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- Genre: Masters Thesis
- Resource Type: Text
- Status: Published
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Skin wounds can be caused by traumatic lacerations or incisions which disrupt the structural and functional integrity of the skin. Wound closure and primary intention treatment of the wound as soon as possible is crucial to avoid or minimize the risk of infection that can result in a compromised healing rate or advanced functional intricacy. The gold standard treatment for skin wound healing is suturing. Light-activated tissue sealing is an appealing alternative to sutures as it seals the wound edges minimizing the risk of infection and scarring, especially when utilized along with biodegradable polymeric biomaterials in the wound bed. Silk fibroins can be used as a biodegradable biomaterial that possesses properties supporting cell migration and proliferation in the tissue it interacts with. In addition, histamine treatment is shown to have extensive effects on cellular functions promoting wound healing. Here, the evaluation of Laser-activated Sealants (LASE) consisting of silk fibroin films induced with Indocyanine Green dye in a wound sealed with laser in the presence of Histamine receptor agonists H1R, H2R and H4R take place. The results were evaluated using Trans-epidermal Water Loss (TEWL), histological and analytical techniques where immune cell biomarkers Arginase-1, Ly6G, iNOS, Alpha-SMA, Proliferating Cell Nuclear Antigen (PCNA), and E-Cadherin were used to study the activity of specific cells such as macrophages, neutrophils, and myofibroblasts that aid in wound healing. PBS was used as a control for histamine receptor agonists. It was found that TEWL increased when treated with H1 receptor agonists while decreasing significantly in H2R and H4R-treated wounds. Arginase-1 activity improved, while it displayed an inverse relationship compared to iNOS. H4R agonist escalated Alpha-SMA cells, while others did not have any significant difference. Ly6G activity depleted in all histamine agonists significantly, while PCNA and E-Cadherin failed to show a positive or negative effect.
ContributorsPatel, Dirghau Manishbhai (Author) / Rege, Kaushal (Thesis advisor) / Massia, Stephen (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2022
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
Ideas from coding theory are employed to theoretically demonstrate the engineering of mutation-tolerant genes, genes that can sustain up to some arbitrarily chosen number of mutations and still express the originally intended protein. Attention is restricted to tolerating substitution mutations. Future advances in genomic engineering will make possible the ability to synthesize entire genomes from scratch. This presents an opportunity to embed desirable capabilities like mutation-tolerance, which will be useful in preventing cell deaths in organisms intended for research or industrial applications in highly mutagenic environments. In the extreme case, mutation-tolerant genes (mutols) can make organisms resistant to retroviral infections.
An algebraic representation of the nucleotide bases is developed. This algebraic representation makes it possible to convert nucleotide sequences into algebraic sequences, apply mathematical ideas and convert results back into nucleotide terms. Using the algebra developed, a mapping is found from the naturally-occurring codons to an alternative set of codons which makes genes constructed from them mutation-tolerant, provided no more than one substitution mutation occurs per codon. The ideas discussed naturally extend to finding codons that can tolerate t arbitrarily chosen number of mutations per codon. Finally, random substitution events are simulated in both a wild-type green fluorescent protein (GFP) gene and its mutol variant and the amino acid sequence expressed from each post-mutation is compared with the amino acid sequence pre-mutation.
This work assumes the existence of synthetic protein-assembling entities that function like tRNAs but can read k nucleotides at a time, with k greater than or equal to 5. The realization of this assumption is presented as a challenge to the research community.
An algebraic representation of the nucleotide bases is developed. This algebraic representation makes it possible to convert nucleotide sequences into algebraic sequences, apply mathematical ideas and convert results back into nucleotide terms. Using the algebra developed, a mapping is found from the naturally-occurring codons to an alternative set of codons which makes genes constructed from them mutation-tolerant, provided no more than one substitution mutation occurs per codon. The ideas discussed naturally extend to finding codons that can tolerate t arbitrarily chosen number of mutations per codon. Finally, random substitution events are simulated in both a wild-type green fluorescent protein (GFP) gene and its mutol variant and the amino acid sequence expressed from each post-mutation is compared with the amino acid sequence pre-mutation.
This work assumes the existence of synthetic protein-assembling entities that function like tRNAs but can read k nucleotides at a time, with k greater than or equal to 5. The realization of this assumption is presented as a challenge to the research community.
ContributorsAmpofo, Prince Kwame (Author) / Tian, Xiaojun (Thesis advisor) / Kiani, Samira (Committee member) / Kuang, Yang (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
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
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