscisic acid (ABA) signal transduction network in plants and the T-LGL leukemia signaling network in humans. Given the relatively large number of components in these
networks, we develop a network reduction technique leading to a significant decrease in the computational burden associated with the state space analysis of Boolean models while preserving essential dynamical features. For the ABA system, we utilize a synchronous and three different asynchronous Boolean dynamic methods and compare the attractors of the system and their basins of attraction for both unperturbed and perturbed systems. For the T-LGL signaling network, the best-performing asynchronous Boolean dynamic method identified in our first study is used to determine the disease states of the components of the system and to propose several novel candidate therapeutic targets. Next, we apply a Boolean-continuous hybrid (piecewise linear) dynamic formalism to model a pathogen-immune system interaction network, and present the results of a comparative study of the dynamic characteristics of Boolean and hybrid models. Finally, we rely on continuous dynamic modeling to prove the existence of traveling wave solutions in a better-characterized physical system, namely, a chain of coupled pendula in the presence of damping and forcing. Overall, the theoretical and computational approaches developed in this dissertation provide a bird’s-eye-view of the avenues available for model-driven analysis of complex
biological and physical systems.
Advisors/Committee Members: Reka Z Albert, Dissertation Advisor/Co-Advisor, Mark Levi, Dissertation Advisor/Co-Advisor, Reka Z Albert, Committee Chair/Co-Chair, Mark Levi, Committee Chair/Co-Chair, Andrew Leonard Belmonte, Committee Member, Timothy Reluga, Committee Member, John Fricks, Committee Member.
Subjects/Keywords: Dynamic modeling; Biological networks; Boolean models; Piecewise linear models
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APA (6th Edition):
Saadatpour Moghaddam, A. (2012). Dynamic Modeling of Biological and Physical Systems. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15348
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Saadatpour Moghaddam, Assieh. “Dynamic Modeling of Biological and Physical Systems.” 2012. Thesis, Penn State University. Accessed April 18, 2021.
https://submit-etda.libraries.psu.edu/catalog/15348.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Saadatpour Moghaddam, Assieh. “Dynamic Modeling of Biological and Physical Systems.” 2012. Web. 18 Apr 2021.
Vancouver:
Saadatpour Moghaddam A. Dynamic Modeling of Biological and Physical Systems. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Apr 18].
Available from: https://submit-etda.libraries.psu.edu/catalog/15348.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Saadatpour Moghaddam A. Dynamic Modeling of Biological and Physical Systems. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/15348
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Virginia Commonwealth University
12.
Thomas, Sterling.
A Novel Method to Detect Functional Subgraphs in Biomolecular Networks.
Degree: PhD, Integrative Life Sciences, 2010, Virginia Commonwealth University
URL: https://doi.org/10.25772/9WVX-AM44
;
https://scholarscompass.vcu.edu/etd/154
► Several biomolecular pathways governing the control of cellular processes have been discovered over the last several years. Additionally, advances resulting from combining these pathways into…
(more)
▼ Several biomolecular pathways governing the control of cellular processes have been discovered over the last several years. Additionally, advances resulting from combining these pathways into
networks have produced new insights into the complex behaviors observed in cell function assays. Unfortunately, identification of important subnetworks, or “motifs”, in these
networks has been slower in development. This study focused on identifying important network motifs and their rate of occurrence in two different biomolecular
networks. The two
networks evaluated for this study represented both ends of the spectrum of interaction knowledge by comparing a well defined network (apoptosis) with and poorly studied network that was early in development (autism). This study identified several motifs that could be important in governing and controlling cellular processes in healthy and diseased cells. Additionally, this study revealed an inverse relationship when comparing the occurrence rate of these motifs in apoptosis and autism.
Advisors/Committee Members: Danail Bonchev.
Subjects/Keywords: Biological Networks; Bioinformatics; Life Sciences
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APA (6th Edition):
Thomas, S. (2010). A Novel Method to Detect Functional Subgraphs in Biomolecular Networks. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/9WVX-AM44 ; https://scholarscompass.vcu.edu/etd/154
Chicago Manual of Style (16th Edition):
Thomas, Sterling. “A Novel Method to Detect Functional Subgraphs in Biomolecular Networks.” 2010. Doctoral Dissertation, Virginia Commonwealth University. Accessed April 18, 2021.
https://doi.org/10.25772/9WVX-AM44 ; https://scholarscompass.vcu.edu/etd/154.
MLA Handbook (7th Edition):
Thomas, Sterling. “A Novel Method to Detect Functional Subgraphs in Biomolecular Networks.” 2010. Web. 18 Apr 2021.
Vancouver:
Thomas S. A Novel Method to Detect Functional Subgraphs in Biomolecular Networks. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2010. [cited 2021 Apr 18].
Available from: https://doi.org/10.25772/9WVX-AM44 ; https://scholarscompass.vcu.edu/etd/154.
Council of Science Editors:
Thomas S. A Novel Method to Detect Functional Subgraphs in Biomolecular Networks. [Doctoral Dissertation]. Virginia Commonwealth University; 2010. Available from: https://doi.org/10.25772/9WVX-AM44 ; https://scholarscompass.vcu.edu/etd/154
13.
Δημητρακοπούλου, Κωνσταντίνα.
Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας.
Degree: 2013, University of Patras
URL: http://hdl.handle.net/10889/6965
► Στην εποχή της Συστημικής Ιατρικής, οι τεχνολογίες μαζικής καταγραφής της γονιδιακής και miRNA έκφρασης (π.χ. μικροσυστοιχίες, RNA-seq) αλλά και οι τεχνολογίες ανίχνευσης πρωτεϊνικών αλληλεπιδράσεων (π.χ.…
(more)
▼ Στην εποχή της Συστημικής Ιατρικής, οι τεχνολογίες μαζικής καταγραφής της γονιδιακής και miRNA έκφρασης (π.χ. μικροσυστοιχίες, RNA-seq) αλλά και οι τεχνολογίες ανίχνευσης πρωτεϊνικών αλληλεπιδράσεων (π.χ. yeast two-hybrid, co-immunoprecipitation) απελευθέρωσαν τεράστια ποσά δεδομένων για την αποσαφήνιση των μηχανισμών των πολύπλοκων ασθενειών. Η παρούσα διδακτορική διατριβή συμβάλλει προσφέροντας νέες υπολογιστικές μεθοδολογίες και εργαλεία και παραθέτοντας νέες αξιόπιστες βιολογικές υποθέσεις για την επίλυση σύνθετων ασθενειών του ανθρώπου.
Καταρχήν, αποκτήθηκε γνώση του θεωρητικού υπόβαθρου διάφορων μέγαλης κλίμακας μοριακών τεχνικών, τεχνικών εξόρυξης δεδομένων όπως η ομαδοποίηση καθώς και γραφοθεωρητικών προσεγγίσεων. Έπειτα, σχεδιάστηκε μια μεθοδολογία για συνδυασμό πρωτεωμικών και μεταγραφωμικών δεδομένων και αναπτύχθηκε ένα αλγόριθμος ομαδοποίησης γράφων, που ονομάζεται Detect Modules (DetMod), ο οποίος ανιχνεύει κοινοτήτες/υπο-δομές (modules) πρωτεϊνών με διακριτή βιολογική λειτουργία και έντονη δυναμική συσχέτιση σε επίπεδο έκφρασης. Η απόδοση και αξιοπιστία της μεθόδου εξετάστηκε και πιστοποιήθηκε στον απλό οργανισμό-μοντέλο Saccharomyces cerevisiae προτού εφαρμοστεί στην επίλυση προβλημάτων της φαρμακογονιδιωματικής όπως η απόκριση του μεταγραφήματος στην θεραπεία με ταμοξιφένη στην περίπτωση του θετικού στην απόκριση σε οιστρογόνα καρκίνου του μαστού. Αποτέλεσμα της μεθόδου είναι δυναμικοί βιοδείκτες της απόκρισης στην ταμοξιφένη με μορφή υπο-δομών αντί μεμονωμένων πρωτεϊνών.
Παράλληλα, στα πλαίσια της σύγχρονης βιβλιογραφίας όπου οι εμπλεκόμενοι μηχανισμοί του καρκίνου αλληλοεπικαλύπτονται με αυτούς της γήρανσης, μια προσαρμοσμένη μεθοδολογία ανάλογη με την προαναφερόμενη εφαρμόστηκε στη μελέτη του φαινομένου της γήρανσης. Τα αποτελέσματα της μεθόδου σε πολλαπλούς ιστούς του ποντικού, και σε δεύτερο στάδιο μεμονωμένα στον καρδιακό ιστό, ανέδειξαν ποια μοριακά μονοπάτια εμπλέκονται στη γήρανση όλων των ιστών και ποια εξειδικεύονται σε ένα μόνο ιστό. Στην περίπτωση του καρδιακού ιστού βιοδείκτες σε μορφή υπο-δομών αποτυπώνουν τα εμπλεκόμενα μονοπάτια αλλά και τη συνεργατική δράση και υπαιτιότητα των miRNA.
Σε επόμενο στάδιο μελετήθηκαν οι μηχανισμοί απόκρισης στη γρίπη Α (Η1Ν1) μέσω της ανακατασκευής Γονιδιακών Ρυθμιστικών Δικτύων (ΓΡΔ) που αναπαριστούν τις χρονικά μεταβαλλόμενες αιτιατές σχέσεις μεταξύ μοριακών μονοπατιών από χρονοσειρές γονιδιακής έκφρασης. Το χρονικά μεταβαλλόμενο ΓΡΔ προέκυψε μέσα από μια μέθοδο συνδυασμού πολλαπλών αλγορίθμων ανακατασκευής από διαφορετικές κλάσεις του μαθηματικού φορμαλισμού. Η μέθοδος προσέφερε νέα γνώση για τη συνδεσιμότητα των μοριακών μονοπατιών μέχρι και την 60η ημέρα μετά την εισβολή του ιού στον πνευμονικό ιστό του ποντικού από το στάδιο της φυσικής ανοσίας, στη χυμική ανοσία και τέλος στη διαδικασία αποκατάστασης.
Τέλος, παρουσιάζεται ο OLYMPUS, ένας νέος υβριδικός μη επιβλεπόμενος αλγόριθμος ομαδοποίησης που εφαρμόστηκε σε χρονοσειρές γονιδιακής έκφρασης σε απόκριση στη γρίπη Α (Η1Ν1). Ο OLYMPUS χρησιμοποιεί τον Διαφορεξελικτικό αλγόριθμο ως στρατηγική…
Advisors/Committee Members: Μπεζεριάνος, Αναστάσιος, Dimitrakopoulou, Konstantina, Μπεζεριάνος, Αναστάσιος, Νικηφορίδης, Γεώργιος, Δερματάς, Ευάγγελος, Σγάρμπας, Κυριάκος, Φλυτζάνης, Κωνσταντίνος, Ζαρκάδης, Ιωάννης, Πλαγιανάκος, Βασίλειος.
Subjects/Keywords: Βιολογικά δίκτυα; Συστημική βιολογία; 570.285; Biological networks; Systems biology
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APA ·
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APA (6th Edition):
Δημητρακοπούλου, . (2013). Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας. (Doctoral Dissertation). University of Patras. Retrieved from http://hdl.handle.net/10889/6965
Chicago Manual of Style (16th Edition):
Δημητρακοπούλου, Κωνσταντίνα. “Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας.” 2013. Doctoral Dissertation, University of Patras. Accessed April 18, 2021.
http://hdl.handle.net/10889/6965.
MLA Handbook (7th Edition):
Δημητρακοπούλου, Κωνσταντίνα. “Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας.” 2013. Web. 18 Apr 2021.
Vancouver:
Δημητρακοπούλου . Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας. [Internet] [Doctoral dissertation]. University of Patras; 2013. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10889/6965.
Council of Science Editors:
Δημητρακοπούλου . Ανάλυση και μοντελοποίηση βιολογικών δικτύων με χρήση δεδομένων από μεγάλης κλίμακας τεχνικές της μοριακής βιολογίας. [Doctoral Dissertation]. University of Patras; 2013. Available from: http://hdl.handle.net/10889/6965
14.
Yartseva Smidtas, Anastasia.
Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks.
Degree: Docteur es, Bioinformatique, 2007, Evry-Val d'Essonne
URL: http://www.theses.fr/2007EVRY0025
► Le domaine scientifique de la Biologie des Systèmes étudie les interactions entre les composantes d'un système biologique afin d'en comprendre son fonctionnement global. Au cours…
(more)
▼ Le domaine scientifique de la Biologie des Systèmes étudie les interactions entre les composantes d'un système biologique afin d'en comprendre son fonctionnement global. Au cours de cette these, nous avons d’abord utilisé des graphes simples. Cette approche a permis d' appréhender la manière dont un réseau biologique peut interagir avec son environnement, lui-même modélisé par un autre réseau. Nous avons ensuite défini le formalisme MIB (Model of Interactions in Biology) qui permet de définir, rechercher et étudier les motifs hétérogènes. Enfin pour approfondir l'étude de la structure et de la dynamique, nous avons proposé le formalisme MIN. MIN possède la structure bipartie de MIB, mais permet d'avoir des annotations beaucoup plus riches des noeuds et des arcs du réseau qui peuvent être utilisées pour la traduction des données automatiquement en d'autres formalismes couramment utilisés en modélisation biologique, tels que les équations différentielles ou la modélisation logique.
The scientific domain of the Systems Biology studies the interactions between the components of a biological system in order to understand its functioning as a whole. In this thesis, we first used searched to apprehend how a biological network, modelled as a simple graph, interact with its environment, modelled by another graph. Next, we have defined the MIB formalism (for Model of Interactions in Biology) that enables to model, to search and to study the heterogeneous motifs in biological networks. Finally, for deepening the study of structure and dynamics of biological networks, we have proposed the MIN formalism (for Modular Interaction Network). MIN inherited the bipartite structure of MIB, but also includes the richer annotations for nodes, arcs and possible states of the network, thus enabling the automatic translation of data contained in MIN into other formalisms commonly used in biology for dynamics modelling, such as logical networks, differential equations or Petri nets.
Advisors/Committee Members: Klaudel, Hanna (thesis director), Képès, François (thesis director).
Subjects/Keywords: Réseaux biologiques; Biological networks
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APA ·
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APA (6th Edition):
Yartseva Smidtas, A. (2007). Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks. (Doctoral Dissertation). Evry-Val d'Essonne. Retrieved from http://www.theses.fr/2007EVRY0025
Chicago Manual of Style (16th Edition):
Yartseva Smidtas, Anastasia. “Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks.” 2007. Doctoral Dissertation, Evry-Val d'Essonne. Accessed April 18, 2021.
http://www.theses.fr/2007EVRY0025.
MLA Handbook (7th Edition):
Yartseva Smidtas, Anastasia. “Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks.” 2007. Web. 18 Apr 2021.
Vancouver:
Yartseva Smidtas A. Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks. [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; 2007. [cited 2021 Apr 18].
Available from: http://www.theses.fr/2007EVRY0025.
Council of Science Editors:
Yartseva Smidtas A. Modélisation incrémentale des réseaux biologiques : Incremental modelling of biological networks. [Doctoral Dissertation]. Evry-Val d'Essonne; 2007. Available from: http://www.theses.fr/2007EVRY0025
15.
TRAN NGOC HIEU.
Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data.
Degree: 2013, National University of Singapore
URL: http://scholarbank.nus.edu.sg/handle/10635/47504
Subjects/Keywords: biological networks; motifs; subnetwork
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Manager
APA (6th Edition):
HIEU, T. N. (2013). Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/47504
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
HIEU, TRAN NGOC. “Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data.” 2013. Thesis, National University of Singapore. Accessed April 18, 2021.
http://scholarbank.nus.edu.sg/handle/10635/47504.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
HIEU, TRAN NGOC. “Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data.” 2013. Web. 18 Apr 2021.
Vancouver:
HIEU TN. Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data. [Internet] [Thesis]. National University of Singapore; 2013. [cited 2021 Apr 18].
Available from: http://scholarbank.nus.edu.sg/handle/10635/47504.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
HIEU TN. Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data. [Thesis]. National University of Singapore; 2013. Available from: http://scholarbank.nus.edu.sg/handle/10635/47504
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Illinois – Urbana-Champaign
16.
Deviprasad Rao, Vikyath.
Multiscale dynamics in honeybee societies.
Degree: PhD, Physics, 2016, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/95563
► In this dissertation, I examine the social organization of a model organism, the honeybee, at multiple scales. I begin in Part I at the microbial…
(more)
▼ In this dissertation, I examine the social organization of a model organism, the honeybee, at multiple scales. I begin in Part I at the microbial scale, by studying the relationship between the social caste of individuals and the microbes they harbour in their gastrointestinal tracts. Using 16S rRNA sequence data, I reconstruct the gut microbiomes of honeybees of different castes. I find that the microbiomes of two previously-uncharacterized social castes – drones and queens – contain the same bacteria as those in the guts of worker bees. However, despite this similarity, I show that the compositions of these bacteria in drones and queens are sufficiently different that their microbiomes can be distinguished from those of workers.
In Part II, I study the honeybee society at the level of its individual constituents, in particular, the set of foragers. I characterize the distribution of foraging activity across these individuals in the society, and find that this is highly skewed, with some individuals contributing much more to the activity of the colony than others. I establish these results in the framework used to describe the wealth of individuals in human society, and also characterize the temporal variation and resilience of foraging activity.
In Part III, I describe a system to track individual honeybees and their interactions inside a two-dimensional observation hive with high spatiotemporal resolution. At the level of individual honeybees, I study the temporal statistics of trophallaxis, an important social interaction that occurs in honeybee societies, and find that the distribution of trophallaxis durations is similar to the distribution of face-to-face interactions among humans. I propose a scaling argument to explain the scaling exponent of these distributions, and test the argument in simple random-walk models of proximity interactions. I then study the honeybee society at the collective scale of the trophallaxis interaction network, and find that although bees exhibit bursty patterns of trophallaxis just as humans do in communication, the dynamics of simulated spreading on the trophallaxis
networks is fast relative to randomized reference models, unlike in human temporal
networks.
Advisors/Committee Members: Goldenfeld, Nigel (advisor), Maslov, Sergei (Committee Chair), DeVille, Robert E (committee member), Kuehn, Seppe (committee member).
Subjects/Keywords: complex systems; biological physics; honey bee; social networks; microbiome; metagenomics
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APA (6th Edition):
Deviprasad Rao, V. (2016). Multiscale dynamics in honeybee societies. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95563
Chicago Manual of Style (16th Edition):
Deviprasad Rao, Vikyath. “Multiscale dynamics in honeybee societies.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021.
http://hdl.handle.net/2142/95563.
MLA Handbook (7th Edition):
Deviprasad Rao, Vikyath. “Multiscale dynamics in honeybee societies.” 2016. Web. 18 Apr 2021.
Vancouver:
Deviprasad Rao V. Multiscale dynamics in honeybee societies. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2142/95563.
Council of Science Editors:
Deviprasad Rao V. Multiscale dynamics in honeybee societies. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95563
University of Minnesota
17.
Schaefer, Robert.
Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits.
Degree: PhD, Biomedical Informatics and Computational Biology, 2015, University of Minnesota
URL: http://hdl.handle.net/11299/188892
► The recent availability of high-throughput technologies in agricultural species provides an opportunity to advance our understanding of complex, agronomically important traits. Genome wide association studies…
(more)
▼ The recent availability of high-throughput technologies in agricultural species provides an opportunity to advance our understanding of complex, agronomically important traits. Genome wide association studies (GWAS) have identified thousands of loci linked to these traits; however in most cases the causal genes remain unknown. Analysis of a single data type is typically unsatisfactory in explaining complex traits that exhibit variation across multiple levels of biological regulation. To address these issues, we developed a computational framework called Camoco (Co-analysis of molecular components) that systematically integrates loci identified by GWAS with gene co-expression networks to identify a focused set of candidate loci with functional coherence. This framework analyzes the overlap between candidate loci generated from GWAS and the co-expression interactions that occur between them and addresses several biological considerations important for integrating diverse data types. On average, using this integrated approach, candidate gene lists identified by GWAS were reduced by two orders of magnitude. By incorporating co-expression network information, we can rapidly evaluate hundreds of GWAS experiments, producing focused sets of candidates with both strong associations with the phenotype of interest as well as evidence for functional coherence in the co-expression network. Identifying these candidates in a systematic and integrated manner is an important step toward resolving genes responsible for agriculturally important traits.
Subjects/Keywords: arabidopsis; biological networks; Camoco; co-expression; computational biology; maize
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APA (6th Edition):
Schaefer, R. (2015). Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/188892
Chicago Manual of Style (16th Edition):
Schaefer, Robert. “Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021.
http://hdl.handle.net/11299/188892.
MLA Handbook (7th Edition):
Schaefer, Robert. “Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits.” 2015. Web. 18 Apr 2021.
Vancouver:
Schaefer R. Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11299/188892.
Council of Science Editors:
Schaefer R. Integrating Co-Expression Networks with GWAS to Detect Causal Genes For Agronomically Important Traits. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/188892
University of Georgia
18.
Tang, Xiaojia.
Computational systems biology for the biological clock of Neurospora crassa.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/25960
► Genetic networks have been applied to describe biological systems, e.g., the biological clock, from a systems biology perspective. A model-driven discovery process, Computing Life, is…
(more)
▼ Genetic networks have been applied to describe biological systems, e.g., the biological clock, from a systems biology perspective. A model-driven discovery process, Computing Life, is developed and used to identify an ensemble of genetic
networks to describe quantitatively the biological clock of the lowly bread mould Neurospora crassa for its light-responsive behavior through iterative cycles combining both experiments and computational simulations. Central to this discovery process is
a new methodology for the rational design of a Maximally Informative Next Experiment (MINE) based on the genetic network ensemble. In each cycle, the MINE approach is used to design the most informative new experiment for the biological goal of
discovering clock-controlled genes which is the outputs of the clock. The new experimental results are then added back to the data pool to provide more information to improve the estimates and predictions made by the genetic network ensemble. The
identified ensemble of light-responsive genetic networks is expanded trying to describe the temperature response of the N. crassa and has been proved to be sufficient to explain the wild type data under different temperatures.
Subjects/Keywords: genetic networks; biological clock; ensemble approach; maximally informative next experiment
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APA (6th Edition):
Tang, X. (2014). Computational systems biology for the biological clock of Neurospora crassa. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/25960
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Tang, Xiaojia. “Computational systems biology for the biological clock of Neurospora crassa.” 2014. Thesis, University of Georgia. Accessed April 18, 2021.
http://hdl.handle.net/10724/25960.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Tang, Xiaojia. “Computational systems biology for the biological clock of Neurospora crassa.” 2014. Web. 18 Apr 2021.
Vancouver:
Tang X. Computational systems biology for the biological clock of Neurospora crassa. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10724/25960.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Tang X. Computational systems biology for the biological clock of Neurospora crassa. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/25960
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
19.
Lim, Chulwoo.
Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/29905
► A fundamental and ubiquitous difficulty of systems biology is identifying relevant model parameters. A genetic network model of the biological clock of Neurospora crassa that…
(more)
▼ A fundamental and ubiquitous difficulty of systems biology is identifying relevant model parameters. A genetic network model of the biological clock of Neurospora crassa that is quantitatively consistent with the available RNA and protein
profiling data was proposed. However, the oscillating nature of biological models poses more challenge for identifying model parameters due to the high dimensional complex search space and computational cost of numerically solving ODEs. In this work, an
Evolutionary Algorithm leveraging the GPU architecture is proposed. Our implementation identified promising model parameters with a speedup of two orders of magnitude compared to the CPU implementation.
Subjects/Keywords: Evolutionary Computing; Biological networks; Graphical processing unit; Runge-Kutta method
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APA (6th Edition):
Lim, C. (2014). Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/29905
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lim, Chulwoo. “Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction.” 2014. Thesis, University of Georgia. Accessed April 18, 2021.
http://hdl.handle.net/10724/29905.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lim, Chulwoo. “Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction.” 2014. Web. 18 Apr 2021.
Vancouver:
Lim C. Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10724/29905.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lim C. Using massively parallel evolutionary computation on GPUS for biological circuit reconstruction. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/29905
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Virginia Tech
20.
Black, Jacob A.
Neural Networks For Phase Demodulation In Optical Interferometry.
Degree: MS, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/93263
► Neural Networks (NNs) (or 'deep' neural networks (DNNs)) have found great success in many applications across all fields of engineering, and in particular have found…
(more)
▼ Neural
Networks (NNs) (or 'deep' neural
networks (DNNs)) have found great success in many applications across all fields of engineering, and in particular have found recent success in the field of Photonics. In this work we discuss the application of NNs to making so-called 'phase' images of
biological cells and tissues (e.g. red blood cells, sperm cells). This is necessary for many
biological samples which are transparent under traditional bright field microscopy. We show that NNs are capable of quantifying the phase of these samples to produce images with higher contrast than possible in a typical microscope image. As an example, we introduce a particular phase microscopy system and study the application of NNs to this system. We show that the NNs are capable of providing solutions for this phase in situations where existing analytical techniques fail. The NNs are also capable of making more precise calculations of the phase than the traditional algorithms in many situations where either technique could be used. Therefore, NNs can provide simultaneously higher performance and more flexibility when designing phase microscopy systems.
Advisors/Committee Members: Zhu, Yizheng (committeechair), Zhu, Yunhui (committee member), Huang, Jia-Bin (committee member), Poon, Ting-Chung (committee member), Safaai-Jazi, Ahmad (committee member).
Subjects/Keywords: Phase imaging; Neural Networks; Machine Learning; Biological Imaging
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APA (6th Edition):
Black, J. A. (2019). Neural Networks For Phase Demodulation In Optical Interferometry. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93263
Chicago Manual of Style (16th Edition):
Black, Jacob A. “Neural Networks For Phase Demodulation In Optical Interferometry.” 2019. Masters Thesis, Virginia Tech. Accessed April 18, 2021.
http://hdl.handle.net/10919/93263.
MLA Handbook (7th Edition):
Black, Jacob A. “Neural Networks For Phase Demodulation In Optical Interferometry.” 2019. Web. 18 Apr 2021.
Vancouver:
Black JA. Neural Networks For Phase Demodulation In Optical Interferometry. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10919/93263.
Council of Science Editors:
Black JA. Neural Networks For Phase Demodulation In Optical Interferometry. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93263
Virginia Tech
21.
Pokrzywa, Revonda Maria.
Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information.
Degree: PhD, Genetics, Bioinformatics, and Computational Biology, 2009, Virginia Tech
URL: http://hdl.handle.net/10919/39938
► One of the primary goals of systems biology is to understand the complex underlying network of biochemical interactions which allow an organism to respond to…
(more)
▼ One of the primary goals of systems biology is to understand the complex underlying network of biochemical interactions which allow an organism to respond to environmental stimuli. Models of these
biological interactions serve as a tool to both codify current understanding of these interactions as well as a starting point for scientific discovery. Due to the massive amount of information which is required for this modeling process, systems biology studies must often attempt to construct models which reflect the whole of the system while having access to only partial information. In some cases, the missing information will not have a confounding effect on the accuracy of the model. In other cases, there is the danger that this missing information will make the model useless.
The focus of this thesis is to study the effect which missing information has on systems level studies within several different contexts. Specifically, we study two contexts : when the missing information takes the role of incomplete molecular interaction network knowledge and when it takes the role of unknown kinetic rate laws. These studies yield interesting results. We show that when metabolism is isolated from gene expression, the effects are not limited to those reactions under strong control by gene expression. Thus, incomplete understanding of molecular interaction
networks may have unexpected effects on the resulting analysis. We also reveal that under the conditions of the current study, mass action was shown to be the superior substitute when the true rate equations for a
biological system are unknown.
In addition to studying the effect of missing information in the aforementioned contexts, we propose a method for limiting the parameter search space of biochemical systems. Even in ideal scenarios where both the molecular interaction network and the relevant kinetic rate equations are known, obtaining appropriate estimates for the unknown system parameters can be challenging. By employing a method which limits the parameter search space, we are able to acquire estimates for parameter values which are much closer to the true values than those which could be obtained otherwise.
Advisors/Committee Members: Mendes, Pedro J. P. (committeechair), Hoeschele, Ina (committee member), Murali, T. M. (committee member), Shulaev, Vladimir (committee member), Laubenbacher, Reinhard C. (committee member).
Subjects/Keywords: Systems Biology; Metabolomics; Biological Networks
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APA (6th Edition):
Pokrzywa, R. M. (2009). Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/39938
Chicago Manual of Style (16th Edition):
Pokrzywa, Revonda Maria. “Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information.” 2009. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021.
http://hdl.handle.net/10919/39938.
MLA Handbook (7th Edition):
Pokrzywa, Revonda Maria. “Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information.” 2009. Web. 18 Apr 2021.
Vancouver:
Pokrzywa RM. Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information. [Internet] [Doctoral dissertation]. Virginia Tech; 2009. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10919/39938.
Council of Science Editors:
Pokrzywa RM. Systems Biology in an Imperfect World: Modeling Biological Systems with Incomplete Information. [Doctoral Dissertation]. Virginia Tech; 2009. Available from: http://hdl.handle.net/10919/39938
Rice University
22.
Peterson, Christine.
Bayesian graphical models for biological network inference.
Degree: PhD, Engineering, 2013, Rice University
URL: http://hdl.handle.net/1911/77444
► In this work, we propose approaches for the inference of graphical models in the Bayesian framework. Graphical models, which use a network structure to represent…
(more)
▼ In this work, we propose approaches for the inference of graphical models in the Bayesian framework. Graphical models, which use a network structure to represent conditional dependencies among random variables, provide a valuable tool for visualizing and understanding the relationships among many variables. However, since these
networks are complex systems, they can be difficult to infer given a limited number of observations. Our research is focused on development of methods which allow incorporation of prior information on particular edges or on the model structure to improve the reliability of inference given small to moderate sample sizes.
First, we propose an approach to graphical model inference using the Bayesian graphical lasso. Our method incorporates informative priors on the shrinkage parameters specific to each edge. We demonstrate through simulations that this method allows improved learning of the network structure when relevant prior information is available, and illustrate the approach on inference of the cellular metabolic network under neuroinflammation. This application highlights the strength of our method since the number of samples available is fairly small, but we are able to draw on rich reference information from publicly available databases describing known metabolic interactions to construct informative priors.
Next, we propose a modeling approach for settings where we would like to estimate
networks for a collection of possibly related sample groups, where the sample size for each subgroup may be limited. We use a Markov random field prior to link the graphs within each group, and a selection prior to infer which groups have shared network structure. This allows us to encourage common edges across sample groups, when supported by the data. We provide simulation studies to illustrate the properties of our method and compare its performance to competing approaches. We conclude by demonstrating use of the proposed method to infer protein
networks for various subtypes of acute myeloid leukemia and to infer signaling
networks under different experimental perturbations.
Advisors/Committee Members: Vannucci, Marina (advisor), Ensor, Katherine B. (committee member), Kavraki, Lydia E. (committee member), Maletic-Savatic, Mirjana (committee member), Stingo, Francesco C. (committee member).
Subjects/Keywords: Statistics; Graphical models; Bayesian inference; Informative priors; Biological networks
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APA (6th Edition):
Peterson, C. (2013). Bayesian graphical models for biological network inference. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/77444
Chicago Manual of Style (16th Edition):
Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Doctoral Dissertation, Rice University. Accessed April 18, 2021.
http://hdl.handle.net/1911/77444.
MLA Handbook (7th Edition):
Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Web. 18 Apr 2021.
Vancouver:
Peterson C. Bayesian graphical models for biological network inference. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1911/77444.
Council of Science Editors:
Peterson C. Bayesian graphical models for biological network inference. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/77444
23.
Pirayre, Aurélie.
Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images.
Degree: Docteur es, Signal, Image, Automatique, 2017, Université Paris-Est
URL: http://www.theses.fr/2017PESC1170
► Dans de nombreuses applications telles que la médecine, l'environnement ou les biotechnologies par exemple, la découverte de nouveau processus de régulations de gènes permet une…
(more)
▼ Dans de nombreuses applications telles que la médecine, l'environnement ou les biotechnologies par exemple, la découverte de nouveau processus de régulations de gènes permet une meilleure compréhension des réponses phénotypiques des cellules à des stimuli externes. Pour cela, il est alors d'usage de générer et d'analyser les données transcriptomiques issues d'expériences de types puces à ADN ou plus récemment de RNAseq. Ainsi, pour chaque gène d'un organisme d'étude placé dans différentes conditions expérimentales, un ensemble de niveau d'expression est obtenu. A partir de ces données, les mécanismes de régulation des gènes peuvent être obtenus à travers un ensemble de liens dans des graphes. Dans ces réseaux, les nœuds correspondent aux gènes. A lien entre deux nœuds est identifié si une relation de régulation existent entre les deux gènes correspondant. De tels réseaux sont appelés Réseaux de Régulation de Gènes (RRGs). Malgré la profusion de méthodes d'inférence disponible, leur construction et leur analyse restent encore à ce jour un défi.Dans cette thèse, nous proposons de répondre au problème d'inférence de réseaux par des techniques d'optimisation dans des graphes. A partir d'information de régulation sur l'ensemble des couples de gènes, nous proposons de déterminer la présence d'arêtes dans le RRG final en adoptant une formulation de fonction objectif intégrant des contraintes. Des a priori à la fois biologiques (sur les interactions entre les gènes) et structuraux (sur la connectivité des nœuds) ont été considérés pour restreindre l'espace des solutions possibles. Les différents a priori donnent des fonctions objectifs ayant des propriétés différentes, pour lesquelles des stratégies d'optimisation adaptées (continue et/ou discrète) peuvent être appliquées. Les post-traitement que nous avons développé ont mené à un ensemble de méthodes nommés BRANE, pour "Biologically-Related A priori for Network Enhancement". Pour chacune des méthodes développées (BRANE Cut, BRANE Relax et BRANE Clust), nos contributions sont triples : formulation de la fonction objectif à l'aide d'a priori, développement de la stratégie d'optimisation et validation (numérique et biologique) sur des données de parangonnage issues des challenges DREAM4 et DREAM5, montrant ainsi des améliorations pouvant atteindre 20%.En complément de l'inférence de réseaux, notre travail s'est étendu à des traitements de données sur graphe plus génériques, tels que les problèmes inverses. Nous avons notamment étudié HOGMep, une approche Bayésienne utilisant des stratégies d'approximation Bayésienne variationnelle. Cette méthode a été développée pour résoudre de façon conjointe, des problèmes de restauration et de classification sur des données multi-composantes (signaux et images). Les performances d'HOGMep dans un contexte de déconvolution d'image couleur montrent de bonnes qualités de reconstruction et de segmentation. Une étude préliminaire dans un contexte de classification de données médicales liant génotype et phénotype a également montré des résultats…
Advisors/Committee Members: Pesquet, Jean-Christophe (thesis director).
Subjects/Keywords: Réseaux biologiques; Graphes; A priori; Modèles biologiques; Optimisation; Biological networks; Graphs; A priori; Biological models; Optimization
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APA (6th Edition):
Pirayre, A. (2017). Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2017PESC1170
Chicago Manual of Style (16th Edition):
Pirayre, Aurélie. “Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images.” 2017. Doctoral Dissertation, Université Paris-Est. Accessed April 18, 2021.
http://www.theses.fr/2017PESC1170.
MLA Handbook (7th Edition):
Pirayre, Aurélie. “Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images.” 2017. Web. 18 Apr 2021.
Vancouver:
Pirayre A. Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images. [Internet] [Doctoral dissertation]. Université Paris-Est; 2017. [cited 2021 Apr 18].
Available from: http://www.theses.fr/2017PESC1170.
Council of Science Editors:
Pirayre A. Reconstruction et classification par optimisation dans des graphes avec à priori pour les réseaux de gènes et les images : Reconstruction and clustering with graph optimization and priors on gene networks and images. [Doctoral Dissertation]. Université Paris-Est; 2017. Available from: http://www.theses.fr/2017PESC1170
24.
Schuman, Catherine Dorothy.
Neuroscience-Inspired Dynamic Architectures.
Degree: 2015, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_graddiss/3361
► Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This…
(more)
▼ Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This work introduces neuroscience-inspired dynamic architectures (NIDA), spiking neural networks embedded in a geometric space that exhibit dynamic behavior. A neuromorphic hardware implementation based on NIDA networks, Dynamic Adaptive Neural Network Array (DANNA), is discussed. Neuromorphic implementations are one alternative/complement to traditional von Neumann computation. A method for designing/training NIDA networks, based on evolutionary optimization, is introduced. We demonstrate the utility of NIDA networks on classification tasks, a control task, and an anomaly detection task. There are known neural structures (such as cortical columns) that are repeated many times in the brain, and there are other structures that are useful for a variety of different tasks. We speculated that ``useful structures" will also emerge in NIDA networks. Three methods for identifying useful substructures within a NIDA network are presented: common structure, activity-based, and causality paths. We explored reusing activity-based useful substructures over the course of evolutionary optimization, but the results for those tests were inconclusive. One component of biological brains that is often ignored in biologically-inspired computation is the influence of affective, or emotion-related, systems. We define artificial affective systems and explore the effect of affective systems in NIDA networks in terms of behavior of the network and on the evolutionary optimization design method. We conclude with an outline of future research opportunities identified during this effort.
Subjects/Keywords: machine learning; neural networks; discrete event simulation; biological networks; optimization algorithms; Artificial Intelligence and Robotics; Theory and Algorithms
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APA (6th Edition):
Schuman, C. D. (2015). Neuroscience-Inspired Dynamic Architectures. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/3361
Chicago Manual of Style (16th Edition):
Schuman, Catherine Dorothy. “Neuroscience-Inspired Dynamic Architectures.” 2015. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed April 18, 2021.
https://trace.tennessee.edu/utk_graddiss/3361.
MLA Handbook (7th Edition):
Schuman, Catherine Dorothy. “Neuroscience-Inspired Dynamic Architectures.” 2015. Web. 18 Apr 2021.
Vancouver:
Schuman CD. Neuroscience-Inspired Dynamic Architectures. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2015. [cited 2021 Apr 18].
Available from: https://trace.tennessee.edu/utk_graddiss/3361.
Council of Science Editors:
Schuman CD. Neuroscience-Inspired Dynamic Architectures. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2015. Available from: https://trace.tennessee.edu/utk_graddiss/3361
25.
Figueiredo, Daniel Oliveira.
Logical foundations and computational tools for synthetic biology
.
Degree: 2020, Universidade de Aveiro
URL: http://hdl.handle.net/10773/29829
► The study and development of tools for computational systems is an area where we can easily find diverse works and, nowadays, it is one of…
(more)
▼ The study and development of tools for computational systems is an area
where we can easily find diverse works and, nowadays, it is one of the
dominant topics when we think about the research on computer science.
As consequence, the field of computation has access to a solid theoretical
basis, as well as to a wide collection of algorithms and tools (such as model
checkers).
The focus of this thesis is to look at a
biological system under a computational perspective, where cells and gens replace the role of transistors as
the fundamental elements of a computational system. Indeed, the notion of
computation is often compared to the functioning of a brain in an animal.
Taking into account this point of view, the goal of this work is to revisit
basic concepts present on the study of intracelular dynamics, which are
fundamentally the same for all living organisms, under a computer science
perpective. Thus, we intend to understand how we can apply concepts,
algorithms and computational tools, which are used the field of Computer
Science, to the mentioned
biological systems. In particular, we start by
describing some kinds of models used to model the intracellular dynamics
of living organisms – Piecewise linear models and Boolean
networks.
Hence, we propose a new perspective over Piecewise linear model, considering these models as reconfigurable. This allows one to use computational
tools like KeYmaera and dReach to reason about these models. Afterward,
discretizing this kind of model but maintaining the notion of reconfigurability, we obtain the concept of reactive Boolean network, based on the switch
graph formalism, and propose a logical language to express and formally
check properties of these systems along with a notion of bisimulation. In
what relates to Boolean
networks, we provide a new point of view over the
notion of “terminal”, by relating it to the notion of bisimulation, which is
widely known in the area of Computater Science. Then, we focus in the
asymptotic graph method and, after a fundamental study, we propose a generalized and intermediate method that is less efficient in a computational
perspective but more suitable to the intended context. Finally, we consider a
new kind of stochastic model which is obtaining embeding weights in edges
of switch graphs. We also develop an extension of PRISM model checker –
rPrism – to ease the study of this specific class of stochastic models.
Advisors/Committee Members: Martins, Manuel António Gonçalves (advisor), Barbosa, Luís Soares (advisor).
Subjects/Keywords: Biological regulatory networks;
Piecewise linear models;
Reactive Boolean networks;
Extended asymptotic graph;
Bisimulation;
Reconfigurability;
Reactivity;
rPrism;
Weighted switch graphs
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APA (6th Edition):
Figueiredo, D. O. (2020). Logical foundations and computational tools for synthetic biology
. (Thesis). Universidade de Aveiro. Retrieved from http://hdl.handle.net/10773/29829
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Figueiredo, Daniel Oliveira. “Logical foundations and computational tools for synthetic biology
.” 2020. Thesis, Universidade de Aveiro. Accessed April 18, 2021.
http://hdl.handle.net/10773/29829.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Figueiredo, Daniel Oliveira. “Logical foundations and computational tools for synthetic biology
.” 2020. Web. 18 Apr 2021.
Vancouver:
Figueiredo DO. Logical foundations and computational tools for synthetic biology
. [Internet] [Thesis]. Universidade de Aveiro; 2020. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10773/29829.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Figueiredo DO. Logical foundations and computational tools for synthetic biology
. [Thesis]. Universidade de Aveiro; 2020. Available from: http://hdl.handle.net/10773/29829
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – San Diego
26.
Ellen, Jeffrey Scott.
Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata.
Degree: Computer Science and Engineering, 2018, University of California – San Diego
URL: http://www.escholarship.org/uc/item/8f18p61p
► For the past few years, Convolutional Neural Networks have had tremendous impact not only within the field of Computer Science but by 2018 their rapid…
(more)
▼ For the past few years, Convolutional Neural Networks have had tremendous impact not only within the field of Computer Science but by 2018 their rapid proliferation in free publically available tools has not only thoroughly permeating all aspects of data science, signal processing, and cybersecurity but had an impact on most human endeavors including entertainment, finance, transportation, agriculture, and medicine to name a few. In this dissertation I utilize CNNs specifically to achieve better classification of zooplankton in scientific images, but I also use the zooplankton images to better understand CNNs, as a benchmark to quantify the performance benefit CNNs provide over the previous state of the art, and as raw material to inspire my own contribution to the growing body of knowledge regarding CNNs. The performance benefit I achieve through utilizing contextual metadata with pixel images may no longer be novel, but it provides a concrete benefit, leverages the past body of work, and is likely applicable to numerous other application areas.Before tackling any specifics, I provide a comprehensive overview of historical and contemporary feature extraction techniques that are particularly applicable to biological object classification in images. This includes a literature review and categorizes previous feature extraction techniques into three different categories: statistical analysis methods, topology based methods, and point/patch correspondence methods. We then baselined existing performance with non-CNN approaches, and considerations as to how they could be made more efficient. It also quantified the number of expertly labeled images required, and which algorithms worked best (in our case SVM and Gradient Boosted Random Forest, also Multi-layer perceptron). Some minor points were investigating whether or not abstaining provided any meaningful gain, and whether or not size fractionation improved performance (no, but significantly increased speed at a minimal performance cost), and whether ensembling two diverse approaches resulted in better performance than either individually (maybe, but only a small amount) Next, we use convolutional neural networks (CNNs) on various types of images, including plankton, looking for clues useful when training models from scratch. This publication identifies a correlation between the statistical distribution of the weights of filters in a fully trained network with the overall accuracy of that network. The implication is that given multiple instances of trained networks, it may be possible to predict future performance.Detouring from machine learning experimentation, I cover some additional image processing work I completed in order to more cleanly segment objects in images obtained by our Zooglider. Finally, we combine all of these contributions to improve plankton image classification: we utilize CNNs on segmented image tiles of plankton images. We also tested the hypothesis that the previously used geometric features, as well as Geotemporal and hydgrographic metadata…
Subjects/Keywords: Artificial intelligence; Biological oceanography; convolutional neural networks; image processing; machine learning; zooplankton
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APA (6th Edition):
Ellen, J. S. (2018). Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/8f18p61p
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ellen, Jeffrey Scott. “Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata.” 2018. Thesis, University of California – San Diego. Accessed April 18, 2021.
http://www.escholarship.org/uc/item/8f18p61p.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ellen, Jeffrey Scott. “Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata.” 2018. Web. 18 Apr 2021.
Vancouver:
Ellen JS. Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2021 Apr 18].
Available from: http://www.escholarship.org/uc/item/8f18p61p.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ellen JS. Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/8f18p61p
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Anna University
27.
Nirmala Devi, M.
VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -.
Degree: Information and Communication
Engineering, 2014, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/26986
► A Biological Neural Network BNN forms the Central Nervous newlineSystem which has highly interconnected neurons to co ordinate all the newlinefunctions like reading and thinking…
(more)
▼ A Biological Neural Network BNN forms the Central
Nervous newlineSystem which has highly interconnected neurons to co
ordinate all the newlinefunctions like reading and thinking
Artificial Neurons are simple newlineabstractions of biological
neurons programmed in software or modeled in newlinehardware
Networks of artificial neurons known as Artificial Neural Networks
newline ANN have a fraction of power of biological neural
structures and they can newlinebe trained to solve many complex
problems An ANN has the ability to learn newlineand it had been
realized in software But learning is a recursive process
newlineinvolving multiple iterations which takes a long time when
implemented in newlinesoftware However availability of powerful
resources at affordable cost has newlineopened up the interesting
possibility of realizing ANN in hardware The focus newlineof the
present work is on Very Large Scale Integration VLSI hardware
newlinerealization of ANN so as to achieve flexibility portability
and adaptability newline newline
reference p.169-183
Advisors/Committee Members: Arumugam, S.
Subjects/Keywords: Artificial Neural Networks; Biological Neural Network; Central Nervous System; Very Large Scale Integration
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APA (6th Edition):
Nirmala Devi, M. (2014). VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/26986
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Nirmala Devi, M. “VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -.” 2014. Thesis, Anna University. Accessed April 18, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/26986.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Nirmala Devi, M. “VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -.” 2014. Web. 18 Apr 2021.
Vancouver:
Nirmala Devi M. VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -. [Internet] [Thesis]. Anna University; 2014. [cited 2021 Apr 18].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/26986.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Nirmala Devi M. VLSI realization of artificial neural Networks using
digital and mixed Signal hardware; -. [Thesis]. Anna University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/26986
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
28.
Sun, Zhongyao.
Analysis and Logical Modeling of Biological Signaling Transduction Networks.
Degree: 2015, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/25268
► The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science,…
(more)
▼ The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together.
In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction
networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction
networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.
Advisors/Committee Members: Reka Z Albert, Dissertation Advisor/Co-Advisor, Reka Z Albert, Committee Chair/Co-Chair, Dezhe Jin, Committee Member, Jorge Osvaldo Sofo, Committee Member, John Fricks, Committee Member.
Subjects/Keywords: network science; biological networks; discrete dynamics; Boolean network; system biology; network modeling; signal transduction
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APA (6th Edition):
Sun, Z. (2015). Analysis and Logical Modeling of Biological Signaling Transduction Networks. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/25268
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sun, Zhongyao. “Analysis and Logical Modeling of Biological Signaling Transduction Networks.” 2015. Thesis, Penn State University. Accessed April 18, 2021.
https://submit-etda.libraries.psu.edu/catalog/25268.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sun, Zhongyao. “Analysis and Logical Modeling of Biological Signaling Transduction Networks.” 2015. Web. 18 Apr 2021.
Vancouver:
Sun Z. Analysis and Logical Modeling of Biological Signaling Transduction Networks. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Apr 18].
Available from: https://submit-etda.libraries.psu.edu/catalog/25268.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sun Z. Analysis and Logical Modeling of Biological Signaling Transduction Networks. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/25268
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
29.
Linard, Benjamin.
Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems.
Degree: Docteur es, Bioinformatique, 2012, Université de Strasbourg
URL: http://www.theses.fr/2012STRAJ044
► La biologie des systèmes s’est beaucoup développée ces dix dernières années, confrontant plusieurs niveaux biologiques (molécule, réseau, tissu, organisme, écosystème…). Du point de vue de…
(more)
▼ La biologie des systèmes s’est beaucoup développée ces dix dernières années, confrontant plusieurs niveaux biologiques (molécule, réseau, tissu, organisme, écosystème…). Du point de vue de l’étude de l’évolution, elle offre de nombreuses possibilités. Cette thèse porte sur le développement de nouvelles méthodologies et de nouveaux outils pour étudier l’évolution des systèmes biologiques tout en considérant l’aspect multidimensionnel des données biologiques. Ce travail tente de palier un manque méthodologique évidant pour réaliser des études haut-débit dans le récent domaine de la biologie évolutionnaire des systèmes. De nouveaux messages évolutifs liés aux contraintes intra et inter processus ont été décrites. En particulier, mon travail a permis (i) la création d’un algorithme et un outil bioinformatique dédié à l’étude des relations évolutives d’orthologie existant entre les gènes de centaines d’espèces, (ii) le développement d’un formalisme original pour l’intégration de variables biologiques multidimensionnelles permettant la représentation synthétique de l’ histoire évolutive d’un gène donné, (iii) le couplage de cet outil intégratif avec des approches mathématiques d’extraction de connaissances pour étudier les perturbations évolutives existant au sein des processus biologiques humains actuellement documentés (voies métaboliques, voies de signalisations…).
Systems biology has developed enormously over the 10 last years, with studies covering diverse biological levels (molecule, network, tissue, organism, ecology…). From an evolutionary point of view, systems biology provides unequalled opportunities. This thesis describes new methodologies and tools to study the evolution of biological systems, taking into account the multidimensional properties of biological parameters associated with multiple levels. Thus it addresses the clear need for novel methodologies specifically adapted to high-throughput evolutionary systems biology studies. By taking account the multi-level aspects of biological systems, this work highlight new evolutionary trends associated with both intra and inter-process constraints. In particular, this thesis includes (i) the development of an algorithm and a bioinformatics tool dedicated to comprehensive orthology inference and analysis for hundreds of species, (ii) the development of an original formalism for the integration of multi-scale variables allowing the synthetic representation of the evolutionary history of a given gene, (iii) the combination of this integrative tool with mathematical knowledge discovery approaches in order to highlight evolutionary perturbations in documented human biological systems (metabolic and signalling pathways...).
Advisors/Committee Members: Thompson, Julie D. (thesis director).
Subjects/Keywords: Orthologie; Extraction de connaissance; Évolution; Réseaux biologiques; Orthology; Knowledge extraction; Evolution; Biological networks; 576; 006.3
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APA ·
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APA (6th Edition):
Linard, B. (2012). Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems. (Doctoral Dissertation). Université de Strasbourg. Retrieved from http://www.theses.fr/2012STRAJ044
Chicago Manual of Style (16th Edition):
Linard, Benjamin. “Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems.” 2012. Doctoral Dissertation, Université de Strasbourg. Accessed April 18, 2021.
http://www.theses.fr/2012STRAJ044.
MLA Handbook (7th Edition):
Linard, Benjamin. “Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems.” 2012. Web. 18 Apr 2021.
Vancouver:
Linard B. Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems. [Internet] [Doctoral dissertation]. Université de Strasbourg; 2012. [cited 2021 Apr 18].
Available from: http://www.theses.fr/2012STRAJ044.
Council of Science Editors:
Linard B. Développement de méthodes évolutionnaires d'extraction de connaissance et application à des systèmes biologiques complexes : Development of evolutionary knowledge extraction methods and their application in biological complex systems. [Doctoral Dissertation]. Université de Strasbourg; 2012. Available from: http://www.theses.fr/2012STRAJ044
University of Oxford
30.
Andrews, Tallulah.
Clustering genes by function to understand disease phenotypes.
Degree: PhD, 2015, University of Oxford
URL: http://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928
► Developmental disorders including: autism, intellectual disability, and congenital abnormalities are present in 3-8% of live births and display a huge amount of phenotypic and genetic…
(more)
▼ Developmental disorders including: autism, intellectual disability, and congenital abnormalities are present in 3-8% of live births and display a huge amount of phenotypic and genetic heterogeneity. Traditionally, geneticists have identified individual monogenic diseases among these patients but a majority of patients fail to receive a clinical diagnosis. However, the genomes of these patients frequently harbour large copynumber variants (CNVs) but their interpretation remains challenging. Using pathway analysis I found significant functional associations for 329 individual phenotypes and show that 39% of these could explain the patientsâ multiple co-morbid phenotypes; and multiple associated genes clustered within individual CNVs. I showed there was significantly more such clustering than expected by chance. In addition, the presence of a multiple functionally-related genes is a significant predictor of CNV pathogenicity beyond the presence of known disease genes and size of the CNV. This clustering of functionally-related genes was part of a broader pattern of functional clusters across the human genome. These genome-wide functional clusters showed tissuespecific expression and some evidence of chromatin-domain level regulation. Furthermore, many genome-wide functional clusters were enriched in segmental duplications making them prone to CNV-causing mutations and were frequently seen disrupted in healthy individuals. However, the majority of the time a pathogenic CNV affected the entire functional cluster, where as benign CNVs tended to affect only one or two genes. I also showed that patients with CNVs affecting the same functional cluster are significantly more phenotypically similar to each other than expected even if their CNVs do not affect any of the same genes. Lastly, I considered one of the major limitations in pathway analysis, namely ascertainment biases in functional information due to the prioritization of genes linked to human disease, and show how the modular nature of gene-networks can be used to identify and prioritize understudied genes.
Subjects/Keywords: 572.8; Computational Biology; Human Genetics; protein-protein interactions; biological networks; copy number variation
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APA ·
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Export
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Manager
APA (6th Edition):
Andrews, T. (2015). Clustering genes by function to understand disease phenotypes. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928
Chicago Manual of Style (16th Edition):
Andrews, Tallulah. “Clustering genes by function to understand disease phenotypes.” 2015. Doctoral Dissertation, University of Oxford. Accessed April 18, 2021.
http://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928.
MLA Handbook (7th Edition):
Andrews, Tallulah. “Clustering genes by function to understand disease phenotypes.” 2015. Web. 18 Apr 2021.
Vancouver:
Andrews T. Clustering genes by function to understand disease phenotypes. [Internet] [Doctoral dissertation]. University of Oxford; 2015. [cited 2021 Apr 18].
Available from: http://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928.
Council of Science Editors:
Andrews T. Clustering genes by function to understand disease phenotypes. [Doctoral Dissertation]. University of Oxford; 2015. Available from: http://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.711928
◁ [1] [2] [3] [4] [5] [6] ▶
. 
Open Access Theses and Dissertations
