+publisher:"Penn State University" +contributor:("Reka Z Albert, Dissertation Advisor/Co-Advisor")

Penn State University

1. Campbell, Colin Edward. complex dynamics of biological systems.

Degree: 2012, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/13132

► The analysis of complex systems has become intertwined with, and driven by, network theory: the study of a system within the context of discrete, interacting… (more)

▼ The analysis of complex systems has become intertwined with, and driven by, network theory: the study of a system within the context of discrete, interacting components. A network-based investigation of a complex system enables analysis of its structure, function, and dynamics, even in the face of noisy or otherwise incomplete data. This is particularly relevant to the biological sciences, as recent advances in data-collection techniques have made a systems-level study of biological systems feasible. Here, I present applications and advancements of network theory within the context of three biological systems that range in scale from cellular to ecological. First, the dynamic “tug of war” between the human immune system and cancer of the brain, bones, and pancreas is studied with a model of coupled ordinary differential equations, with the ultimate goal of directing researchers towards curative therapies. Known qualitative and quantitative time-course data are replicated, and several predictions of the model are experimentally validated. Second, a set of topological network measures are proposed and applied to a network representation of the immune response to attack by respiratory bacteria and allergen. The measures elucidate the functioning of the network, and along with analysis of the small-scale structure of the network, identify key regulators in the immune system response to the joint attack. Finally, a novel, dynamic model of the formation of ecological communities consisting of plants and their pollinators is proposed and shown to replicate expected ecological behavior. The model is used as the basis for a study of the stability of the communities in the face of species extinctions, and successfully identifies key properties in critical species and communities susceptible to significant damage from the loss of a single species. In this dissertation, mathematical models are developed, networks are formed, network topologies are analyzed, and both discrete- and continuous-time dynamics are studied. Novel measures and models are proposed and discussed. Thus, in addition to offering significant insight into each of the studied biological systems, this dissertation constitutes an advancement of the techniques by which complex systems are studied. Advisors/Committee Members: Advisor%22%29&pagesize-30">Reka Z Albert, Dissertation Advisor/Co-Advisor, Dezhe Jin, Committee Member, Alexay Kozhevnikov, Committee Member, Istvan Albert, Committee Member.

Subjects/Keywords: complex systems; network theory; systems biology; cancer; ecosystem stability; immune system

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APA (6^th Edition):

Campbell, C. E. (2012). complex dynamics of biological systems. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13132

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Campbell, Colin Edward. “complex dynamics of biological systems.” 2012. Thesis, Penn State University. Accessed April 15, 2021. https://submit-etda.libraries.psu.edu/catalog/13132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Campbell, Colin Edward. “complex dynamics of biological systems.” 2012. Web. 15 Apr 2021.

Vancouver:

Campbell CE. complex dynamics of biological systems. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Apr 15]. Available from: https://submit-etda.libraries.psu.edu/catalog/13132.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Campbell CE. complex dynamics of biological systems. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/13132

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

2. 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)

Subjects/Keywords: network science; biological networks; discrete dynamics; Boolean network; system biology; network modeling; signal transduction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Sun, Zhongyao. “Analysis and Logical Modeling of Biological Signaling Transduction Networks.” 2015. Thesis, Penn State University. Accessed April 15, 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 (7^th Edition):

Sun, Zhongyao. “Analysis and Logical Modeling of Biological Signaling Transduction Networks.” 2015. Web. 15 Apr 2021.

Vancouver:

Sun Z. Analysis and Logical Modeling of Biological Signaling Transduction Networks. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Apr 15]. 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

Penn State University

3. Gomez Tejeda Zanudo, Jorge. Network-based dynamic modeling and control strategies in complex diseases.

Degree: 2016, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/27687

► In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge… (more)

▼ In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. Creating such models is a challenging task in complex diseases such as cancer, in which numerous components are known to play an important role. To model the dynamics of the networks underlying complex diseases I use network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states. I developed analytical and computational tools based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the network, and which I termed stable motif. Based on the concept of stable motif, I proposed a method to identify a model's dynamical attractors, which have been found to be identifiable with the cell fates and cell behaviors of modeled organisms. I also proposed a stable-motif-based control method that identifies targets whose manipulation ensures the convergence of the model towards an attractor of interest. The identified control targets can be single or multiple nodes, are proven to always drive any initial condition to the desired attractor, and need to be applied only transiently to be effective. I illustrated the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition), and also applied them to several published models for complex diseases, such as a type of white blood cell cancer (T-LGL leukemia). These methods allowed me to find attractors of larger models than what was previously possible, identify the subnetworks responsible for the disease and the healthy cell states, and show that stabilizing the activity of a few select components can drive the cell towards a desired fate or away from an undesired fate, the validity of which is supported by experimental work. Advisors/Committee Members: Advisor%22%29&pagesize-30">Reka Z Albert, Dissertation Advisor/Co-Advisor, Reka Z Albert, Committee Chair/Co-Chair, Dezhe Jin, Committee Member, Lu Bai, Committee Member, Timothy Reluga, Committee Member, Richard Wallace Robinett, Special Member.

Subjects/Keywords: Systems Biology; Complex Networks; Network models; Cancer

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APA (6^th Edition):

Gomez Tejeda Zanudo, J. (2016). Network-based dynamic modeling and control strategies in complex diseases. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/27687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in complex diseases.” 2016. Thesis, Penn State University. Accessed April 15, 2021. https://submit-etda.libraries.psu.edu/catalog/27687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in complex diseases.” 2016. Web. 15 Apr 2021.

Vancouver:

Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in complex diseases. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Apr 15]. Available from: https://submit-etda.libraries.psu.edu/catalog/27687.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in complex diseases. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/27687

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

4. Steinway, Steven Nathaniel. Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/24852

► Biology is incredibly complex – at the molecular, cellular, tissue, and population level, there exists a tremendous number of discrete interacting components tightly regulating the… (more)

▼ Biology is incredibly complex – at the molecular, cellular, tissue, and population level, there exists a tremendous number of discrete interacting components tightly regulating the processes that sustain life. Biological systems have traditionally been viewed in a reductionist manner often literally (and metaphorically) through a magnifying glass, leading to insight into how the individual parts work. Network theory, on the other hand, can be used to put the pieces together, to understand how complex and emergent behaviors arise from the totality of interactions in complex systems, such as those seen in biology. Network theory is the study of systems of discrete interacting components and provides a framework for understanding complex systems. A network-focused investigation of a complex biological system allows for the understanding of the system’s emergent properties, for example its function and dynamics. Network dynamics are of particular interest biologically because biological systems are not static but are constantly changing in response to perturbations and environmental stimuli in space and time. Systems level biological analysis has been aided by the recent explosion of high throughput data. This has led to an abundance of quantitative and qualitative information related to the activation of biological systems, but frequently there is still a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of biological systems by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system (i.e. how it changes over time), both in normal and in perturbation (e.g. disease) scenarios. In this dissertation we present the application of network theory and discrete dynamic modeling integrated with experimental laboratory analysis to understand biological diseases in three contexts. The first is the construction of a network model of epidermal derived growth factor receptor (EGFR) signaling in cancer. We translate this model into two types of discrete models: a Boolean model and a three-state model. We show how the effects of an EGFR inhibitor (such as the drug gefitinib) can suppress tumor growth, and we model how genomic variants can augment the effect of EGFR inhibition in tumor growth. Importantly, we compare discrete modeling outcomes to an alternative modeling framework, which relies on detailed kinetic information, called ordinary differential equation (ODE) modeling and show that both models achieve similar findings. Our results demonstrate that discrete dynamic model can accurately model biomedical systems and make important predictions about the effect a drug will have on a disease (e.g. tumor growth) in the context of various perturbations. Importantly, discrete dynamic models can be employed in the… Advisors/Committee Members: Advisor%22%29&pagesize-30">Reka Z Albert, Dissertation Advisor/Co-Advisor, Advisor%22%29&pagesize-30">"Thomas P Loughran, Jr", Dissertation Advisor/Co-Advisor, Reka Z Albert, Committee Chair/Co-Chair, David J Feith, Committee Member, James Riley Broach, Committee Member, Diane M Thiboutot, Committee Member.

Subjects/Keywords: Network analysis; dynamic modeling; discrete dynamic modeling; cancer; microbiome; tumor invasion & metastasis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Steinway, S. N. (2015). Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/24852

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Steinway, Steven Nathaniel. “Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease.” 2015. Thesis, Penn State University. Accessed April 15, 2021. https://submit-etda.libraries.psu.edu/catalog/24852.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Steinway, Steven Nathaniel. “Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease.” 2015. Web. 15 Apr 2021.

Vancouver:

Steinway SN. Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Apr 15]. Available from: https://submit-etda.libraries.psu.edu/catalog/24852.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Steinway SN. Predictive Network Modeling And Experimentation In Complex Biological Systems: Applications To Cancer And Infectious Disease. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/24852

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

5. 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: Advisor%22%29&pagesize-30">Reka Z Albert, Dissertation Advisor/Co-Advisor, Advisor%22%29&pagesize-30">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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Saadatpour Moghaddam, Assieh. “Dynamic Modeling of Biological and Physical Systems.” 2012. Thesis, Penn State University. Accessed April 15, 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 (7^th Edition):

Saadatpour Moghaddam, Assieh. “Dynamic Modeling of Biological and Physical Systems.” 2012. Web. 15 Apr 2021.

Vancouver:

Saadatpour Moghaddam A. Dynamic Modeling of Biological and Physical Systems. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Apr 15]. 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

Open Access Theses and Dissertations