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Description
Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, or amyotrophic lateral sclerosis are defined by the loss of several types of neurons and glial cells within the central nervous system (CNS). Combatting these diseases requires a robust population of relevant cell types that can be employed in cell therapies, drug screening, or patient specific disease modeling. Human induced pluripotent stem cells (hiPSC)-derived neural progenitor cells (hNPCs) have the ability to self-renew indefinitely and differentiate into the various neuronal and glial cell types of the CNS. In order to realize the potential of hNPCs, it is necessary to develop a xeno-free scalable platform for effective expansion and differentiation. Previous work in the Brafman lab led to the engineering of a chemically defined substrate—vitronectin derived peptide (VDP), which allows for the long-term expansion and differentiation of hNPCs. In this work, we use this substrate as the basis for a microcarrier (MC)-based suspension culture system. Several independently derived hNPC lines were cultured on MCs for multiple passages as well as efficiently differentiated to neurons. Finally, this MC-based system was used in conjunction with a low shear rotating wall vessel (RWV) bioreactor for the integrated, large-scale expansion and neuronal differentiation of hNPCs. Finally, VDP was shown to support the differentiation of hNPCs into functional astrocytes. Overall, this fully defined and scalable biomanufacturing system will facilitate the generation of hNPCs and their derivatives in quantities necessary for basic and translational applications.
ContributorsMorgan, Daylin (Author) / Brafman, David (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2018
Description
Calcium imaging is a well-established, non-invasive or minimally technique designed to study the electrical signaling neurons. Calcium regulates the release of gliotransmitters in astrocytes. Analyzing astrocytic calcium transients can provide significant insights into mechanisms such as neuroplasticity and neural signal modulation.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
ContributorsBhandarkar, Siddhi Umesh (Author) / Brafman, David (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Tian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2019
Description
Several debilitating neurological disorders, such as Alzheimer's disease, stroke, and spinal cord injury, are characterized by the damage or loss of neuronal cell types in the central nervous system (CNS). Human neural progenitor cells (hNPCs) derived from human pluripotent stem cells (hPSCs) can proliferate extensively and differentiate into the various neuronal subtypes and supporting cells that comprise the CNS. As such, hNPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine applications. However, the use hNPCs for the study and treatment of neurological diseases requires the development of defined, robust, and scalable methods for their expansion and neuronal differentiation. To that end a rational design process was used to develop a vitronectin-derived peptide (VDP)-based substrate to support the growth and neuronal differentiation of hNPCs in conventional two-dimensional (2-D) culture and large-scale microcarrier (MC)-based suspension culture. Compared to hNPCs cultured on ECMP-based substrates, hNPCs grown on VDP-coated surfaces displayed similar morphologies, growth rates, and high expression levels of hNPC multipotency markers. Furthermore, VDP surfaces supported the directed differentiation of hNPCs to neurons at similar levels to cells differentiated on ECMP substrates. Here it has been demonstrated that VDP is a robust growth and differentiation matrix, as demonstrated by its ability to support the expansions and neuronal differentiation of hNPCs derived from three hESC (H9, HUES9, and HSF4) and one hiPSC (RiPSC) cell lines. Finally, it has been shown that VDP allows for the expansion or neuronal differentiation of hNPCs to quantities (>1010) necessary for drug screening or regenerative medicine purposes. In the future, the use of VDP as a defined culture substrate will significantly advance the clinical application of hNPCs and their derivatives as it will enable the large-scale expansion and neuronal differentiation of hNPCs in quantities necessary for disease modeling, drug screening, and regenerative medicine applications.
ContributorsVarun, Divya (Author) / Brafman, David (Thesis advisor) / Nikkhah, Mehdi (Committee member) / Stabenfeldt, Sarah (Committee member) / Arizona State University (Publisher)
Created2016
Description
Neurotoxicology has historically focused on substances that directly damage nervous tissue. Behavioral assays that test sensory, cognitive, or motor function are used to identify neurotoxins. But, the outcomes of behavioral assays may also be influenced by the physiological status of non-neural organs. Therefore, toxin induced damage to non- neural organs may contribute to behavioral modifications. Heavy metals and metalloids are persistent environmental pollutants and induce neurological deficits in multiple organisms. However, in the honey bee, an important insect pollinator, little is known about the sublethal effects of heavy metal and metalloid toxicity though they are exposed to these toxins chronically in some environments. In this thesis I investigate the sublethal effects of copper, cadmium, lead, and selenium on honey bee behavior and identify potential mechanisms mediating the behavioral modifications. I explore the honey bees’ ability to detect these toxins, their sensory perception of sucrose following toxin exposure, and the effects of toxin ingestion on performance during learning and memory tasks. The effects depend on the specific metal. Honey bees detect and reject copper containing solutions, but readily consume those contaminated with cadmium and lead. And, exposure to lead may alter the sensory perception of sucrose. I also demonstrate that acute selenium exposure impairs learning and long-term memory formation or recall. Localizing selenium accumulation following chronic exposure reveals that damage to non-neural organs and peripheral sensory structures is more likely than direct neurotoxicity. Probable mechanisms include gut microbiome alterations, gut lining
damage, immune system activation, impaired protein function, or aberrant DNA methylation. In the case of DNA methylation, I demonstrate that inhibiting DNA methylation dynamics can impair long-term memory formation, while the nurse-to- forager transition is not altered. These experiments could serve as the bases for and reference groups of studies testing the effects of metal or metalloid toxicity on DNA methylation. Each potential mechanism provides an avenue for investigating how neural function is influenced by the physiological status of non-neural organs. And from an ecological perspective, my results highlight the need for environmental policy to consider sublethal effects in determining safe environmental toxin loads for honey bees and other insect pollinators.
damage, immune system activation, impaired protein function, or aberrant DNA methylation. In the case of DNA methylation, I demonstrate that inhibiting DNA methylation dynamics can impair long-term memory formation, while the nurse-to- forager transition is not altered. These experiments could serve as the bases for and reference groups of studies testing the effects of metal or metalloid toxicity on DNA methylation. Each potential mechanism provides an avenue for investigating how neural function is influenced by the physiological status of non-neural organs. And from an ecological perspective, my results highlight the need for environmental policy to consider sublethal effects in determining safe environmental toxin loads for honey bees and other insect pollinators.
ContributorsBurden, Christina Marie (Author) / Amdam, Gro (Thesis advisor) / Smith, Brian H. (Thesis advisor) / Gallitano-Mendel, Amelia (Committee member) / Harrison, Jon (Committee member) / Vu, Eric (Committee member) / Arizona State University (Publisher)
Created2016
Description
Energy use within urban building stocks is continuing to increase globally as populations expand and access to electricity improves. This projected increase in demand could require deployment of new generation capacity, but there is potential to offset some of this demand through modification of the buildings themselves. Building stocks are quasi-permanent infrastructures which have enduring influence on urban energy consumption, and research is needed to understand: 1) how development patterns constrain energy use decisions and 2) how cities can achieve energy and environmental goals given the constraints of the stock. This requires a thorough evaluation of both the growth of the stock and as well as the spatial distribution of use throughout the city. In this dissertation, a case study in Los Angeles County, California (LAC) is used to quantify urban growth, forecast future energy use under climate change, and to make recommendations for mitigating energy consumption increases. A reproducible methodological framework is included for application to other urban areas.
In LAC, residential electricity demand could increase as much as 55-68% between 2020 and 2060, and building technology lock-in has constricted the options for mitigating energy demand, as major changes to the building stock itself are not possible, as only a small portion of the stock is turned over every year. Aggressive and timely efficiency upgrades to residential appliances and building thermal shells can significantly offset the projected increases, potentially avoiding installation of new generation capacity, but regulations on new construction will likely be ineffectual due to the long residence time of the stock (60+ years and increasing). These findings can be extrapolated to other U.S. cities where the majority of urban expansion has already occurred, such as the older cities on the eastern coast. U.S. population is projected to increase 40% by 2060, with growth occurring in the warmer southern and western regions. In these growing cities, improving new construction buildings can help offset electricity demand increases before the city reaches the lock-in phase.
In LAC, residential electricity demand could increase as much as 55-68% between 2020 and 2060, and building technology lock-in has constricted the options for mitigating energy demand, as major changes to the building stock itself are not possible, as only a small portion of the stock is turned over every year. Aggressive and timely efficiency upgrades to residential appliances and building thermal shells can significantly offset the projected increases, potentially avoiding installation of new generation capacity, but regulations on new construction will likely be ineffectual due to the long residence time of the stock (60+ years and increasing). These findings can be extrapolated to other U.S. cities where the majority of urban expansion has already occurred, such as the older cities on the eastern coast. U.S. population is projected to increase 40% by 2060, with growth occurring in the warmer southern and western regions. In these growing cities, improving new construction buildings can help offset electricity demand increases before the city reaches the lock-in phase.
ContributorsReyna, Janet Lorel (Author) / Chester, Mikhail V (Thesis advisor) / Gurney, Kevin (Committee member) / Reddy, T. Agami (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2016
Description
The portability of genetic tools from one organism to another is a cornerstone of synthetic biology. The shared biological language of DNA-to-RNA-to-protein allows for expression of polypeptide chains in phylogenetically distant organisms with little modification. The tools and contexts are diverse, ranging from catalytic RNAs in cell-free systems to bacterial proteins expressed in human cell lines, yet they exhibit an organizing principle: that genes and proteins may be treated as modular units that can be moved from their native organism to a novel one. However, protein behavior is always unpredictable; drop-in functionality is not guaranteed.
My work characterizes how two different classes of tools behave in new contexts and explores methods to improve their functionality: 1. CRISPR/Cas9 in human cells and 2. quorum sensing networks in Escherichia coli.
1. The genome-editing tool CRISPR/Cas9 has facilitated easily targeted, effective, high throughput genome editing. However, Cas9 is a bacterially derived protein and its behavior in the complex microenvironment of the eukaryotic nucleus is not well understood. Using transgenic human cell lines, I found that gene-silencing heterochromatin impacts Cas9’s ability to bind and cut DNA in a site-specific manner and I investigated ways to improve CRISPR/Cas9 function in heterochromatin.
2. Bacteria use quorum sensing to monitor population density and regulate group behaviors such as virulence, motility, and biofilm formation. Homoserine lactone (HSL) quorum sensing networks are of particular interest to synthetic biologists because they can function as “wires” to connect multiple genetic circuits. However, only four of these networks have been widely implemented in engineered systems. I selected ten quorum sensing networks based on their HSL production profiles and confirmed their functionality in E. coli, significantly expanding the quorum sensing toolset available to synthetic biologists.
My work characterizes how two different classes of tools behave in new contexts and explores methods to improve their functionality: 1. CRISPR/Cas9 in human cells and 2. quorum sensing networks in Escherichia coli.
1. The genome-editing tool CRISPR/Cas9 has facilitated easily targeted, effective, high throughput genome editing. However, Cas9 is a bacterially derived protein and its behavior in the complex microenvironment of the eukaryotic nucleus is not well understood. Using transgenic human cell lines, I found that gene-silencing heterochromatin impacts Cas9’s ability to bind and cut DNA in a site-specific manner and I investigated ways to improve CRISPR/Cas9 function in heterochromatin.
2. Bacteria use quorum sensing to monitor population density and regulate group behaviors such as virulence, motility, and biofilm formation. Homoserine lactone (HSL) quorum sensing networks are of particular interest to synthetic biologists because they can function as “wires” to connect multiple genetic circuits. However, only four of these networks have been widely implemented in engineered systems. I selected ten quorum sensing networks based on their HSL production profiles and confirmed their functionality in E. coli, significantly expanding the quorum sensing toolset available to synthetic biologists.
ContributorsDaer, René (Author) / Haynes, Karmella (Thesis advisor) / Brafman, David (Committee member) / Nielsen, David (Committee member) / Kiani, Samira (Committee member) / Arizona State University (Publisher)
Created2017
Description
Synthetic gene networks have evolved from simple proof-of-concept circuits to
complex therapy-oriented networks over the past fifteen years. This advancement has
greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a
mechanism that cells use to achieve a discrete number of mutually exclusive states in
response to environmental inputs. However, complex contextual connections of gene
regulatory networks in natural settings often impede the experimental establishment of
the function and dynamics of each specific gene network.
In this work, diverse synthetic gene networks are rationally designed and
constructed using well-characterized biological components to approach the cell fate
determination and state transition dynamics in multistable systems. Results show that
unimodality and bimodality and trimodality can be achieved through manipulation of the
signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to
communicate with each other.
Moreover, a synthetic quadrastable circuit is also built and experimentally
demonstrated to have four stable steady states. Experiments, guided by mathematical
modeling predictions, reveal that sequential inductions generate distinct cell fates by
changing the landscape in sequence and hence navigating cells to different final states.
Circuit function depends on the specific protein expression levels in the circuit.
We then establish a protein expression predictor taking into account adjacent
transcriptional regions’ features through construction of ~120 synthetic gene circuits
(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.
These combined results illustrate applications of synthetic gene networks to
understand the cell fate determination and state transition dynamics in multistable
systems. A protein-expression predictor is also developed to evaluate and tune circuit
dynamics.
complex therapy-oriented networks over the past fifteen years. This advancement has
greatly facilitated expansion of the emerging field of synthetic biology. Multistability is a
mechanism that cells use to achieve a discrete number of mutually exclusive states in
response to environmental inputs. However, complex contextual connections of gene
regulatory networks in natural settings often impede the experimental establishment of
the function and dynamics of each specific gene network.
In this work, diverse synthetic gene networks are rationally designed and
constructed using well-characterized biological components to approach the cell fate
determination and state transition dynamics in multistable systems. Results show that
unimodality and bimodality and trimodality can be achieved through manipulation of the
signal and promoter crosstalk in quorum-sensing systems, which enables bacterial cells to
communicate with each other.
Moreover, a synthetic quadrastable circuit is also built and experimentally
demonstrated to have four stable steady states. Experiments, guided by mathematical
modeling predictions, reveal that sequential inductions generate distinct cell fates by
changing the landscape in sequence and hence navigating cells to different final states.
Circuit function depends on the specific protein expression levels in the circuit.
We then establish a protein expression predictor taking into account adjacent
transcriptional regions’ features through construction of ~120 synthetic gene circuits
(operons) in Escherichia coli. The predictor’s utility is further demonstrated in evaluating genes’ relative expression levels in construction of logic gates and tuning gene expressions and nonlinear dynamics of bistable gene networks.
These combined results illustrate applications of synthetic gene networks to
understand the cell fate determination and state transition dynamics in multistable
systems. A protein-expression predictor is also developed to evaluate and tune circuit
dynamics.
ContributorsWu, Fuqing (Author) / Wang, Xiao (Thesis advisor) / Haynes, Karmella (Committee member) / Marshall, Pamela (Committee member) / Nielsen, David (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
The pathophysiology of Alzheimer’s disease (AD) remains difficult to precisely ascertain in part because animal models fail to fully recapitulate many aspects of the disease and postmortem studies do not allow for the study of the pathophysiology. In vitro models of AD generated with patient derived human induced pluripotent stem cells (hiPSCs) could provide new insight into disease mechanisms. Although many protocols exist to differentiate hiPSCs to neurons, standard practice relies on two-dimensional (2-D) systems, which do not accurately mimic the complexity and architecture of the in vivo brain microenvironment. This research aims to create three-dimensional (3-D) models of AD using hiPSCs, which would enhance the understanding of AD pathophysiology thereby, enabling the generation of effective therapeutics.
ContributorsLundeen, Rachel (Author) / Brafman, David (Thesis advisor) / Kiani, Samira (Committee member) / Ebrahimkhani, Mohammad (Committee member) / Arizona State University (Publisher)
Created2017
Description
An in vitro model of Alzheimer’s disease (AD) is required to study the poorly understood molecular mechanisms involved in the familial and sporadic forms of the disease. Animal models have previously proven to be useful in studying familial Alzheimer’s disease (AD) by the introduction of AD related mutations in the animal genome and by the overexpression of AD related proteins. The genetics of sporadic Alzheimer’s is however too complex to model in an animal model. More recently, AD human induced pluripotent stem cells (hiPSCs) have been used to study the disease in a dish. However, AD hiPSC derived neurons do not faithfully reflect all the molecular characteristics and phenotypes observed in the aged cells with neurodegenerative disease. The truncated form of nuclear protein Lamin-A, progerin, has been implicated in premature aging and is found in increasing concentrations as normal cells age. We hypothesized that by overexpressing progerin, we can cause cells to ‘age’ and display the neurodegenerative effects observed with aging in both diseased and normal cells. To answer this hypothesis, we first generated a retrovirus that allows for the overexpression of progerin in AD and non-demented control (NDC) hiPSC derived neural progenitor cells(NPCs). Subsequently, we generated a pure population of hNPCs that overexpress progerin and wild type lamin. Finally, we analyzed the presence of various age related phenotypes such as abnormal nuclear structure and the loss of nuclear lamina associated proteins to characterize ‘aging’ in these cells.
ContributorsRaman, Sreedevi (Author) / Brafman, David (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2017
Description
This dissertation treats a number of related problems in control and data analysis of complex networks.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue.
Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control.
Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics.
At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.
ContributorsWang, Lezhi (Author) / Lai, Ying-Cheng (Thesis advisor) / Wang, Xiao (Thesis advisor) / Papandreoou-Suppappola, Antonia (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2017