Quantitative Biology Colloquium

Image-based, organ-scale computational modeling of prostate cancer growth

The current clinical management of prostate cancer (PCa) enables its detection at early organ-confined stages by combining regular screening and patient classification in risk groups. Although these newly diagnosed tumors do not usually pose a threat to the patient, most PCa cases are prescribed a radical treatment immediately after diagnosis (e.g., surgery or radiotherapy). However, the limited individualization of the clinical management beyond risk-group definition has led to significant overtreatment and undertreatment rates, which might adversely impact the patients’ lives and life expectancy, respectively. Thus, PCa is a paradigmatic disease in which an individualized predictive technology could make a crucial difference in clinical practice, thereby separating less aggressive tumors that could be safely monitored from lethal tumors that require immediate treatment. To address this critical need, we can use routine clinical and imaging data to construct and parametrize personalized mathematical models of PCa growth including the key mechanisms involved in this pathology. Then, we can run computer simulations with these models to forecast the growth of a patient’s tumor, which may assist physicians in clinical-decision making. In this seminar, I will show that these models can reproduce tumor growth over the local anatomy of a patient’s prostate extracted from imaging data, along with the dynamics of the Prostate Specific Antigen (PSA, a ubiquitous biomarker in PCa clinical management). Additionally, I will discuss the importance of the inhibitive effect of growth-induced mechanical stress on PCa and how the compression exerted by concomitant benign prostatic hyperplasia dramatically impedes tumor growth. Finally, I will argue that these imaging-based models could constitute a promising computational technology to assist physicians to provide a personalized clinical management of PCa.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

 

When

Noon Friday

Where

Online

Modeling stoichiometric foraging behaviors

Nutritional constraints are common as food resources are rarely optimally suited for grazing species. Elemental mismatches between trophic levels can influence population growth and foraging behaviors. Grazing species, such as Daphnia, utilize optimal foraging techniques, such as compensatory feeding. Mathematical models developed under the framework of Ecological Stoichiometry can help shed light on population dynamics subject to stoichiometric constraints. I will give a brief overview of stoichiometric producer-grazer models (systems of ordinary differential equations) and present model extensions that explore grazer foraging behaviors. I develop a base model that incorporates a fixed energetic foraging cost, as well as an optimal foraging model where energetic foraging costs depend on food nutritional content. A variable energetic foraging cost results in cell quota dependent predation behaviors. Analyzing and comparing these two models allows us to investigate the potential benefits of stoichiometric compensatory foraging behaviors on grazer populations.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon April 2, 2021

Where

Online

Transmission patterns of SARS-CoV-2 inferred from statistical phylogenetics and case investigations

Identifying patterns of transmission of an infectious disease can provide key information for controlling its spread.  To learn about transmission of SARS-CoV-2---the virus that causes COVID-19---in New Mexico, we use a combination of statistical phylogenetic analyses and public health case investigation data.  We find examples of many different transmission patterns: cases at a location can be explained by either one or more chains of transmission, and a transmission chain can span either one or more locations.  Our findings demonstrate how analyses of local public health data and viral genomes can provide both practical answers and general insights into viral transmission patterns.

Zoom:  https://asu.zoom.us/j/85049043960

When

12:30 p.m. April 9, 2021

Where

Online

Staggered Release Policies for COVID-19 Control: Costs and Benefits of Relaxing Restrictions by Age and Risk

Lockdown and social distancing restrictions have been widely used as part of policy efforts aimed at controlling the ongoing COVID-19 pandemic. Since these restrictions have a negative impact on the economy, there exists a strong incentive to relax these policies while protecting public health. Using a multigroup SEIR epidemiological model, we explore the costs and benefits associated with the sequential release of specific groups based on age and risk from isolation. The results suggest that properly designed staggered-release policies can do better than simultaneous-release policies in terms of protecting the most vulnerable members of a population, reducing health risks overall, and increasing economic activity.

Zoom:  https://asu.zoom.us/j/85049043960

When

Noon March 26, 2021

Where

Online

The Interplay between Climate and Urbanization on Mosquito Populations in Maricopa County, Arizona

Mosquito-borne diseases are the number one cause of human morbidity and mortality worldwide. Arizona experienced some of the nation's highest levels of West Nile Virus (WNV) and St. Louis Encephalitis (SLE) incidence in 2019, which are caused from the bites of infectious female Culex quinquefasciatus mosquitoes. Numerous biotic and abiotic factors influence the presence and distribution of mosquito populations, including local climate, land cover, and human behavior. This study uses climate and remote sensing data along with statistical modeling techniques to analyze the spatial and temporal effect that climate, land-cover, and urbanization have on Aedes aegypti and Culex quinquefasciatus mosquito populations in Maricopa County, Arizona. This talk will compare how seasonality and localized differences in land cover affect the distribution of each mosquito species. These results provide insight into guiding public health decisions to control local mosquito populations in response to the growing urbanization in desert cities.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon March 19, 2021

Where

Online

The EpiCovDA pipeline: from reported data to forecasts

When the COVID-19 pandemic began, researchers across the country developed forecasts for weekly cases and deaths for the United States. Such forecasts have been used by the CDC to communicate the expected severity for each state. Here we describe EpiCovDA, a forecasting framework developed with Joceline Lega. We will discuss the workflow of creating our forecasts each week, including data sources, our modeling methodology, forecast visualization, and forecast submission to the COVID Hub group.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon March 12, 2021

Where

Online

Viral phylodynamics and genealogy-valued Markov processes

Phylodynamics is the project of extracting information from genome sequences to inform models of the biological processes that generate them. In the context of infectious disease, one seeks to parameterize models of pathogen transmission using genealogies reconstructed from virus genomes. In this talk, I show how the problem is naturally formulated in terms of a class of Markov processes on a space of genealogies. For interesting transmission models, the exact likelihood is intractable, but I show how to construct an efficient sequential Monte Carlo algorithm to estimate it with high accuracy.

Aaron A. King is the Nelson G. Hairston Collegiate Professor of Ecology, Evolutionary Biology, and Complex Systems at the University of Michigan. An applied mathematician and a biologist, he investigates problems in the population biology of infectious diseases using mathematical models. He is particularly interested in how nonlinearity, seasonality, and stochasticity interact to shape ecological dynamics and in the development and application of powerful model-based methods for inferring the structure and properties of ecological systems from observational data.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon March 5, 2021

Where

Online

Mathematical Model of a Personalized Cancer Vaccine and the Human Immune System: Evaluation of Efficacy

Abstract:         Cancer vaccines are a novel immunotherapy, enhancing the immune response to malignant cells by activating CD4 + and CD8 + T-cells. In this work, we developed a mathematical model of nonlinear ordinary differential equations to describe key interactions of a personalized neoantigen cancer vaccine with the immune system of an individual patient. We quantify the effect of a personalized, peptide-based neoantigen cancer vaccine on the CD4 + and CD8 + T-cell species and tumor size. This model was calibrated using patient-specific data from a neoantigen peptide vaccine for anti-melanoma clinical trial. Model parameters estimated through model fitting describe the activation of naïve T-cells, and the killing and proliferation interactions between activated T-cells and tumor cells. The model predicts the clinical outcome of patients from a clinical trial and simulate their observed CD4 + and CD8 + T-cells response over time. Based on sampled initial tumor burden of a patient, the model predicts the ‘best’ clinical outcome of a personalized neoantigen peptide vaccine. Some model parameters were identified to be important through global sensitivity analysis such as proliferation rate of activated T-cells, which has been shown to be a favorable prognostic sign and may help determine efficacy of the immunotherapy. Our model has the potential to lay the foundation for generating in silico clinical trial data and aid in the development and efficacy assessment of personalized cancer vaccines.

Bio: I obtained my Ph.D. in Applied Mathematics from Arizona State University in 2018. Upon completing my Ph.D., I took a one year visiting lecturer position in the Department of Mathematics at Dartmouth College in Hanover, NH. After that, I joined the Data Science Initiative center at Brown University, where I started a postdoctoral position. In December 2019, I joined the Analytics and Benefit-Risk Assessment Team under the Office of Biostatistics and Epidemiology in the Center of Biologics Evaluation and Research at FDA as an ORISE Research Fellow, where I have been mainly working on developing a computational tool that can aid the development and efficacy assessment of personalized cancer vaccines.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon Feb. 26, 2021

Where

Online

Multiscale/multiphysics modeling of ocular physiology: the eye as a window on the body

The eye is the only place in the human body where vascular and hemodynamic features can be observed and measured easily and non-invasively down to the capillary level. Numerous clinical studies have shown correlations between alterations in ocular blood flow and ocular diseases (e.g. glaucoma, age-related macular degeneration, diabetic retinopathy), neurodegenerative diseases (e.g. Alzheimer’s disease, Parkinson’s disease) and other systemic diseases (e.g. hypertension, diabetes). Thus, deciphering the mechanisms governing ocular blood flow could be the key to the use of eye examinations as a non-invasive approach to the diagnosis and continuous monitoring for many patients.

However, many factors influence ocular hemodynamics, including arterial blood pressure, intraocular pressure, cerebrospinal fluid pressure and blood flow regulation, and it is extremely challenging to single out their individual contributions during clinical and animal studies. In the recent years, we have been developing mathematical models and computational methods to aid the interpretation of clinical data and provide new insights in ocular physiology in health and disease. In this talk, we will review how these mathematical models have helped elucidate the mechanisms governing the interaction between ocular biomechanics, hemodynamics, solute transport and delivery in health and disease. We will also present a web-based interface that allows the user to run and utilize these models independently, without the need of advanced software expertise.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

Noon Feb. 19, 2021

Where

Online

Quantitative Methods for Assessing Racial/Ethnic Inequities Across the US Using Diverse Cohort Studies

Racial inequities in overall health and, particularly, cardiovascular health (CVH) continue to remain a public health concern in the United States. We use unique population-based data from the Multi-Ethnic Study of Atherosclerosis cohort to explore the Black-White differences in optimal CVH. Utilizing geographically weighted regression methods, we assessed the spatial heterogeneity in Black-White differences in optimal CVH and the impact of both individual- and neighborhood-level risk factors. We found evidence of significant spatial heterogeneity in Black-White differences that varied within and between five cities/regions across the US. Initial models showed decreased odds of optimal CVH for Blacks that ranged from 60% to 70% reduced odds –with noticeable variation of these decreased odds within each city/region. Adjusting for risk factors resulted in reductions in the Black-White differences in optimal CVH. Further understanding of the reasons for spatial heterogeneities in Black-White differences in nationally representative cohorts may provide important clues regarding the drivers of these differences.

Bio:  Dr. Loni Philip Tabb, Associate Professor of Biostatistics, received her MS (2005) and BS (2003) in Mathematics from Drexel University, and her PhD (2010) and AM (2007) degrees in Biostatistics from Harvard University. Since joining the faculty at Drexel, her research focuses primarily on spatial statistics and epidemiology with applications in health and social disparities, violence, and toxicity studies. Much of Dr. Tabb’s work involves using Bayesian statistical methods in the presence of complex data structures. Earlier research focused on the intersection of alcohol and violence in urban settings; with a more recent focus on the additional impact of marijuana access and availability - given the changing landscape of legalization of marijuana in the US. More recently, Dr. Tabb has concentrated her research efforts on the intersection of health and place, specifically as it applies to cardiovascular health. In particular, she uses novel spatial and spatio-temporal statistical methods to look at the local and national geographic patterns of Black-White inequities in this country – and how both individual- and neighborhood-level characteristics play a role in these racial/ethnic cardiovascular health inequities.

Place:   Zoom:  https://asu.zoom.us/j/85049043960

When

9:30 a.m. Feb. 12, 2021

Where

Online
Subscribe to RSS - Quantitative Biology Colloquium