Calendar

Dec
5
Tue
2017
Jessye Talley, Morgan State University, “A Food Vector-Borne Contamination Model”
Dec 5 @ 11:00 am – 12:00 pm

Jump to:

Bio

“A Food Vector-Borne Contamination Model”

Dr. Jessye Talley attended North Carolina Agricultural & Technical State University, where she received all of her degrees in Industrial & Systems Engineering with a concentration in Operations Research. Her research expertise includes stochastic and deterministic modeling of supply chains using stochastic programming, Markov chains, differential equations, linear programming, and queueing theory. During her master’s degree, her research consisted of developing a stochastic facility location model, which used stochastic programming to determine the optimal number of teams and supplies that should be prepositioned to begin port recovery and fix aids to navigation after a hurricane occurs. Additionally, within a research group she helped to develop an interactive simulation model to quantify the impact of port disruptions. Her doctoral work entailed the safety and defense of food supply chains, which was modeled using compartmental models to show the progression of the illness of a consumer from eating a contaminated food product and the effects of interventions. Dr. Talley’s current research interest consists of applications in humanitarian relief, emergency preparedness and response to address ports, healthcare, and food supply chain safety and defense. Dr. Talley is currently a lecturer at Morgan State University in the Industrial & Systems Engineering department.

Click here to view webcast.

Abstract

“A Food Vector-Borne Contamination Model”

 

 

According to the Centers for Disease Control and Prevention (CDC), in 20ll, 80% of pathogens transmitted through food were unspecified agents, causing uncertainty in pathogens that could potentially lead to consumer illness. The purpose of this research is to develop models that will help quantify consumer morbidity and mortality, consider the impact of various characteristics of the consumer on the spread of contamination, and provide recommendations for interventions to public health officials. This problem occurs when a chemical or biological agent, at any point in the food supply chain, contaminates a food product. The final product can reach the consumer from one of three distribution channels: food retail, food manufacturing, and food service.

These models consider several factors such as: purchasing and consumption patterns of consumers, geographic distribution of contaminated food products, and lag time between purchase and consumption. Data of these factors are gathered by a food consumer behavior survey or the CDC. Compartmental models are applied to simulate different cases of consumers and food, causing illness from a food contamination event and incorporate a diverse set of interventions into the system. This will help to understand the spread of contamination and the reduction of people that can become ill. The results will show the number of consumers exposed increases linearly as a function of the consumption and purchasing. Using some type of control measure can reduce the amount of illnesses in the total food supply chain for a food system. Timing of food recalls can change the dynamics of the food system.

Click here to view webcast.

Jan
23
Tue
2018
Alison L. Hill, Harvard University, “Countdown to a Cure? Mathematical Approaches to Designing Better HIV Treatments”
Jan 23 @ 11:00 am – 12:00 pm

Jump to:

Bio

“Countdown to a Cure? Mathematical Approaches to Designing Better HIV Treatments”

Dr. Alison Hill is a Research Fellow and independent principle investigator at Harvard University’s Program for Evolutionary Dynamics. She holds a NIH Director’s Early Independence Award and is a  member of the John Harvard Distinguished Science Fellows program. Dr. Hill develops mathematical and computational tools to help better understand, predict, and treat infectious diseases, with a particular focus on human viral infections including HIV/AIDS. Hill received her  PhD in 2013 through Harvard’s Biophysics Program and was a joint graduate student in the Harvard-MIT Division of Health-Sciences and Technology (HST)’s Medical Engineering and Medical Physics program. She received her undergraduate degree in Physics from Queen’s University in Ontario, Canada.

 

Click here to view webcast.

Abstract

“Countdown to a Cure? Mathematical Approaches to Designing Better HIV Treatments”

 

Individuals infected with HIV can now be prevented from progressing to AIDS, thanks to the introduction of combination antiretroviral therapy. However, these drugs are still unable to cure the infection, because long-lived latent virus persists despite years of treatment. If drugs are stopped at any point, the infection will quickly rebound, which makes global control of the epidemic extremely difficult. In this talk I will discuss how we use mathematical models of virus dynamics to evaluate new methods to cure HIV. First, I will show how these models have helped us understand how much the pool of latent virus must be reduced to delay or prevent the infection from rebounding when drugs are stopped. We explain why existing anti-latency drugs have had negligible benefit, and why we have seen multiple cases of apparent (but false) “cures” of HIV. We argue that math models should be used to help plan and interpret future clinical trials. Secondly, I will present a method to infer the mechanism of persistence of latent virus, based on the way the virus “tags” the cells it infects. We show that the natural proliferation of HIV-infected white blood cells drives virus persistence, and suggest that therapies to target this process could be highly effective. Overall, this work highlights the role that simulation, analysis, and inference using mathematical models can play in informing new potentially-curative treatments for HIV.

 

Click here to view webcast.

Feb
6
Tue
2018
Cancelled: Danielle S. Bassett, University of Pennsylvania, “Implications of Brain Network Architecture for Intrinsic and Exogenous Control”
Feb 6 @ 11:00 am – 12:00 pm

Jump to:

Bio

“Implications of Brain Network Architecture for Intrinsic and Exogenous Control”

Dr. Danielle Bassett is the Eduardo D. Glandt Faculty Fellow and Associate Professor in the Department of Bioengineering at the University of Pennsylvania. She is most well-known for her work blending neural and systems engineering to identify fundamental mechanisms of cognition and disease in human brain networks. She received a B.S. in physics from the Pennsylvania State University and a Ph.D. in physics from the University of Cambridge, UK. Following a postdoctoral position at UC Santa Barbara, she was a Junior Research Fellow at the Sage Center for the Study of the Mind. In 2012, she was named American Psychological Association’s `Rising Star’ and given an Alumni Achievement Award from the Schreyer Honors College at Pennsylvania State University for extraordinary achievement under the age of 35. In 2014, she was named an Alfred P Sloan Research Fellow and received the MacArthur Fellow Genius Grant. In 2015, she received the IEEE EMBS Early Academic Achievement Award, and was named an ONR Young Investigator. In 2016, she received an NSF CAREER award and was named one of Popular Science’s Brilliant 10. In 2017 she was awarded the Lagrange Prize in Complexity Science. She is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Her work — which has resulted in 153 accepted or published articles — has been supported by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Army Research Laboratory, the Alfred P Sloan Foundation, the John D and Catherine T MacArthur Foundation, and the Office of Naval Research.

Abstract

“Implications of Brain Network Architecture for Intrinsic and Exogenous Control”

 

The human brain is a complex organ characterized by a heterogeneous pattern of structural connections that supports long-range functional interactions. Non-invasive imaging techniques now allow for these patterns to be carefully and comprehensively mapped in individual humans, paving the way for a better understanding of how the complex network architecture of structural wiring supports our thought processes. While a large body of work now focuses on descriptive statistics to characterize these wiring patterns, a critical open question lies in how the organization of these networks constrains the potential repertoire of brain dynamics. In this talk, I will describe an approach for understanding how perturbations to brain dynamics propagate through complex wiring patterns, driving the brain into new states of activity. Drawing on a range of disciplinary tools – from graph theory to network control theory and optimization – I will identify controlpoints in brain networks, characterize trajectories of brain activity states following perturbation to those points, and propose a mechanism for how network control evolves in our brains as we grow from children into adults. Finally, I will describe how these computational tools and approaches can be used to better understand how the brain controls its own dynamics (and we in turn control our own behavior), and also how we can inform stimulation devices to control abnormal brain dynamics, for example in patients with severe epilepsy.

Feb
8
Thu
2018
ACCM 2018 Research Retreat
Feb 8 @ 5:00 pm – 8:00 pm

Please register to attend here.

JHU - Institute for Computational Medicine