Matching Items (95)
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
Trees serve as a natural umbrella to mitigate insolation absorbed by features of the urban environment, especially building structures and pavements. For a desert community, trees are a particularly valuable asset because they contribute to energy conservation efforts, improve home values, allow for cost savings, and promote enhanced health and well-being. The main obstacle in creating a sustainable urban community in a desert city with trees is the scarceness and cost of irrigation water. Thus, strategically located and arranged desert trees with the fewest tree numbers possible potentially translate into significant energy, water and long-term cost savings as well as conservation, economic, and health benefits. The objective of this dissertation is to achieve this research goal with integrated methods from both theoretical and empirical perspectives.
This dissertation includes three main parts. The first part proposes a spatial optimization method to optimize the tree locations with the objective to maximize shade coverage on building facades and open structures and minimize shade coverage on building rooftops in a 3-dimensional environment. Second, an outdoor urban physical scale model with field measurement is presented to understand the cooling and locational benefits of tree shade. The third part implements a microclimate numerical simulation model to analyze how the specific tree locations and arrangements influence outdoor microclimates and improve human thermal comfort. These three parts of the dissertation attempt to fill the research gap of how to strategically locate trees at the building to neighborhood scale, and quantifying the impact of such arrangements.
Results highlight the significance of arranging residential shade trees across different geographical scales. In both the building and neighborhood scales, research results recommend that trees should be arranged in the central part of the building south front yard. More cooling benefits are provided to the building structures and outdoor microclimates with a cluster tree arrangement without canopy overlap; however, if residents are interested in creating a better outdoor thermal environment, open space between trees is needed to enhance the wind environment for better human thermal comfort. Considering the rapid urbanization process, limited water resources supply, and the severe heat stress in the urban areas, judicious design and planning of trees is of increasing importance for improving the life quality and sustaining the urban environment.
ContributorsZhao, Qunshan (Author) / Wentz, Elizabeth (Thesis advisor) / Sailor, David (Committee member) / Wang, Zhi-Hua (Committee member) / Arizona State University (Publisher)
Created2017
Description
Neurodegenerative diseases such as Alzheimer’s Disease, Parkinson’s Disease and Amyotrophic Lateral Sclerosis are marked by the loss of different types of neurons and glial cells in the central nervous system (CNS). Human Pluripotent Stem Cell (hPSC)-derived Neural Progenitor Cells (hNPCs) have the ability to self-renew indefinitely and to differentiate into various cell types of the CNS. HNPCs can be used in cell based therapies and have the potential to reverse or arrest neurodegeneration and to replace lost neurons and glial cells. However, the lack of completely defined, scalable systems to culture these cells, limits their therapeutic and clinical applications. In a previous study, a completely defined, robust, synthetic peptide- a Vitronectin Derived Peptide (VDP) that supports the long term expansion and differentiation of various embryonic and induced pluripotent stem cell (hESC/hIPSC) derived hNPC lines on two dimensional (2D) tissue culture plates was identified. In this study, the culture of hNPCs was scaled up using VDP coated microcarriers (MC). VDP MC were able to support the long term expansion of hESC and hiPSC derived hNPCs over multiple passages and supported higher fold changes in cell densities, compared to VDP coated 2D surfaces. VDP MC also showed the ability to support the neuronal differentiation of hNPCs, and produced mature neurons expressing several neuronal, neurotransmitter and cortical markers. Additionally, alzheimer’s disease (AD) relevant phenotypes were studied in patient hIPSC derived hNPCs cultured on laminin MC to assess if the MC culture system could be used for disease modelling and drug screening. Finally, a microcarrier based bioreactor system was developed for the large scale expansion of hNPCs, exhibiting more than a five-fold change in cell density and supporting more than 100 million hNPCs in culture. Thus, the development of a xeno-free, scalable system allows hNPC culture under standard and reproducible conditions in quantities required for therapeutic and clinical applications.
ContributorsRajaram Srinivasan, Gayathri (Author) / Brafman, David (Thesis advisor) / Wang, Xiao (Committee member) / Haynes, Karmella (Committee member) / Arizona State University (Publisher)
Created2017
Description
The past decade has seen a drastic increase in collaboration between Computer Science (CS) and Molecular Biology (MB). Current foci in CS such as deep learning require very large amounts of data, and MB research can often be rapidly advanced by analysis and models from CS. One of the places where CS could aid MB is during analysis of sequences to find binding sites, prediction of folding patterns of proteins. Maintenance and replication of stem-like cells is possible for long terms as well as differentiation of these cells into various tissue types. These behaviors are possible by controlling the expression of specific genes. These genes then cascade into a network effect by either promoting or repressing downstream gene expression. The expression level of all gene transcripts within a single cell can be analyzed using single cell RNA sequencing (scRNA-seq). A significant portion of noise in scRNA-seq data are results of extrinsic factors and could only be removed by customized scRNA-seq analysis pipeline. scRNA-seq experiments utilize next-gen sequencing to measure genome scale gene expression levels with single cell resolution.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
Almost every step during analysis and quantification requires the use of an often empirically determined threshold, which makes quantification of noise less accurate. In addition, each research group often develops their own data analysis pipeline making it impossible to compare data from different groups. To remedy this problem a streamlined and standardized scRNA-seq data analysis and normalization protocol was designed and developed. After analyzing multiple experiments we identified the possible pipeline stages, and tools needed. Our pipeline is capable of handling data with adapters and barcodes, which was not the case with pipelines from some experiments. Our pipeline can be used to analyze single experiment scRNA-seq data and also to compare scRNA-seq data across experiments. Various processes like data gathering, file conversion, and data merging were automated in the pipeline. The main focus was to standardize and normalize single-cell RNA-seq data to minimize technical noise introduced by disparate platforms.
ContributorsBalachandran, Parithi (Author) / Wang, Xiao (Thesis advisor) / Brafman, David (Committee member) / Lockhart, Thurmon (Committee member) / Arizona State University (Publisher)
Created2017
Description
Alzheimer’s disease (AD), despite over a century of research, does not have a clearly defined pathogenesis for the sporadic form that makes up the majority of disease incidence. A variety of correlative risk factors have been identified, including the three isoforms of apolipoprotein E (ApoE), a cholesterol transport protein in the central nervous system. ApoE ε3 is the wild-type variant with no effect on risk. ApoE ε2, the protective and most rare variant, reduces risk of developing AD by 40%. ApoE ε4, the risk variant, increases risk by 3.2-fold and 14.9-fold for heterozygous and homozygous representation respectively. Study of these isoforms has been historically complex, but the advent of human induced pluripotent stem cells (hiPSC) provides the means for highly controlled, longitudinal in vitro study. The effect of ApoE variants can be further elucidated using this platform by generating isogenic hiPSC lines through precise genetic modification, the objective of this research. As the difference between alleles is determined by two cytosine-thymine polymorphisms, a specialized CRISPR/Cas9 system for direct base conversion was able to be successfully employed. The base conversion method for transitioning from the ε3 to ε2 allele was first verified using the HEK293 cell line as a model with delivery via electroporation. Following this verification, the transfection method was optimized using two hiPSC lines derived from ε4/ε4 patients, with a lipofection technique ultimately resulting in successful base conversion at the same site verified in the HEK293 model. Additional research performed included characterization of the pre-modification genotype with respect to likely off-target sites and methods of isolating clonal variants.
ContributorsLakers, Mary Frances (Author) / Brafman, David (Thesis advisor) / Haynes, Karmella (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2017
Description
Alzheimer’s disease (AD) affects over 5 million individuals each year in the United States. Furthermore, most cases of AD are sporadic, making it extremely difficult to model and study in vitro. CRISPR/Cas9 and base editing technologies have been of recent interest because of their ability to create single nucleotide edits at nearly any genomic sequence using a Cas9 protein and a guide RNA (sgRNA). Currently, there is no available phenotype to differentiate edited cells from unedited cells. Past research has employed fluorescent proteins bound to Cas9 proteins to attempt to enrich for edited cells, however, these methods are only reporters of transfection (RoT) and are no indicative of actual base-editing occurring. Thus, this study proposes a transient reporter for editing enrichment (TREE) and Cas9-mediated adenosine TREE (CasMasTREE) which use plasmids to co-transfect with CRISPR/Cas9 technologies to serve as an indicator of base-editing. Specifically, TREE features a blue fluorescent protein (BFP) mutant that, upon a C-T conversion, changes the emission spectrum to a green fluorescent protein (GFP). CasMasTREE features a mCherry and GFP protein separated by a stop codon which can be negated using an A-G conversion. By employing a sgRNA that targets one of the TREE plasmids and at least one genomic site, cells can be sorted for GFP(+) cells. Using these methods, base-edited isogenic hiPSC line generation using TREE (BIG-TREE) was created to generate isogenic hiPSC lines with AD-relevant edits. For example, BIG-TREE demonstrates the capability of converting Apolipoprotein E (APOE), a gene associated with AD-risk development, wildtype (3/3) into another isoform, APOE2/2, to create isogenic hiPSC lines. The capabilities of TREE are vast and can be applied to generate various models of diseases with specific genomic edits.
ContributorsNguyen, Toan Thai Tran (Author) / Brafman, David (Thesis advisor) / Wang, Xiao (Committee member) / Tian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2020
Description
ABSTRACT
Domestic dogs have assisted humans for millennia. However, the extent to which these helpful behaviors are prosocially motivated remains unclear. To assess the propensity of pet dogs to spontaneously and actively rescue distressed humans, this study tested whether sixty pet dogs would release their seemingly trapped owners from a large box. To examine the causal mechanisms that shaped this behavior, the readiness of each dog to open the box was tested in three conditions: 1) the owner sat in the box and called for help (“Distress” test), 2) an experimenter placed high-value food rewards in the box (“Food” test), and 3) the owner sat in the box and calmly read aloud (“Reading” test).
Dogs were as likely to release their distressed owner as to retrieve treats from inside the box, indicating that rescuing an owner may be a highly rewarding action for dogs. After accounting for ability, dogs released the owner more often when the owner called for help than when the owner read aloud calmly. In addition, opening latencies decreased with test number in the Distress test but not the Reading test. Thus, rescuing the owner could not be attributed solely to social facilitation, stimulus enhancement, or social contact-seeking behavior.
Dogs displayed more stress behaviors in the Distress test than in the Reading test, and stress scores decreased with test number in the Reading test but not in the Distress test. This evidence of emotional contagion supports the hypothesis that rescuing the distressed owner was an empathetically-motivated prosocial behavior. Success in the Food task and previous (in-home) experience opening objects were both strong predictors of releasing the owner. Thus, prosocial behavior tests for dogs should control for physical ability and previous experience.
Domestic dogs have assisted humans for millennia. However, the extent to which these helpful behaviors are prosocially motivated remains unclear. To assess the propensity of pet dogs to spontaneously and actively rescue distressed humans, this study tested whether sixty pet dogs would release their seemingly trapped owners from a large box. To examine the causal mechanisms that shaped this behavior, the readiness of each dog to open the box was tested in three conditions: 1) the owner sat in the box and called for help (“Distress” test), 2) an experimenter placed high-value food rewards in the box (“Food” test), and 3) the owner sat in the box and calmly read aloud (“Reading” test).
Dogs were as likely to release their distressed owner as to retrieve treats from inside the box, indicating that rescuing an owner may be a highly rewarding action for dogs. After accounting for ability, dogs released the owner more often when the owner called for help than when the owner read aloud calmly. In addition, opening latencies decreased with test number in the Distress test but not the Reading test. Thus, rescuing the owner could not be attributed solely to social facilitation, stimulus enhancement, or social contact-seeking behavior.
Dogs displayed more stress behaviors in the Distress test than in the Reading test, and stress scores decreased with test number in the Reading test but not in the Distress test. This evidence of emotional contagion supports the hypothesis that rescuing the distressed owner was an empathetically-motivated prosocial behavior. Success in the Food task and previous (in-home) experience opening objects were both strong predictors of releasing the owner. Thus, prosocial behavior tests for dogs should control for physical ability and previous experience.
ContributorsVan Bourg, Joshua Lazar (Author) / Wynne, Clive D (Thesis advisor) / Gilby, Ian C (Committee member) / Aktipis, C. Athena (Committee member) / Arizona State University (Publisher)
Created2019