Matching Items (97)
Description
Most drugs work by binding to receptors on the cell surface. These receptors can then carry the message into the cell and have a wide array of results. However, studying how fast the binding is can be difficult. Current methods involve extracting the receptor and labeling them, but both these steps have issues. Previous works found that binding on the cell surface is accompanied with a small change in cell size, generally an increase. They have also developed an algorithm that can track these small changes without a label using a simple bright field microscope. Here, this relationship is further explored by comparing edge tracking results to a more widely used method, surface plasmon resonance. The kinetic constants found from the two methods are in agreement. No corrections or manipulations were needed to create agreement. The Bland-Altman plots shows that the error between the two methods is about 0.009 s-1. This is about the same error between cells, making it a non-dominant source of error.
ContributorsHunt, Ashley (Author) / Tao, Nongjian (Thesis advisor) / Ros, Alexandra (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2018
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measuring molecular interaction with membrane proteins is critical for understanding cellular functions, validating biomarkers and screening drugs. Despite the importance, developing such a capability has been a difficult challenge, especially for small molecules binding to membrane proteins in their native cellular environment. The current mainstream practice is to isolate membrane proteins from the cell membranes, which is difficult and often lead to the loss of their native structures and functions. In this thesis, novel detection methods for in situ quantification of molecular interactions with membrane proteins are described.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
ContributorsZhang, Fenni (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Borges, Chad (Committee member) / Jing, Tianwei (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
Proteins play a central role to human body and biological activities. As powerful tools for protein detections, many surface plasmon resonance based techniques have been developed to enhance the sensitivity. However, sensitivity is not the only final goal. As a biosensor, four things really matter: sensitivity, specificity, resolution (temporal/spatial) and throughput.
This dissertation presents several works on developing novel plasmonic based techniques for protein detections on the last two aspects to extend the application field. A fast electrochemically controlled plasmonic detection technique is first developed with the capability of monitoring electrochemical signal with nanosecond response time. The study reveals that the conformational gating of electron transfer in a redox protein (cytochrome c) takes place over a broad range of time scales (sub-µs to ms). The second platform integrates ultra-low volume piezoelectric liquid dispensing and plasmonic imaging detection to monitor different protein binding processes simultaneously with low sample cost. Experiment demonstrates the system can observe binding kinetics in 10×10 microarray of 6 nL droplet, with variations of kinetic rate constants among spots less than ±5%. A focused plasmonic imaging system with bi-cell algorithm is also proposed for spatial resolution enhancement. The two operation modes, scanning mode and focus mode, can be applied for different purposes. Measurement of bacterial aggregation demonstrates the higher spatial resolution. Detections of polystyrene beads binding and 50 nm gold nanoparticles oscillation show a high signal to noise ratio of the system.
The real properties of protein rely on its dynamic personalities. The above works shed light upon fast and high throughput detection of protein kinetics, and enable more applications for plasmonic imaging techniques. It is anticipated that such methods will help to invoke a new surge to unveil the mysteries of biological activities and chemical process.
This dissertation presents several works on developing novel plasmonic based techniques for protein detections on the last two aspects to extend the application field. A fast electrochemically controlled plasmonic detection technique is first developed with the capability of monitoring electrochemical signal with nanosecond response time. The study reveals that the conformational gating of electron transfer in a redox protein (cytochrome c) takes place over a broad range of time scales (sub-µs to ms). The second platform integrates ultra-low volume piezoelectric liquid dispensing and plasmonic imaging detection to monitor different protein binding processes simultaneously with low sample cost. Experiment demonstrates the system can observe binding kinetics in 10×10 microarray of 6 nL droplet, with variations of kinetic rate constants among spots less than ±5%. A focused plasmonic imaging system with bi-cell algorithm is also proposed for spatial resolution enhancement. The two operation modes, scanning mode and focus mode, can be applied for different purposes. Measurement of bacterial aggregation demonstrates the higher spatial resolution. Detections of polystyrene beads binding and 50 nm gold nanoparticles oscillation show a high signal to noise ratio of the system.
The real properties of protein rely on its dynamic personalities. The above works shed light upon fast and high throughput detection of protein kinetics, and enable more applications for plasmonic imaging techniques. It is anticipated that such methods will help to invoke a new surge to unveil the mysteries of biological activities and chemical process.
ContributorsWang, Yan (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Goryll, Michael (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
The role of climate change, as measured in terms of changes in the climatology of geophysical variables (such as temperature and rainfall), on the global distribution and burden of vector-borne diseases (VBDs) remains a subject of considerable debate. This dissertation attempts to contribute to this debate via the use of mathematical (compartmental) modeling and statistical data analysis. In particular, the objective is to find suitable values and/or ranges of the climate variables considered (typically temperature and rainfall) for maximum vector abundance and consequently, maximum transmission intensity of the disease(s) they cause.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.
The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).
Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.
ContributorsOkuneye, Kamaldeen O (Author) / Gumel, Abba B (Thesis advisor) / Kuang, Yang (Committee member) / Smith, Hal (Committee member) / Thieme, Horst (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2018
Description
Environmental pollution has been one of the most challenging problems in modern society and more and more health issues are now linked to environmental pollution and especially, air pollution. Certain sensitive group like patients with asthma are highly influenced by the environmental air quality and knowledge of the daily air pollution exposure is of great importance for the management and prevention of asthma attack. Hence small form factor, real time, accurate, sensitive and easy to use portable devices for environmental monitoring are of great value.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
ContributorsDu, Zijian (Author) / Tao, Nongjian (Thesis advisor) / Goryll, Michael (Committee member) / Herckes, Pierre (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Rabies is an infectious viral disease. It is usually fatal if a victim reaches the rabid stage, which starts after the appearance of disease symptoms. The disease virus attacks the central nervous system, and then it migrates from peripheral nerves to the spinal cord and brain. At the time when the rabies virus reaches the brain, the incubation period is over and the symptoms of clinical disease appear on the victim. From the brain, the virus travels via nerves to the salivary glands and saliva.
A mathematical model is developed for the spread of rabies in a spatially distributed fox population to model the spread of the rabies epizootic through middle Europe that occurred in the second half of the 20th century. The model considers both territorial and wandering rabid foxes and includes a latent period for the infection. Since the model assumes these two kinds of rabid foxes, it is a system of both partial differential and integral equations (with integration
over space and, occasionally, also over time). To study the spreading speeds of the rabies epidemic, the model is reduced to a scalar Volterra-Hammerstein integral equation, and space-time Laplace transform of the integral equation is used to derive implicit formulas for the spreading speed. The spreading speeds are discussed and implicit formulas are given for latent periods of fixed length, exponentially distributed length, Gamma distributed length, and log-normally distributed length. A number of analytic and numerical results are shown pertaining to the spreading speeds.
Further, a numerical algorithm is described for the simulation
of the spread of rabies in a spatially distributed fox population on a bounded domain with Dirichlet boundary conditions. I propose the following methods for the numerical approximation of solutions. The partial differential and integral equations are discretized in the space variable by central differences of second order and by
the composite trapezoidal rule. Next, the ordinary or delay differential equations that are obtained this way are discretized in time by explicit
continuous Runge-Kutta methods of fourth order for ordinary and delay differential systems. My particular interest
is in how the partition of rabid foxes into
territorial and diffusing rabid foxes influences
the spreading speed, a question that can be answered by purely analytic means only for small basic reproduction numbers. I will restrict the numerical analysis
to latent periods of fixed length and to exponentially
distributed latent periods.
The results of the numerical calculations
are compared for latent periods
of fixed and exponentially distributed length
and for various proportions of territorial
and wandering rabid foxes.
The speeds of spread observed in the
simulations are compared
to spreading speeds obtained by numerically solving the analytic formulas
and to observed speeds of epizootic frontlines
in the European rabies outbreak 1940 to 1980.
A mathematical model is developed for the spread of rabies in a spatially distributed fox population to model the spread of the rabies epizootic through middle Europe that occurred in the second half of the 20th century. The model considers both territorial and wandering rabid foxes and includes a latent period for the infection. Since the model assumes these two kinds of rabid foxes, it is a system of both partial differential and integral equations (with integration
over space and, occasionally, also over time). To study the spreading speeds of the rabies epidemic, the model is reduced to a scalar Volterra-Hammerstein integral equation, and space-time Laplace transform of the integral equation is used to derive implicit formulas for the spreading speed. The spreading speeds are discussed and implicit formulas are given for latent periods of fixed length, exponentially distributed length, Gamma distributed length, and log-normally distributed length. A number of analytic and numerical results are shown pertaining to the spreading speeds.
Further, a numerical algorithm is described for the simulation
of the spread of rabies in a spatially distributed fox population on a bounded domain with Dirichlet boundary conditions. I propose the following methods for the numerical approximation of solutions. The partial differential and integral equations are discretized in the space variable by central differences of second order and by
the composite trapezoidal rule. Next, the ordinary or delay differential equations that are obtained this way are discretized in time by explicit
continuous Runge-Kutta methods of fourth order for ordinary and delay differential systems. My particular interest
is in how the partition of rabid foxes into
territorial and diffusing rabid foxes influences
the spreading speed, a question that can be answered by purely analytic means only for small basic reproduction numbers. I will restrict the numerical analysis
to latent periods of fixed length and to exponentially
distributed latent periods.
The results of the numerical calculations
are compared for latent periods
of fixed and exponentially distributed length
and for various proportions of territorial
and wandering rabid foxes.
The speeds of spread observed in the
simulations are compared
to spreading speeds obtained by numerically solving the analytic formulas
and to observed speeds of epizootic frontlines
in the European rabies outbreak 1940 to 1980.
ContributorsAlanazi, Khalaf Matar (Author) / Thieme, Horst R. (Thesis advisor) / Jackiewicz, Zdzislaw (Committee member) / Baer, Steven (Committee member) / Gardner, Carl (Committee member) / Kuang, Yang (Committee member) / Smith, Hal (Committee member) / Arizona State University (Publisher)
Created2018
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
Mechanical properties of cells are important in maintaining physiological functions of biological systems. Quantitative measurement and analysis of mechanical properties can help understand cellular mechanics and its functional relevance and discover physical biomarkers for diseases monitoring and therapeutics.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
ContributorsYang, Yunze (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / Goryll, Michael (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cancer is a major health problem in the world today and is expected to become an even larger one in the future. Although cancer therapy has improved for many cancers in the last several decades, there is much room for further improvement. Mathematical modeling has the advantage of being able to test many theoretical therapies without having to perform clinical trials and experiments. Mathematical oncology will continue to be an important tool in the future regarding cancer therapies and management.
This dissertation is structured as a growing tumor. Chapters 2 and 3 consider spheroid models. These models are adept at describing 'early-time' tumors, before the tumor needs to co-opt blood vessels to continue sustained growth. I consider two partial differential equation (PDE) models for spheroid growth of glioblastoma. I compare these models to in vitro experimental data for glioblastoma tumor cell lines and other proposed models. Further, I investigate the conditions under which traveling wave solutions exist and confirm numerically.
As a tumor grows, it can no longer be approximated by a spheroid, and it becomes necessary to use in vivo data and more sophisticated modeling to model the growth and diffusion. In Chapter 4, I explore experimental data and computational models for describing growth and diffusion of glioblastoma in murine brains. I discuss not only how the data was obtained, but how the 3D brain geometry is created from Magnetic Resonance (MR) images. A 3D finite-difference code is used to model tumor growth using a basic reaction-diffusion equation. I formulate and test hypotheses as to why there are large differences between the final tumor sizes between the mice.
Once a tumor has reached a detectable size, it is diagnosed, and treatment begins. Chapter 5 considers modeling the treatment of prostate cancer. I consider a joint model with hormonal therapy as well as immunotherapy. I consider a timing study to determine whether changing the vaccine timing has any effect on the outcome of the patient. In addition, I perform basic analysis on the six-dimensional ordinary differential equation (ODE). I also consider the limiting case, and perform a full global analysis.
This dissertation is structured as a growing tumor. Chapters 2 and 3 consider spheroid models. These models are adept at describing 'early-time' tumors, before the tumor needs to co-opt blood vessels to continue sustained growth. I consider two partial differential equation (PDE) models for spheroid growth of glioblastoma. I compare these models to in vitro experimental data for glioblastoma tumor cell lines and other proposed models. Further, I investigate the conditions under which traveling wave solutions exist and confirm numerically.
As a tumor grows, it can no longer be approximated by a spheroid, and it becomes necessary to use in vivo data and more sophisticated modeling to model the growth and diffusion. In Chapter 4, I explore experimental data and computational models for describing growth and diffusion of glioblastoma in murine brains. I discuss not only how the data was obtained, but how the 3D brain geometry is created from Magnetic Resonance (MR) images. A 3D finite-difference code is used to model tumor growth using a basic reaction-diffusion equation. I formulate and test hypotheses as to why there are large differences between the final tumor sizes between the mice.
Once a tumor has reached a detectable size, it is diagnosed, and treatment begins. Chapter 5 considers modeling the treatment of prostate cancer. I consider a joint model with hormonal therapy as well as immunotherapy. I consider a timing study to determine whether changing the vaccine timing has any effect on the outcome of the patient. In addition, I perform basic analysis on the six-dimensional ordinary differential equation (ODE). I also consider the limiting case, and perform a full global analysis.
ContributorsRutter, Erica Marie (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Frakes, David (Committee member) / Gardner, Carl (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2016