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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
Cell morphology and the distribution of voltage gated ion channels play a major role in determining a neuron's firing behavior, resulting in the specific processing of spatiotemporal synaptic input patterns. Although many studies have provided insight into the computational properties arising from neuronal structure as well as from channel kinetics, no comprehensive theory exists which explains how the interaction of these features shapes neuronal excitability. In this study computational models based on the identified Drosophila motoneuron (MN) 5 are developed to investigate the role of voltage gated ion channels, the impact of their densities and the effects of structural features.
First, a spatially collapsed model is used to develop voltage gated ion channels to study the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from resonator to integrator properties. Second, morphologically realistic multicompartment models are studied to investigate the passive properties of MN5. The passive electrical parameters fall in a range that is commonly observed in neurons, MN5 is spatially not compact, but for the single subtrees synaptic efficacy is location independent. Further, different subtrees are electrically independent from each other. Third, a continuum approach is used to formulate a new cable theoretic model to study the output in a dendritic cable with many subtrees, both analytically and computationally. The model is validated, by comparing it to a corresponding model with discrete branches. Further, the approach is demonstrated using MN5 and used to investigate spatially distributions of voltage gated ion channels.
First, a spatially collapsed model is used to develop voltage gated ion channels to study the excitability of the model neuron. Changing the channel densities reproduces different in situ observed firing patterns and induces a switch from resonator to integrator properties. Second, morphologically realistic multicompartment models are studied to investigate the passive properties of MN5. The passive electrical parameters fall in a range that is commonly observed in neurons, MN5 is spatially not compact, but for the single subtrees synaptic efficacy is location independent. Further, different subtrees are electrically independent from each other. Third, a continuum approach is used to formulate a new cable theoretic model to study the output in a dendritic cable with many subtrees, both analytically and computationally. The model is validated, by comparing it to a corresponding model with discrete branches. Further, the approach is demonstrated using MN5 and used to investigate spatially distributions of voltage gated ion channels.
ContributorsBerger, Sandra (Author) / Crook, Sharon (Thesis advisor) / Baer, Steven (Committee member) / Hamm, Thomas (Committee member) / Smith, Brian (Committee member) / Arizona State University (Publisher)
Created2014
Description
Traumatic brain injury (TBI) most frequently occurs in pediatric patients and remains a leading cause of childhood death and disability. Mild TBI (mTBI) accounts for 70-90% of all TBI cases, yet its neuropathophysiology is still poorly understood. While a single mTBI injury can lead to persistent deficits, repeat injuries increase the severity and duration of both acute symptoms and long term deficits. In this study, to model pediatric repetitive mTBI (rmTBI) we subjected unrestrained juvenile animals (post-natal day 20) to repeat weight drop impact. Animals were anesthetized and subjected to sham or rmTBI once per day for 5 days. At 14 days post injury (PID), magnetic resonance imaging (MRI) revealed that rmTBI animals displayed marked cortical atrophy and ventriculomegaly. Specifically, the thickness of the cortex was reduced up to 46% beneath and the ventricles increased up to 970% beneath the impact zone. Immunostaining with the neuron specific marker NeuN revealed an overall loss of neurons within the motor cortex but no change in neuronal density. Examination of intrinsic and synaptic properties of layer II/III pyramidal neurons revealed no significant difference between sham and rmTBI animals at rest or under convulsant challenge with the potassium channel blocker, 4-Aminophyridine. Overall, our findings indicate that the neuropathological changes reported after pediatric rmTBI can be effectively modeled by repeat weight drop in juvenile animals. Developing a better understanding of how rmTBI alters the pediatric brain may help improve patient care and direct "return to game" decision making in adolescents.
ContributorsGoddeyne, Corey (Author) / Anderson, Trent (Thesis advisor) / Smith, Brian (Committee member) / Kleim, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2014
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
Description
Food is an essential driver of animal behavior. For social organisms, the acquisition of food guides interactions with the environment and with group-mates. Studies have focused on how social individuals find and choose food sources, and share both food and information with group-mates. However, it is often not clear how experiences throughout an individual's life influence such interactions. The core question of this thesis is how individuals’ experience contributes to within-caste behavioral variation in a social group. I investigate the effects of individual history, including physical injury and food-related experience, on individuals' social food sharing behavior, responses to food-related stimuli, and the associated neural biogenic amine signaling pathways. I use the eusocial honey bee (Apis mellifera) system, one in which individuals exhibit a high degree of plasticity in responses to environmental stimuli and there is a richness of communicatory pathways for food-related information. Foraging exposes honey bees to aversive experiences such as predation, con-specific competition, and environmental toxins. I show that foraging experience changes individuals' response thresholds to sucrose, a main component of adults’ diets, depending on whether foraging conditions are benign or aversive. Bodily injury is demonstrated to reduce individuals' appetitive responses to new, potentially food-predictive odors. Aversive conditions also impact an individual's social food sharing behavior; mouth-to-mouse trophallaxis with particular groupmates is modulated by aversive foraging conditions both for foragers who directly experienced these conditions and non-foragers who were influenced via social contact with foragers. Although the mechanisms underlying these behavioral changes have yet to be resolved, my results implicate biogenic amine signaling pathways as a potential component. Serotonin and octopamine concentrations are shown to undergo long-term change due to distinct foraging experiences. My work serves to highlight the malleability of a social individual's food-related behavior, suggesting that environmental conditions shape how individuals respond to food and share information with group-mates. This thesis contributes to a deeper understanding of inter-individual variation in animal behavior.
ContributorsFinkelstein, Abigail (Author) / Amdam, Gro V (Thesis advisor) / Conrad, Cheryl (Committee member) / Smith, Brian (Committee member) / Neisewander, Janet (Committee member) / Bimonte-Nelson, Heather A. (Committee member) / Arizona State University (Publisher)
Created2017
Description
The goal of the present study was to investigate whether a rest period following the end of chronic stress would impact fear extinction. Past research has indicated that chronic stress leads to impairments in the learning and recall of fear conditioning extinction. Moreover, the effects of chronic stress can return to levels similar to controls when a post-stress “rest” period (i.e., undisturbed except for normal husbandry) is given prior to testing. Male rats underwent chronic restraint stress for 6hr/day/21days (STR-IMM). Some rats, underwent a post-stress rest period for 6- or 3-weeks after the end of stress (STR-R6, STR-R3). Control (CON) rats were unrestrained for the duration of the experiment. In Experiment 1, following the stress or rest manipulation, all rats were acclimated to conditioning and extinction contexts, fear conditioned with 3 tone-foot shock pairings, and then had two days of extinction training. All groups froze similarly to the tone across all training sessions. However, STR-R6/R3 froze less in the non-shock context than did STR-IMM or CON. During extinction training, STR-IMM showed high levels of freezing to the non-shock context, leading to a concern they may be generalizing across contexts. Consequently, a follow-up experiment tested for context generalization. In Experiment 2, STR-IMM rats underwent a generalization test in an environment that was either different or the same as the conditioning environment, using STR-R6 as a comparison. STR-IMM and STR-R6 showed similar relative levels of freezing to tone and context, regardless of their conditioning environment to reveal that STR-IMM did not generalize and instead, maybe expressing hypervigilance. Thus, the present study demonstrated the novel finding that a rest period from chronic stress can lead to reduced fear responsiveness in a non-shock environment.
ContributorsJudd, Jessica M (Author) / Conrad, Cheryl D. (Thesis advisor) / Sanabria, Federico (Committee member) / Smith, Brian (Committee member) / Arizona State University (Publisher)
Created2018