To be presented at the 2015 APS March Meeting in San Antonio, Texas, March 5

A team of Cornell University researchers focusing on a fictional zombie outbreak as an approach to disease modeling suggests heading for the hills, in the Rockies, to save your brains from the undead.

Reading World War Z, an oral history of the first zombie war, and a graduate statistical mechanics class inspired a group of Cornell University researchers to explore how an "actual" zombie outbreak might play out in the U.S.

During the 2015 American Physical Society March Meeting, on Thursday, March 5 in San Antonio, Texas, the group will describe their work modeling the statistical mechanics of zombies--those thankfully fictional "undead" creatures with an appetite for human flesh. (See the abstract:

Why model the mechanics of zombies? "Modeling zombies takes you through a lot of the techniques used to model real diseases, albeit in a fun context," says Alex Alemi, a graduate student at Cornell University.

Alemi and colleagues' work offers a nice introduction to disease modeling in general, as well as some techniques of statistical physics for measuring second-order phase transitions. "It's interesting in its own right as a model, as a cousin of traditional SIR [susceptible, infected, and resistant] models--which are used for many diseases--but with an additional nonlinearity," points out Alemi.

All told, the project was an overview of modern epidemiology modeling, starting with differential equations to model a fully connected population, then moving on to lattice-based models, and ending with a full U.S.-scale simulation of an outbreak across the continental U.S.

It involved a lot of computational results generated from simulations the researchers wrote themselves. "At their heart, the simulations are akin to modeling chemical reactions taking place between different elements and, in this case, we have four states a person can be in--human, infected, zombie, or dead zombie--with approximately 300 million people," Alemi explains.

The project's large-scale simulations are stochastic in nature, meaning that they have an element of randomness. "Each possible interaction--zombie bites human, human kills zombie, zombie moves, etc.--is treated like a radioactive decay, with a half-life that depends on some parameters, and we tried to simulate the times it would take for all of these different interactions to fire, where complications arise because when one thing happens it can affect the rates at which all of the other things happen," he says.

In most films or books, "if there is a zombie outbreak, it is usually assumed to affect all areas at the same time, and some months after the outbreak you're left with small pockets of survivors," explains Alemi. "But in our attempt to model zombies somewhat realistically, it doesn't seem like this is how it would actually go down."

Cities would fall quickly, but it would take weeks for zombies to penetrate into less densely populated areas, and months to reach the northern mountain-time zone.

"Given the dynamics of the disease, once the zombies invade more sparsely populated areas, the whole outbreak slows down--there are fewer humans to bite, so you start creating zombies at a slower rate," he elaborates. "I'd love to see a fictional account where most of New York City falls in a day, but upstate New York has a month or so to prepare."

If you somehow happen to find yourself in the midst of a fictional zombie outbreak and want to survive as long as possible, Alemi recommends making a run for the northern Rockies. While not an entirely practical implication, it's "fun to know," he says, and points out the benefits of applying hard science to fun topics--especially to help make learning more entertaining and enjoyable.

"A lot of modern research can be off-putting for people because the techniques are complicated and the systems or models studied lack a strong connection to everyday experiences," Alemi adds. "Not that zombies are an everyday occurrence, but most people can wrap their braains around them."

What's next for Alemi and colleagues? "Given the time, we could attempt to add more complicated social dynamics to the simulation, such as allowing people to make a run for it, include plane flights, or have an awareness of the zombie outbreak, etc.," he notes. 

Researchers have developed a novel three-dimensional, multiscale and multicomponent model of endothelial cells monolayer, the inner lining of artery, to identify the cellular mechanisms involved in cardiovascular diseases (CVD). New research based on the model is able to identify the main cellular pathways involved in the initiation and progression of the disease.  

The model allows researchers, for the first time, to see how changes in  blood flow patterns are transmitted within cell monolayers and through the cellular components. In certain regions of the vasculature, an increased permeability of the blood vessel endothelium enhances the accumulation of cholesterol-laden low-density lipoprotein (LDL) along with the transmigration of neutrophil leukocytes (white blood cells) from the bloodstream into the vessel wall inner layer. In these regions, it is believed the disturbed blood flow is linked to the cellular shape change and activation of all related mechanisms.

The model also quantifies the intracellular and intercellular mechanical stresses in a confluent vascular monolayer, for the first time. The model is able to cross the boundaries between different length scales in order to provide a global view of potential locations for the disease activated by shear forces from blood.

The model allowed the researchers to answer the questions that experiments could not answer. For example, the model estimates the forces per molecule in the cell attachment points to the external cellular matrix and cell–cell adhesion points. The research suggests that direct force-induced activation by single molecules is possible at both signaling pathways.

Help with developing drugs

The model could provide a solid basis for the design of most effective therapeutics to prevent the progression of CVDs. According to Dabaghmeshin, there are a lot of medicines for the disease but not all of them are effective because they are designed to treat the causes of the CVDs but not the cellular responses to the causes.

“The root is that there are pathways that become activated by e.g., age-related wall stiffness and if we can somehow prevent that activation, then that’s the point. The drug should aim to target the cellular responses to wall stiffness, rather than stiffness itself,” Dabaghmeshin explains.

The model can be also extended to other applications from studying microfluidics to nano-materials.

The first comprehensive supercomputer model to simulate the development of blood cells could help in the development of new treatments for leukaemia and lymphoma, say researchers at the University of Cambridge and Microsoft Research.

The human body produces over 2.5 million new blood cells during every second of our adult lives, but how this process is controlled remains poorly understood. Around 30,000 new patients each year are diagnosed with cancers of the blood each year in the UK alone. These cancers, which include leukaemia, lymphoma and myeloma, occur when the production of new blood cells gets out of balance, for example if the body produces an overabundance of white blood cells. 

Biomedical scientists from the Wellcome Trust-MRC Cambridge Stem Cell Institute and the Cambridge Institute for Medical Research collaborated for the past 2 years with computational biologists at Microsoft Research and Cambridge University's Department of Biochemistry. This interdisciplinary team of researchers have developed a computer model to help gain a better understanding of the control mechanisms that keep blood production normal. The details are published today in the journal Nature Biotechnology.

"With this new computer model, we can carry out simulated experiments in seconds that would take many weeks to perform in the laboratory, dramatically speeding up research into blood development and the genetic mutations that cause leukaemia," says Professor Bertie Gottgens whose research team is based at the University's Cambridge Institute for Medical Research.

Dr Jasmin Fisher from Microsoft Research and the Department of Biochemistry at the University of Cambridge says: "This is yet another endorsement of how computer programs empower us to gain better understanding of remarkably complicated processes. What is ground-breaking about the current work is that we show how we can automate the process of building such programs based on raw experimental data. It provides us with a blueprint to develop computer models relevant to other human diseases including common cancers such as breast and colon cancer."

To construct the supercomputer model, PhD student Vicki Moignard from the Stem Cell Institute measured the activity of 48 genes in over 3,900 blood progenitor cells that give rise to all other types of blood cell: red and white blood cells, and platelets. These genes include TAL1 and RUNX1, both of which are essential for the development of blood stem cells, and hence to human life.

Computational biology PhD student Steven Woodhouse then used the resulting dataset to construct the computer model of blood cell development, using computational approaches originally developed at Microsoft Research for synthesis of computer code. Importantly, subsequent laboratory experiments validated the accuracy of this new computer model.

One way the computer model can be used is to simulate the activity of key genes implicated in blood cancers. For example, around one in five of all children who develop leukaemia has a faulty version of the gene RUNX1, as does a similar proportion of adults with acute myeloid leukaemia, one of the most deadly forms of leukaemia in adults. The computer model shows how RUNX1 interacts with other genes to control blood cell development: the gene produces a protein also known as Runx1, which in healthy patients activates a particular network of key genes; in patients with leukaemia, an altered form of the protein is thought to suppress this same network. If the researchers change the 'rules' in the network model, they can simulate the formation of abnormal leukaemia cells. By tweaking the leukaemia model until the behaviour of the network reverts back to normal, the researchers believe they can identify promising pathways to target with drugs.

Professor Gottgens adds: "Because the computer simulations are very fast, we can quickly screen through lots of possibilities to pick the most promising ones as pathways for drug development. The cost of developing a new drug is enormous, and much of this cost comes from new candidate drugs failing late in the drug development process. Our model could significantly reduce the risk of failure, with the potential to make drug discovery faster and cheaper."

The research was supported by the Medical Research Council, the Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and the Wellcome Trust.

Dr Matt Kaiser, Head of Research at UK blood cancer charity Leukaemia & Lymphoma Research, which has funded Professor Gottgens' team for over a decade, said: "For some leukaemias, the majority of patients still ultimately die from their disease. Even for blood cancers for which the long-term survival chances are fairly good, such as childhood leukaemia, the treatment can be really gruelling. By harnessing the power of cutting-edge computer technology, this research will dramatically speed up the search for more effective and kinder treatments that target these cancers at their roots."

The Medical College of Wisconsin (MCW) has received a four-year, $8 million grant from the National Institutes of Health's National Heart, Lung and Blood Institute to fund the Rat Genome Database (RGD), a unique, globally-accessible collection of data from ongoing rat genetic and genomic research efforts.

Howard J. Jacob, Ph.D., the Warren P. Knowles Professor of Genetics and director of MCW's Human and Molecular Genetics Center, is the primary investigator on the grant.

The RGD was established at MCW in 1999 by Dr. Jacob and Peter J. Tonellato, Ph.D., senior research scientist at Harvard Medical School, to collect, consolidate and integrate data from genetic and genomic research into rat models and make the data widely available to the scientific community. Additionally, the RGD holds complete files of rat, human and mouse genes, as well as files on specific animal strains. 

RGD contains nearly 4.5 million functional data annotations for rat, human and mouse genes and information on 500,000 disease-specific annotations. In 2014, more than 180,000 users in 190 countries accessed RGD for scientific research data.

"The rat is a model organism for investigating the biology and pathophysiology of disease," said Dr. Jacob. "Additionally, our bioinformaticians under Dr. Mary Shimoyama, assistant professor of surgery and bioinformatics, have created tools to allow users to seamlessly move from the rat genomic region to the corresponding human genomic region, and to create comprehensive summaries of information on all three species in a single report, both of which are valuable tools to genomics researchers."

Including the new grant, funding for RGD has exceeded $35 million. The information curated within is free for users. Those users include research and academic institutions, medical schools and healthcare institutions, and pharmacy and biotechnology companies.

On Dec. 26, 2013, a two-year-old boy living in the Guinean village of Meliandou, Guéckédou Prefecture was stricken with a rare disease, caused by the filament-shaped Ebola virus.

The child is believed to be the first case in what soon became a flood-tide of contagion, ravaging the West African countries of Guinea, Sierra Leone and Liberia, infecting, according to the World Health Organization, over 21,000 cases as of Jan. 21, with nearly 9000 confirmed deaths--the actual toll likely much higher.

Now, researchers from Arizona State University and Georgia State University are trying to better understand the epidemiology and control of Ebola Virus Disease in order to alleviate suffering and prevent future disease outbreaks from reaching the catastrophic proportions of the current crisis.

In reports appearing in the February 2015 issue of the prestigious British medical journal The Lancet Infectious Disease, ASU researchers report on new efforts to model the impact of timely diagnostic testing on the spread of Ebola across populations. A better understanding of viral dissemination and techniques for disease management are vital if a similar calamity is to be avoided in the future.

Researchers from the Biodesign Institute, and the Simon A. Levin Mathematical, Computational and Modeling Sciences Center present a new study: Modelling the effect of early detection of Ebola. The study examines the levels of detection and patient isolation required to shut down transmission of Ebola.

In related research, Gerardo Chowell, a newly appointed faculty member in the School of Public Health at Georgia State University and adjunct faculty member in the ASU's Simon A. Levin Mathematical, Computational and Modeling Sciences Center, together with Cécile Viboud from the from the National Institutes of Health discuss recent large-scale modeling efforts to explain the spatial-temporal patterns of spread of the epidemic in Liberia. Chowell is also co-author of Ebola control: rapid diagnostic testing, which appears in the Lancet's correspondence section.

Wave of destruction

The Ebola virus has become notorious, not only for its highly contagious and lethal nature, but for the nightmarish assortment of symptoms collectively known as hemorrhagic fever. These may include vomiting of blood, bleeding from the eyes, ears, nose, mouth, rectum, internal bleeding, excruciating pain and the liquidization of internal organs.

The three West African nations centrally affected by the epidemic were acutely unprepared for the crisis. Treatment centers were rapidly swamped with severely ill patients. Resources for proper care, isolation of infected patients and even basic means of sterilization were soon depleted. Health care workers were especially vulnerable to infection.

A number of exacerbating factors contributed to the outbreak and rapid spread of Ebola in the region. Timber and mining activities have impacted densely forested regions and brought fruit bats--believed to be a natural reservoir for the virus--in closer contact with humans. Infected animals consumed as bush meat may also have planted early seeds of the disease in the vulnerable population. Long periods of civil unrest have left the area deeply impoverished and the health infrastructure fractured.

Meliandou, the town identified as ground zero, is situated in a forested area at the convergence point of Guinea, Liberia and Sierra Leone. Populations move fluidly across these porous borders, as impoverished residents are often on the move in search of work. These conditions created a perfect storm for the aggressive virus.

An additional factor fueling the explosive spread of Ebola in West Africa was the delayed and inadequate response to the crisis on the part of developed countries and global health organizations.

Time is the enemy

As the authors of the Lancet modeling study emphasize, breaking the chain of Ebola transmission presents intimidating challenges. After the development of symptoms, the virus is highly contagious and each new contact presents an opportunity for further spread of the disease.

Tracking all contacts of infected individuals can be a daunting challenge, even in first world settings, with low case numbers. In the absence of a vaccine or reliable therapeutic for Ebola, diagnosis of the disease at a pre-symptomatic stage and rapid isolation of infected individuals are the surest means for arresting further disease transmission.

According to Biodesign's Karen Anderson, PhD., "Early detection of Ebola infection provides the opportunity and time to safely isolate and treat individuals before they become contagious. Our findings show two key things: first, that the predicted impact of early diagnostic tests depends on existing public health measures. Second, there appears to be a tipping point, where early diagnosis of high-risk individuals, combined with adequate isolation, can markedly decrease the predicted number of infected individuals."

Stopping an epidemic in its tracks requires a reduction in a critical value known as the reproductive ratio or R0-- a measure of new infections generated by a single case over the course of the infectious period. The higher the number for R0, the more difficult an epidemic is to contain.

A technique known as polymerase chain reaction (PCR) can be used for pre-symptomatic identification of the Ebola virus. The current study models the expected outcomes on viral transmission of Ebola using PCR-based pre-symptomatic diagnosis and isolation of infected patients within 3 days of the onset of symptoms.

"Our results underscore the dramatic impact that diagnostic capacity can bring about during an Ebola epidemic to quickly identify Ebola cases before these start new chains of transmission in the community or health care settings," according to Diego Chowell, lead author of the study.

Carlos Castillo-Chavez, director of the Simon A. Levin Mathematical, Computational and Modeling Sciences Center, emphasises the power of mathematical modeling for understanding and limiting the scale of epidemics: "Finding that small differences in isolation effectiveness may have a large impact on epidemic size highlights the importance of evaluating novel diagnostic technologies at the population level using mathematical models," he says. "An intervention may not work or be effective unless it is effectively used beyond a tipping point." 

The authors urge the implementation of the strategy of pre-symptomatic diagnosis and rapid isolation, targeting high-risk individuals, including care givers and health care workers.

In his comment to the Lancet, Geraldo Chowell examines another mathematical model, put forward by Merler and his colleagues. This study models the course of the Ebola epidemic in Liberia, based on population structure and geography, including location of households, hospitals, and Ebola treatment units.

Chowell notes that the establishment of new treatment centers, isolation of new patients and distribution of household protection kits all likely played a role in curtailing the spread of Ebola in Liberia, relative to neighboring states of Guinea and Sierra Leone.

"Carefully calibrated mathematical models have potential to guide public health authorities to effectively respond to disease epidemics," Chowell says. "In the context of the Ebola epidemic in West Africa, several key factors, including delays in responding to the epidemic, behavior changes and increased public health infrastructure in the region in order to trace contacts of infected individuals and break chains of transmission through effective isolation have played a major role in shaping the trajectory of this epidemic."

The desperate need for early diagnosis of Ebola was further emphasized in Chowell's correspondence, which points out that most West African Ebola patients remained undiagnosed in their communities and the average time from symptom onset to diagnosis was about 5 days--a prescription for rapid, far-flung transmission of the disease.

While underscoring the diagnostic power of PCR, Chowell notes that such tests presently require transportation to a laboratory or transit center, causing critical delays in diagnosis and treatment and heightening transmission risks. His recommendation is to supplement these efforts with the distribution of point-of-care rapid tests that could be used in households for early protection.

Chowell and his colleagues conducted a simulation based on reducing the time between symptom onset and diagnosis, using rapid testing. The results were dramatic. If 60 percent of Ebola patients can be rapidly diagnosed and isolated (within 1 day of symptom onset), the proportion of the population eventually infected (known as the attack rate) drops from 80 percent to nearly zero.

A vaccine for Ebola?

While authors of the current Lancet papers model Ebola transmission and propose strategies to address future epidemics, ASU has also been on the forefront of efforts toward Ebola therapeutics and eventual vaccines.

Charles Arntzen, Ph.D., offered a prescient warning back in 2011 that the next outbreak of Ebola could be far more devastating than those in the past, if sufficient resources were not immediately brought to bear.

Regrettably, Arntzen's prediction became a reality with the recent epidemic, far surpassing in death toll and geographic extent all previous Ebola outbreaks combined.

Arntzen's earlier study in the Proceedings of the National Academy of Science described an experimental cocktail of monoclonal antibodies produced from tobacco plants, which showed considerable promise in animal studies.

The recent West African epidemic provided an unprecedented opportunity to test the effectiveness of the drug formula, developed with Arntzen's longtime collaborators at San Diego based MAPP Pharmaceuticals. Two health care workers returning to the U.S. after having been stricken with Ebola in Africa were treated with the drug, known as ZMapp. Both survived, offering the tantalizing potential for a safe, highly effective vaccine against the disease.

Arntzen's efforts also highlighted the potential of similar plant-made pharmaceuticals. A number of these are currently being investigated at the Biodesign Institute by Qiang "Shawn" Chen, Ph.D., a researcher in the Center for Infectious Diseases and Vaccinology. Chen hopes to apply similar techniques to produce therapeutics against other diseases, including West Nile Fever, a focus of current research.

According to the latest reports from the World Health Organization, the Ebola epidemic appears to be weakening its grip on the region. For the first time since June 2014, there have been fewer than 100 new weekly cases reported in the 3 countries most affected, signaling what health care workers hope is the final phase of Ebola's devastating reign.

Increased vigilance and new tools at both the epidemiological and therapeutic ends of the spectrum are vitally needed, if another epidemic--perhaps of even greater scale--is to be prevented.

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