Matching Items (112)
Description
Over the past 20 years, the fields of synthetic biology and synthetic biosystems engineering have grown into mature disciplines, leading to significant breakthroughs in cancer research, diagnostics, cell-based medicines, biochemical production, etc. Application of mathematical modelling to biological and biochemical systems have not only given great insight into how these systems function, but also have lent enough predictive power to aid in the forward-engineering of synthetic constructs. However, progress has been impeded by several modes of context-dependence unique to biological and biochemical systems that are not seen in traditional engineering disciplines, resulting in the need for lengthy design-build-test cycles before functional prototypes are generated.In this work, two of these universal modes of context dependence – resource competition and growth feedback –their effects on synthetic gene circuits and potential control mechanisms, are studied and characterized. Results demonstrate that a novel competitive control architecture can be utilized to mitigate the effects of winner-take-all resource competition (a form of context dependence where distinct gene modules influence each other by competing over a shared pool of transcriptional/translational resources) in synthetic gene circuits and restore circuits to their intended function. Application of the fluctuation-dissipation theorem and rigorous stochastic simulations demonstrate that realistic resource constraints present in cells at the transcriptional and translational levels influence noise in gene circuits in a nonmonotonic fashion, either increasing or decreasing noise depending on the transcriptional/translational capacity.
Growth feedback on the other hand links circuit function to cellular growth rate via increased protein dilution rate during exponential growth phase. This in turn can result in the collapse of bistable gene circuits as the accelerated dilution rate forces switches in a high stable state to fall to a low stable state. Mathematical modelling and experimental data demonstrate that application of repressive links can insulate sensitive parts of gene circuits against growth-fluctuations and can in turn increase the robustness of multistable circuits in growth contexts.
The results presented in this work aid in the accumulation of understanding of biological and biochemical context dependence, and corresponding control strategies and design principles engineers can utilize to mitigate these effects.
ContributorsStone, Austin (Author) / Tian, Xiao-jun (Thesis advisor) / Wang, Xiao (Committee member) / Smith, Barbara (Committee member) / Kuang, Yang (Committee member) / Cheng, Albert (Committee member) / Arizona State University (Publisher)
Created2023
Description
The blood-brain-barrier (BBB) is a significant obstacle for treating many neurological disorders. Bubble-assisted focused ultrasound (BAFUS) medicated BBB disruption is a promising technology that enables the delivery of large drug doses at targeted locations across the BBB. However, the current lack of an in vitro model of this process hinders the full understanding of BAFUS BBB disruption for better translation into clinics. In this work, a US-transparent organ-on-chip device has been fabricated that can be critical for the in vitro modeling of the BAFUS BBB disruption. The transparency of the device window to focused ultrasound (FUS) was calculated theoretically and demonstrated by experiments. Nanobubbles were fabricated, characterized by cryogenic transmission electron microscopy (cryo-TEM), and showed bubble cavitation under FUS. Human colorectal adenocarcinoma (Caco-2) cells were used to form a good cellular barrier for BAFUS barrier disruption, as suggested by the measured permeability and transepithelial electrical resistance (TEER). Finally, barrier disruption and recovery were observed in BAFUS disrupted US-transparent organ-on-chips with Caco-2 barriers, showing great promise of the platform for future modeling BAFUS BBB disruption in vitro.
ContributorsAkkad, Adam Rifat (Author) / Gu, Jian (Thesis advisor) / Nikkhah, Mehdi (Thesis advisor) / Belohlavek, Marek (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Two distinct aspects of synthetic biology were investigated: the development of viral structures for new methods of studying self-assembly and nanomanufacturing, and the designs of genetic controls systems based on controlling the secondary structure of nucleic acids. Viral structures have been demonstrated as building blocks for molecular self-assembly of diverse structures, but the ease with which viral genomes can be modified to create specific structures depends on the mechanisms by which the viral coat proteins self-assemble. The experiments conducted demonstrate how the mechanisms that guide bacteriophage lambda’s self-assembly make it a useful and flexible platform for further research into biologically enabled self-assembly. While the viral platform investigations focus on the creation of new structures, the genetic control systems research focuses on new methods for signal interpretation in biological systems. Regulators of genetic activity that operate based on the secondary structure formation of ribonucleic acid (RNA), also known as riboswitches, are genetically compact devices for controlling protein translation. The toehold switch ribodevice can be modified to enable multiplexed logical operations with RNA inputs, requiring no additional protein transcription factors to regulate activity, but they cannot receive chemical inputs. RNA sequences generated to bind to specific chemicals, known as aptamers, can be used in riboswitches to confer genetic activity upon binding their target chemical. But attempts to use aptamers for logical operations and genetic circuits are difficult to generalize due to differences in sequence and binding strength. The experiments conducted demonstrate a ribodevice structure in which aptamers can be used semi-interchangeably to translate chemical inputs into the toehold switch paradigm, marrying the programmability and orthogonality of toehold switches with the broad sensing potential of aptamer-based ribodevices.
ContributorsMcCutcheon, Griffin Cooper (Author) / Green, Alexander (Thesis advisor) / Hariadi, Rizal (Committee member) / Stephanopoulos, Nicholas (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle in the further development of related technologies. In this work, a genetic circuit design principle with synthetic biology approaches is developed to form two-strain microbial consortia with different inter-strain interactions. The microbial systems are well-defined and inducible. Co-culture experiment results show that our microbial consortia behave consistently with previous ecological knowledge and thus serves as excellent model systems to simulate ecosystems with similar interactions. Colony patterns also emerge when co-culturing multiple species on solid media. With the engineered microbial consortia, image-processing based methods were developed to quantify the shape of co-culture colonies and distinguish microbial consortia with different interactions. Factors that affect the population ratios were identified through induction and variations in the inoculation process. Further time-lapse experiments revealed the basic rules of colony growth, composition variation, patterning, and how spatial factors impact the co-culture colony.
ContributorsChen, Xingwen (Author) / Wang, Xiao (Thesis advisor) / Kuang, Yang (Committee member) / Tian, Xiaojun (Committee member) / Brafman, David (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022
Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum
scarring has been studied for more than four decades, but the problem of efficiently detecting
quantum scars has remained to be challenging, relying mostly on human visualization of
wave function patterns. This paper develops a machine learning approach to detecting
quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta
learning. The first step is to construct a few-shot classification algorithm, under the
requirement that the one-shot classification accuracy be larger than 90%. Then propose a
scheme based on a combination of neural networks to improve the accuracy. This paper shows that
the machine learning scheme can find the correct quantum scars from thousands images of
wave functions, without any human intervention, regardless of the symmetry of the underlying
classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the
structures of time-dependent spin networks using time series measurements taken locally
from a small subset of the spins. Physically, the dynamical evolution of a spin network under
time-dependent driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework
whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can
also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a
variety of structures. In the extreme case where measurements are taken from only one spin,
the achieved tomography fidelity values can reach about 90%. The developed machine
learning framework is applicable to any time-dependent systems whose quantum dynamical
evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
The mutual inhibition between synthetic gene circuits and cell growth produces growth feedback in the host-circuit system. Previous studies have demonstrated that the growth feedback has an marked impact on the molecular dynamics of the host-circuit system. However, the complexity of the growth feedback effect is not fully understood. A theoretical framework was developed to study the dynamics of the coupling between growth feedback and synthetic gene circuits. The study’s results reveal three major points about the impact of growth feedback. First, a nonlinear emergent behavior mediated by growth feedback. The unexpected behavior depends on the dynamic ribosome allocation between gene circuit expression and host cell growth. Second, the emergence and loss of unexpected qualitative states on the host-circuit system generated by ultrasensitive growth feedback. Third, the growth feedback-induced cooperativity behavior in synthetic gene modules competing for resources. In addition, growth feedback attenuated the winner-takes-all rules on resource competition between the two self-activating modules. These results demonstrate that growth feedback plays an important role in the host-circuit system’s molecular dynamics. Characterizing general principles from the effect of growth facilitates the ability to minimize or even harness unexpected gene expression behaviors derived from the effect of growth feedback.
ContributorsMelendez-Alvarez, Juan Ramon (Author) / Tian, Xiaojun (Thesis advisor) / Wang, Xiao (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Few-layer black phosphorous (FLBP) is one of the most important two-dimensional (2D) materials due to its strongly layer-dependent quantized bandstructure, which leads to wavelength-tunable optical and electrical properties. This thesis focuses on the preparation of stable, high-quality FLBP, the characterization of its optical properties, and device applications.Part I presents an approach to preparing high-quality, stable FLBP samples by combining O2 plasma etching, boron nitride (BN) sandwiching, and subsequent rapid thermal annealing (RTA). Such a strategy has successfully produced FLBP samples with a record-long lifetime, with 80% of photoluminescence (PL) intensity remaining after 7 months. The improved material quality of FLBP allows the establishment of a more definitive relationship between the layer number and PL energies.
Part II presents the study of oxygen incorporation in FLBP. The natural oxidation formed in the air environment is dominated by the formation of interstitial oxygen and dangling oxygen. By the real-time PL and Raman spectroscopy, it is found that continuous laser excitation breaks the bonds of interstitial oxygen, and free oxygen atoms can diffuse around or form dangling oxygen under low heat. RTA at 450 °C can turn the interstitial oxygen into dangling oxygen more thoroughly. Such oxygen-containing samples show similar optical properties to the pristine BP samples. The bandgap of such FLBP samples increases with the concentration of the incorporated oxygen.
Part III deals with the investigation of emission natures of the prepared samples. The power- and temperature-dependent measurements demonstrate that PL emissions are dominated by excitons and trions, with a combined percentage larger than 80% at room temperature. Such measurements allow the determination of trion and exciton binding energies of 2-, 3-, and 4-layer BP, with values around 33, 23, 15 meV for trions and 297, 276, 179 meV for excitons at 77K, respectively.
Part IV presents the initial exploration of device applications of such FLBP samples. The coupling between photonic crystal cavity (PCC) modes and FLBP's emission is realized by integrating the prepared sandwich structure onto 2D PCC. Electroluminescence has also been achieved by integrating such materials onto interdigital electrodes driven by alternating electric fields.
ContributorsLi, Dongying (Author) / Ning, Cun-Zheng (Thesis advisor) / Vasileska, Dragica (Committee member) / Lai, Ying-Cheng (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Synthetic biology (SB) has become an important field of science focusing on designing and engineering new biological parts and systems, or re-designing existing biological systems for useful purposes. The dramatic growth of SB throughout the past two decades has not only provided us numerous achievements, but also brought us more timely and underexplored problems. In SB's entire history, mathematical modeling has always been an indispensable approach to predict the experimental outcomes, improve experimental design and obtain mechanism-understanding of the biological systems. \textit{Escherichia coli} (\textit{E. coli}) is one of the most important experimental platforms, its growth dynamics is the major research objective in this dissertation. Chapter 2 employs a reaction-diffusion model to predict the \textit{E. coli} colony growth on a semi-solid agar plate under multiple controls. In that chapter, a density-dependent diffusion model with non-monotonic growth to capture the colony's non-linear growth profile is introduced. Findings of the new model to experimental data are compared and contrasted with those from other proposed models. In addition, the cross-sectional profile of the colony are computed and compared with experimental data. \textit{E. coli} colony is also used to perform spatial patterns driven by designed gene circuits. In Chapter 3, a gene circuit (MINPAC) and its corresponding pattern formation results are presented. Specifically, a series of partial differential equation (PDE) models are developed to describe the pattern formation driven by the MINPAC circuit. Model simulations of the patterns based on different experimental conditions and numerical analysis of the models to obtain a deeper understanding of the mechanisms are performed and discussed. Mathematical analysis of the simplified models, including traveling wave analysis and local stability analysis, is also presented and used to explore the control strategies of the pattern formation. The interaction between the gene circuit and the host \textit{E. coli} may be crucial and even greatly affect the experimental outcomes. Chapter 4 focuses on the growth feedback between the circuit and the host cell under different nutrient conditions. Two ordinary differential equation (ODE) models are developed to describe such feedback with nutrient variation. Preliminary results on data fitting using both two models and the model dynamical analysis are included.
ContributorsHe, Changhan (Author) / Kuang, Yang (Thesis advisor) / Wang, Xiao (Committee member) / Kostelich, Eric (Committee member) / Tian, Xiaojun (Committee member) / Gumel, Abba (Committee member) / Arizona State University (Publisher)
Created2021
Description
Extrachromosomal circular DNA (eccDNA) has become an increasingly popular subject of study in eukaryotic cell biology due to its prevalence in human cancer. Though the literature reports a consensus regarding DNA break repair as a driver of eccDNA formation, there remains a lack of knowledge surrounding the exact mechanisms for eccDNA formation and the selective dynamics that promote their retainment in a cell or population. A central issue to studying eccDNA is the inability to distinguish between linear and circular DNA of homologous sequence. The work presented here describes an adapted eccDNA enrichment and detection assay, specifically for investigating the effects of manipulating a known eccDNA-forming locus in the budding yeast Saccharomyces cerevisiae. First, a galactose inducible GFP reporter was integrated within the copper inducible CUP1 tandem repeat locus of yeast cells. The eccDNA enrichment and detection assay was first applied to wildtype yeast to demonstrate the presence of CUP1 eccDNA in copper induced cells by qPCR. Although subsequent sequencing analysis failed to validate this result, it indicated the presence of various other known and previously un-reported eccDNA species. Finally, application of the enrichment protocol and qPCR detection assay to CUP1-GFP reporter cells yielded inconclusive results, suggesting the assay requires further optimization to sensitively detect eccDNA from this altered locus. While more work is necessary to draw conclusions regarding the limits of eccDNA production at a manipulated eccDNA-forming locus, this knowledge will lend to the potential for therapeutically targeting eccDNA at the point of de novo formation.
ContributorsKeal, Tula (Author) / Wang, Xiao (Thesis advisor) / Tian, Xiaojun (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022
Description
Regenerative medicine utilizes living cells as therapeutics to replace or repair damaged or diseased tissue, but the manufacturing processes to produce cell-based tissue products require customized biounit operations that do not currently exist as conventional biochemical and biopharma manufacturing processes. Living cells are constantly changing and reacting to their environment, which in the case of cells isolated from their hosts, are utilized as living bioreactor components that, by themselves, are manipulated to biomanufacturer selected tissue products. Therefore, specialized technology is required to assure that cellular products produce the phenotypical tissue characteristics that the final product is designated to have, while also maintaining sterility of the culture. Because of this, FDA guidelines encourage the use of Process Analytical Technology (PAT – see Ref ) to be integrated into manufacturing systems of biologics to ensure quality and safety. To address the need for evaluation of sensor technologies for potential use in PAT, a literature review of both existing sensing technologies and biomarkers was conducted. After a thorough assessment of the sensor technologies that were most applicable to biomanufacturing, spectrophotometry was selected to monitor the metabolic components glucose and lactate of living cells in culture in real time. Initially, spectrophotometric measurements were taken of mock solutions of glucose and lactate solutions at concentrations relevant to human cell culture and physiology. With that data, a mathematical model was developed to predict a solution’s glucose and lactate concentration. This model was then integrated into a Matlab program that was used to continuously monitor and estimate solutions of glucose and lactate concentrations in real time. After testing the accuracy of this program in different solutions, it was determined that calibration curves and models must be made for each media type and estimates of glucose and lactate were found accurate only at higher concentrations. This program was successfully utilized to monitor in real time glucose and lactate production and consumption trends of Mesenchymal Stem Cells (MSCs) in culture, demonstrating proof-of-concept of the proposed bioprocess monitoring schema.
ContributorsBerger, Aubrey (Author) / Pizziconi, Vincent (Thesis director) / Wang, Xiao (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05