The University of Pittsburgh and biopharmaceutical company Pfizer Inc. have announced a partnership to develop a computational model that will help identify the drivers of schizophrenia, Alzheimer's disease, and related brain diseases and enable researchers to better understand and treat the diseases.

Kayhan Batmanghelich, assistant professor in the Department of Biomedical Informatics at Pitt's School of Medicine, will be the principal investigator in the one-year study. The goal of the study is to develop a statistical model that relates abnormal anatomical variations of brain structure to the underlying genetic markers of the diseases in order to develop an algorithm that explains causal relationships between such heterogeneous data, and to be able to use the method in similar settings for precision medicine.

In addition to the genotype data, measurements from magnetic resonance brain images will be used to characterize abnormal brain variations.

"By studying brain images and relating the variations of each brain region to the genetics and clinical observations of patients, we provide deeper insight about the underlying biology of the diseases," said Batmanghelich.

The study will use the publicly available datasets of ADNI (Alzheimer's Disease Neuroimaging Initiative) and private datasets of the GENUS (Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia) Consortium, both of which contain images, genetic information, biological information, and clinical observations of patients, to develop software that can be used to associate the images with gene patterns.

"The exciting thing about this type of translational research with Pfizer is that it expands the research impact of what we do at Pitt, inclusively involves participation across our campus, and meets the core missions of both our University and industry partner," said Donald Taylor, assistant vice chancellor for commercial translation in the health sciences at Pitt. "Discovering the relationship between the disease status and the results of imaging and genetic positions to search for undiscovered variables in images and DNA also leverages our core commercial translation themes in precision medicine, brain health, and digital health. We wouldn't be able to do this specific research without an industry partner, and we're thrilled to have Pfizer's collaboration."

When disease outbreaks occur, people with essential roles - healthcare workers, first responders, and teachers, for example - are typically up close and personal with infected people. As these front-line workers become infected, healthy individuals take their places.

Mathematical biologist Samuel Scarpino, who creates and analyzes epidemiological models, wondered how this exchange of critical people affects the spread of disease. The practice clearly raises the risk of infection for the replacement individuals - but the population dynamics of this increase are neglected in existing epidemiological models. 

Scarpino, while he was an Omidyar Fellow at the Santa Fe Institute, set out to quantify that risk and understand its influence. He enlisted the help of theoretical physicist Laurent Hébert-Dufresne, a James S. McDonnell Fellow at the Institute, and Antoine Allard, Hébert-Dufresne's longtime collaborator from the University of Barcelona. Both study complex patterns in networks.

The trio integrated this "human exchange" into network models of disease and found that replacing sick individuals with healthy ones can actually accelerate the spread of infection. Scarpino and Hébert-Dufresne tested their ideas on 17 years' worth of data on two diseases: influenza and dengue. Their analysis, just published in Nature Physics, reveals that human exchange likely accelerates outbreaks of influenza, which spreads via human contact. But it has no effect on the spread of dengue - which makes sense, as dengue spreads via mosquitoes. 

"We didn't see a strong signal in diseases where we didn't expect it," says Hébert-Dufresne. 

Scarpino, now an assistant professor at the University of Vermont, says he hopes to see this effect integrated into future epidemiological models. "Models where you start to incorporate slightly more realistic human behavior are essential if we're going to make high-fidelity public health and clinical decisions," he says.

Tomorrow, Sierra Leone is expected to be declared Ebola-free by the World Health Organisation. But what was key to eliminating the disease? A mathematical model published in the Journal of Biological Dynamics describes the reasons as complex, but confirms that increased identification and isolation of infectious cases played the major role.

Sierra Leone is situated in a region with many deep-rooted cultural traditions, and changing the way that death is mourned was key to stemming the spread of the disease. This includes deterring individuals from making physical contact with deceased and infected bodies in order to avoid infection.

The authors of the study are Prof. Glenn Webb and Prof. Cameron Browne of Vanderbilt University, Nashville, USA. They state: “Our model simulations indicate that this enhanced removal of infectious individuals is key to elimination of the epidemic. This removal is quantified in our model in two ways: (1) with respect to the rate of infectious individuals hospitalized per day, and (2) with respect to an earlier disease age of the infectious individuals hospitalized. Both of these considerations are critical for epidemic control and both can be influenced by public health policies and public awareness.”

This open access study is available online now. The MATHEMATICA computer code developed for the simulations is available on request via the authors.

http://www.tandfonline.com/doi/full/10.1080/17513758.2015.1090632

New method maps 2014 outbreak in Sierra Leone, can be used in real-time for future disease outbreaks elsewhere

Using a novel statistical model, a research team led by Columbia University's Mailman School of Public Health mapped the spread of the 2014-2015 Ebola outbreak in Sierra Leone, providing the most detailed picture to date on how and where the disease spread and identifying two critical opportunities to control the epidemic. The result, published in the Journal of the Royal Society Interface, matches with details known about the early phase of the Ebola outbreak, suggesting the real-time value of the method to health authorities as they plan interventions to contain future outbreaks, and not just of Ebola.

Their analysis uses data from the Sierra Leone Ministry of Health and Sanitation to chart the course of the Ebola outbreak, beginning with the arrival of the disease in the border district of Kailahun in late May 2014. By mid-June, Ebola spread west to nearby Kenema--a pathway consistent with a recent field investigation. At its peak, 67 percent of Ebola cases in Kenema were imported from Kailahun; by early July, the epidemic was firmly established in Kenema with most cases infected locally. From Kenema, the outbreak continued west, south, and north. Beginning in early July, a second path emerged in capital city, Freetown, spreading east to Port Loko by late July, then quickly east and south.

Because of their many connections to other districts, Kenema and Port Loko were critical junction points for the outbreak. At these points, windows of opportunity may have existed for controlling the spread of Ebola within Sierra Leone, the study suggests. The researchers estimate that first window, before Ebola reached Kenema, was approximately one month. The second window, before it reached Port Loko, was much shorter.

The method described in the paper uses three principal ingredients: the home district of the Ebola-positive patient, the population of that district, and geographic distance between districts--all information that was available during the outbreak.

"While this analysis is too late to be used for application to and intervention in the Ebola epidemic, the method we used could be useful for future disease outbreaks, and not just for Ebola," says Jeffrey Shaman, PhD, the study's senior author and associate professor of Environmental Health Sciences at the Mailman School.

"To be able to infer the spatial-temporal course of an outbreak and the rate of its spread between population centers in real time," Shaman continues, "may greatly aid public health planning, including the level and speed of deployment of intervention measures such as how many doctors and beds are needed and where to put them."

The traditional method to track disease spread is contract tracing, in which health workers interview patients and everyone they came into contact with. "Contact tracing is highly labor intensive," says lead author Wan Yang, PhD, associate research scientist at the Mailman School. "Especially in resource-poor areas, an epidemic like Ebola can easily outrun any such effort to track it. The minimal information needed in our method makes it a particularly valuable tool to aid public health efforts during a novel disease outbreak in these areas."

During the Ebola outbreak, there was a collapse of the healthcare system in Sierra Leone. Observational data were very limited and error-laden. "Having the ability to infer the course of the outbreak gives officials the ability to see what's happening rather than flying completely blind," Shaman says. "In a public health emergency, it's critical that they have as much information as possible so they can make informed decisions.

"If you had perfect observation," Shaman adds, "you wouldn't need these methods, but you're never going to get that."

Previous work by Shaman and Yang has used computational methods to predict infectious disease spread. Beginning in the summer of 2014, they generated weekly estimates of countrywide Ebola incidence in Sierra Leone, Guinea, and Liberia. They also developed a prize-winning method to forecast seasonal influenza. Forecasts are available online at Columbia Prediction of Infectious Diseases.

Virginia Tech researchers write the book on immunology modeling

The complexity of the human immune response has been difficult to characterize on a 'big picture' level, but researchers at the Virginia Bioinformatics Institute at Virginia Tech have written the book on how it can be done.

'Computational Immunology: Models and Tools,' explains a set of techniques that enable researchers to study immunity at an unprecedented scale: simulated immune systems with tens of billions of interacting components.

"What makes this approach so exciting is that it reveals just how time and context-dependent our immune functions really are," said Josep Bassaganya-Riera, the book's editor and director of the Nutritional Immunology and Molecular Medicine Laboratory at the Virginia Bioinformatics Institute. "Modeling and informatics tools allow researchers to connect immunology to the world above the skin, testing interventions in virtual laboratories to guide human studies."

Understanding the human immune response has posed significant challenges in characterizing adaptive behavior, heterogeneity, and spatial complexity at the systems level.

While incremental progress has been made by studying key components of the immune system in isolation, technical limitations prevented researchers from efficiently investigating how all these interdependent pieces work together to influence functions at the systems level.

The new book offers methods for overcoming those obstacles.

The first reference of its kind, this volume was produced through a series of collaborations between computer scientists and immunologists at the Center for Modeling Immunity to Enteric Pathogens. Established with support from the National Institute of Allergy and Infectious Diseases, this center is dedicated to developing and disseminating user-friendly models for studying the immune system.

"'Computational Immunology: Models and Tools' provides valuable insights on the art of team science and how computer scientists, mathematicians, immunologists and bioinformaticians can successfully work together to build products that are greater than the sum of their parts," said Vida Abedi, a contributing author to the book and a member of the Nutritional Immunology and Molecular Medicine Laboratory.

To illustrate how these methods can be applied and refined, the book uses examples from studies modeling the body's complicated immune response to H. pylori, a common gut bacterium carried by half the world's population, and inflammatory bowel disease, a debilitating immune-mediated disease that afflicts over 4 million people worldwide.

"The use cases included in the book encompass our entire process for knowledge discovery from generating new hypotheses based on simulation data to testing those predictions in the lab," said Raquel Hontecillas, a contributing author to the book and co-director of the Nutritional Immunology and Molecular Medicine Laboratory. "In one illustration, our computational hypothesis allowed us to identify a population of CX3CR1+ macrophages that may help to regulate inflammation resulting from infection or auto-immune disease."

By developing technologies with the ability to predict how our bodies will manage disease, the book's authors hope to accelerate the path to cures by quickly identifying leads for further study and uncovering hidden truths about how our immune system operates.

"The development and use of advanced information and communication technologies in support of lab hypothesis generation and to developing novel interventions is fundamental to the advancement of precision medicine," said Bruno Sobral, a professor and director of the One Health Institute at Colorado State University and senior scientific adviser to the Virginia Bioinformatics Institute's Pathosystems Resource Integration Center team. "The coupling of systems of microorganisms with the mammalian immune system provides a fertile ground for such transformative innovation."

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