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CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression. We present a study comparing methylated DNA and normal DNA wrt its persistence length and contour length. Although, previous experiments and studies show no difference between the physical properties of the two, the data collected and interpreted here gives a different picture to the methylation phenomena and its effect on gene silencing. The study was extended to the artificially reconstituted chromatin and its interactions with the methyl CpG binding proteins were also probed.
ContributorsKaur, Parminder (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Tao, Nongjian (Committee member) / Vaiana, Sara (Committee member) / Beckenstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2012
Description
Mortality of 1918 influenza virus was high, partly due to bacteria coinfections. We characterize pandemic mortality in Arizona, which had high prevalence of tuberculosis. We applied regressions to over 35,000 data points to estimate the basic reproduction number and excess mortality. Age-specific mortality curves show elevated mortality for all age groups, especially the young, and senior sparing effects. The low value for reproduction number indicates that transmissibility was moderately low.
ContributorsJenner, Melinda Eva (Author) / Chowell-Puente, Gerardo (Thesis director) / Kostelich, Eric (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Background: While research has quantified the mortality burden of the 1957 H2N2 influenza pandemic in the United States, little is known about how the virus spread locally in Arizona, an area where the dry climate was promoted as reducing respiratory illness transmission yet tuberculosis prevalence was high.
Methods: Using archival death certificates from 1954 to 1961, this study quantified the age-specific seasonal patterns, excess-mortality rates, and transmissibility patterns of the 1957 pandemic in Maricopa County, Arizona. By applying cyclical Serfling linear regression models to weekly mortality rates, the excess-mortality rates due to respiratory and all-causes were estimated for each age group during the pandemic period. The reproduction number was quantified from weekly data using a simple growth rate method and generation intervals of 3 and 4 days. Local newspaper articles from The Arizona Republic were analyzed from 1957-1958.
Results: Excess-mortality rates varied between waves, age groups, and causes of death, but overall remained low. From October 1959-June 1960, the most severe wave of the pandemic, the absolute excess-mortality rate based on respiratory deaths per 10,000 population was 17.85 in the elderly (≥65 years). All other age groups had extremely low excess-mortality and the typical U-shaped age-pattern was absent. However, relative risk was greatest (3.61) among children and young adolescents (5-14 years) from October 1957-March 1958, based on incidence rates of respiratory deaths. Transmissibility was greatest during the same 1957-1958 period, when the mean reproduction number was 1.08-1.11, assuming 3 or 4 day generation intervals and exponential or fixed distributions.
Conclusions: Maricopa County largely avoided pandemic influenza from 1957-1961. Understanding this historical pandemic and the absence of high excess-mortality rates and transmissibility in Maricopa County may help public health officials prepare for and mitigate future outbreaks of influenza.
Methods: Using archival death certificates from 1954 to 1961, this study quantified the age-specific seasonal patterns, excess-mortality rates, and transmissibility patterns of the 1957 pandemic in Maricopa County, Arizona. By applying cyclical Serfling linear regression models to weekly mortality rates, the excess-mortality rates due to respiratory and all-causes were estimated for each age group during the pandemic period. The reproduction number was quantified from weekly data using a simple growth rate method and generation intervals of 3 and 4 days. Local newspaper articles from The Arizona Republic were analyzed from 1957-1958.
Results: Excess-mortality rates varied between waves, age groups, and causes of death, but overall remained low. From October 1959-June 1960, the most severe wave of the pandemic, the absolute excess-mortality rate based on respiratory deaths per 10,000 population was 17.85 in the elderly (≥65 years). All other age groups had extremely low excess-mortality and the typical U-shaped age-pattern was absent. However, relative risk was greatest (3.61) among children and young adolescents (5-14 years) from October 1957-March 1958, based on incidence rates of respiratory deaths. Transmissibility was greatest during the same 1957-1958 period, when the mean reproduction number was 1.08-1.11, assuming 3 or 4 day generation intervals and exponential or fixed distributions.
Conclusions: Maricopa County largely avoided pandemic influenza from 1957-1961. Understanding this historical pandemic and the absence of high excess-mortality rates and transmissibility in Maricopa County may help public health officials prepare for and mitigate future outbreaks of influenza.
ContributorsCobos, April J (Author) / Jehn, Megan (Thesis director) / Chowell-Puente, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
ContributorsPerrings, Charles (Author) / Castillo-Chavez, Carlos (Author) / Chowell-Puente, Gerardo (Author) / Daszak, Peter (Author) / Fenichel, Eli P. (Author) / Finnoff, David (Author) / Horan, Richard D. (Author) / Kilpatrick, A. Marm (Author) / Kinzig, Ann (Author) / Kuminoff, Nicolai (Author) / Levin, Simon (Author) / Morin, Benjamin (Author) / Smith, Katherine F. (Author) / Springborn, Michael (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / W.P. Carey School of Business (Contributor) / Economics (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-12-01
Description
Background
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
ContributorsNishiura, Hiroshi (Author) / Chowell-Puente, Gerardo (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2011-03-24
Description
A major challenge with tissue samples used for biopsies is the inability to monitor their molecular quality before diagnostic testing. When tissue is resected from a patient, the cells are removed from their blood supply and normal temperature-controlled environment, which causes significant biological stress. As a result, the molecular composition and integrity undergo significant change. Currently, there is no method to track the effects of these artefactual stresses on the sample tissue to determine any deviations from the actual patient physiology. Without a way to track these changes, pathologists have to blindly trust that the tissue samples they are given are of high quality and fit for molecular analysis; physicians use the analysis to make diagnoses and treatment plans based on the assumption that the samples are valid. A possible way to track the quality of the tissue is by measuring volatile organic compounds (VOCs) released from the samples. VOCs are carbon-based chemicals with high vapor pressure at room temperature. There are over 1,800 known VOCs within humans and a number of these exist in every tissue sample. They are individualized and often indicative of a person’s metabolic condition. For this reason, VOCs are often used for diagnostic purposes. Their usefulness in diagnostics, reflectiveness of a person’s metabolic state, and accessibility lends them to being beneficial for tracking degradation. We hypothesize that there is a relationship between the change in concentration of the volatile organic compounds of a sample, and the molecular quality of a sample. This relationship is what would indicate the accuracy of the tissue quality used for a biopsy in relation to the tissue within the body.
ContributorsSharma, Nandini (Co-author) / Fragoso, Claudia (Co-author) / Grenier, Tyler (Co-author) / Hanson, Abigail (Co-author) / Compton, Carolyn (Thesis director) / Tao, Nongjian (Committee member) / Moakley, George (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
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
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