Tag: Modeling

Year in Review: Our Top Three Posts of 2020

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Over the year, we dove into the inner workings of cells, interviewed award-winning researchers supported by NIGMS, shared a cool collection of science-themed backgrounds for video calls, and more. Here, we highlight three of the most popular posts from 2020. Tell us which of this year’s posts you liked best in the comments section below!

The Science of Infectious Disease Modeling

Oblong light-blue structures with red spots in the middle connected to the surface of a sphere. Spike proteins on the surface of a coronavirus. Credit: David Veesler, University of Washington.

What does “modeling the spread” (or “flattening the curve”) mean, and how does it apply to infectious diseases such as COVID-19? Learn about the science of infectious disease modeling and how NIGMS supports scientists in the field.

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Learn Directly From Scientists Through Available Webinar Series

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Looking for more virtual learning opportunities? NIGMS recently recorded a series of 14 webinars where experts shared their knowledge on topics from infectious disease modeling to pursuing a career in biomedical science. With the start of the 2020-2021 academic year, we’re highlighting a webinar that’s particularly relevant for our Biomedical Beat readers who are educators. You can check out the whole series on the NIGMS YouTube channel.

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The Science of Infectious Disease Modeling

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What Is Computer Modeling and How Does It Work?

Recent news headlines are awash in references to “modeling the spread” and “flattening the curve.” You may have wondered what exactly this means and how it applies to the COVID-19 pandemic. Infectious disease modeling is part of the larger field of computer modeling. This type of research uses computers to simulate and study the behavior of complex systems using mathematics, physics, and computer science. Each model contains many variables that characterize the system being studied. Simulation is done by adjusting each of the variables, alone or in combination, to see how the changes affect the outcomes. Computer modeling is used in a wide array of applications, from weather forecasting, airplane flight simulation, and drug development to infectious disease spread and containment.

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Revealing a Piece of Cilia’s Puzzle

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A multicolored tube made up of small dots with three sets of appendages attached along its length. A partial model of a doublet microtubule. Credit: Veronica Falconieri.

Cilia (cilium in singular) are complex organelles found on all of our cells except red blood cells. Their rhythmic beating moves fluid or materials over the cell to help transport food and oxygen or remove debris. For example, cilia in our windpipe prevent bacteria and mucous from traveling to the lungs. Some pick up signals like antennae, such as cilia in our ears that help detect sounds. One component of cilia is the doublet microtubule, a major part of cilia’s skeleton that gives it strength and rigidity.

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Advances in 3D Printing of Replacement Tissue

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A bioprint of the small air sac in the lungs with red blood cells moving through a vessel network supplying oxygen to living cells. Credit: Rice University. A bioprint of the small air sac in the lungs with red blood cells moving through a vessel network supplying oxygen to living cells. Credit: Rice University.

A team of bioengineers, funded in part by NIGMS, has devised a way to use 3D bioprinting technology to construct the small air sacs in the lungs and intricate blood vessels. Continue reading “Advances in 3D Printing of Replacement Tissue”

Amazing Organisms and the Lessons They Can Teach Us

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What do you have in common with rodents, birds, and reptiles? A lot more than you might think. These creatures have organs and body systems very similar to our own: a skeleton, digestive tract, brain, nervous system, heart, network of blood vessels, and more. Even so-called “simple” organisms such as insects and worms use essentially the same genetic and molecular pathways we do. Studying these organisms provides a deeper understanding of human biology in health and disease, and makes possible new ways to prevent, diagnose, and treat a wide range of conditions.

Historically, scientists have relied on a few key organisms, including bacteria, fruit flies, rats, and mice, to study the basic life processes that run bodily functions. In recent years, scientists have begun to add other organisms to their toolkits. Many of these newer research organisms are particularly well suited for a specific type of investigation. For example, the small, freshwater zebrafish grows quickly and has transparent embryos and see-through eggs, making it ideal for examining how organs develop. Organisms such as flatworms, salamanders, and sea urchins can regrow whole limbs, suggesting they hold clues about how to improve wound healing and tissue regeneration in humans.

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Interview With a Scientist – Rommie Amaro: Computational and Theoretical Model Builder

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Many researchers who search for anti-cancer drugs have labs filled with chemicals and tissue samples. Not Rommie Amaro. Her work uses computers to analyze the shape and behavior of a protein called p53. Defective versions of p53 are associated with more human cancers than any other malfunctioning protein.

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Online Virus Tracking Tool Nextstrain Wins Inaugural Open Science Prize

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Nextstrain’s analysis of the genomes from Zika virus obtained in 25 countries over the past few years.

Credit: Trevor Bedford and Richard Neher, nextstrain.org.

Over the past decade, scientists and clinicians have eagerly deposited their burgeoning biomedical data into publicly accessible databases. However, a lack of computational tools for sharing and synthesizing the data has prevented this wealth of information from being fully utilized.

In an attempt to unleash the power of open-access data, the National Institutes of Health, in collaboration with the Howard Hughes Medical Institute and Britain’s Wellcome Trust, launched the Open Science Prize Exit icon. Last week, after a multi-stage public voting process, the inaugural award was announced. The winner of the grand prize—and $230,000—is a prototype computational tool called nextstrain Exit icon that tracks the spread of emerging viruses such as Ebola and Zika. This tool could be especially valuable in revealing the transmission patterns and geographic spread of new outbreaks before vaccines are available, such as during the 2013-2016 Ebola epidemic and the current Zika epidemic.

An international team of scientists—led by NIGMS grantee Trevor Bedford of the Fred Hutchinson Cancer Research Center, Seattle, and Richard Neher Exit icon of Biozentrum at the University of Basel, Switzerland—developed nextstrain as an open-access system capable of sharing and analyzing viral genomes. The system mines viral genome sequence data that researchers have made publicly available online. nextstrain then rapidly determines the evolutionary relationships among all the viruses in its database and displays the results of its analyses on an interactive public website.

The image here shows nextstrain’s analysis of the genomes from Zika virus obtained in 25 countries over the past few years. Plotting the relatedness of these viral strains on a timeline provides investigators a sense of how the virus has spread and evolved, and which strains are genetically similar. Researchers can upload genome sequences of newly discovered viral strains—in this case Zika—and find out in short order how their new strain relates to previously discovered strains, which could potentially impact treatment decisions.

Nearly 100 interdisciplinary teams comprising 450 innovators from 45 nations competed for the Open Science Prize. More than 3,500 people from six continents voted online for the winner. Other finalists for the prize focused on brain maps Exit icon, gene discovery Exit icon, air-quality monitoring Exit icon, neuroimaging Exit icon and drug discovery Exit icon.

nextstrain was funded in part by NIH under grant U54GM111274.

Forecasting Infectious Disease Spread with Web Data

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Just as you might turn to Twitter or Facebook for a pulse on what’s happening around you, researchers involved in an infectious disease computational modeling project are turning to anonymized social media and other publicly available Web data to improve their ability to forecast emerging outbreaks and develop tools that can help health officials as they respond.

Mining Wikipedia Data

Screen shot of the Wikipedia site
Incorporating real-time, anonymized data from Wikipedia and other novel sources of information is aiding efforts to forecast and respond to emerging outbreaks. Credit: Stock image.

“When it comes to infectious disease forecasting, getting ahead of the curve is problematic because data from official public health sources is retrospective,” says Irene Eckstrand of the National Institutes of Health, which funds the project, called Models of Infectious Disease Agent Study (MIDAS). “Incorporating real-time, anonymized data from social media and other Web sources into disease modeling tools may be helpful, but it also presents challenges.”

To help evaluate the Web’s potential for improving infectious disease forecasting efforts, MIDAS researcher Sara Del Valle of Los Alamos National Laboratory conducted proof-of-concept experiments involving data that Wikipedia releases hourly to any interested party. Del Valle’s research group built models based on the page view histories of disease-related Wikipedia pages in seven languages. The scientists tested the new models against their other models, which rely on official health data reported from countries using those languages. By comparing the outcomes of the different modeling approaches, the Los Alamos team concluded that the Wikipedia-based modeling results for flu and dengue fever performed better than those for other diseases. Continue reading “Forecasting Infectious Disease Spread with Web Data”

Field Focus: Asking Our Expert About Modeling Ebola

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Irene Eckstrand
NIGMS’ Irene Eckstrand answers questions about modeling Ebola. Credit: National Institute of General Medical Sciences.

Ebola is the focus of many NIH-supported research efforts, from analyzing the genetics of virus samples to evaluating the safety and effectiveness of treatments and vaccines. Researchers involved in our Models of Infectious Disease Agent Study, or MIDAS, have been using computational methods to forecast the potential course of the outbreak and the impact of various intervention strategies.

Wondering how their work is going, I recently asked our modeling expert Irene Eckstrand a few questions.

How useful are the forecasts?

Forecasts give us a range of possible outcomes. In addition to being a useful public health tool to prepare for an outbreak, they’re an important research tool to test assumptions about how a disease may spread. When we compare the predicted and actual outcomes, we can confirm assumptions, such as the groups of individuals who are more likely to spread the infection to others. Continually doing this helps refine the models and ensure that their forecasts are as accurate as possible.

What are some of the challenges the modelers face?

Ebola virus
Ebola virus particles (green) attached to and budding from a cell (blue). Credit: National Institute of Allergy and Infectious Diseases.

We need data to build and test models. The data available from this outbreak have been more limited than in most previous outbreaks of Ebola simply because the public health systems are overwhelmed with sick people, and recording information is a secondary priority.

Another issue with forecasting future trends is incorporating information about the deployment of resources and the implementation of interventions that actually slowed the outbreak. We also need to incorporate changes in people’s behavior. If people think an outbreak is leveling off, they may relax the precautions they’ve been taking—and that could lead to another spike in the disease.

What other Ebola-related projects are the MIDAS modelers working on?

The MIDAS researchers are:

  • Modeling logistical factors such as the number and placement of treatment beds and staffing needs.
  • Tracking potential transmission within and between communities and at hospitals and funerals.
  • Developing a method to estimate the amount of underreporting of case data.
  • Applying models of “tipping points” to look for evidence that the disease curve is slowing.
  • Estimating the number of people who are infected but not symptomatic.
  • Creating new resources for Ebola modelers, including standards for using infectious disease data.
  • Calculating the risk of importation of cases for a wide variety of countries based on travel networks.

How are the modelers working together?

The MIDAS modelers conference call 1-2 times a week to discuss results, modeling strategies, data sources and questions amenable to modeling. They also participate in discussions with government and other academic groups, so there’s a sizable number of modelers working on a wide variety of public health, logistical and basic research questions.

If you’re interested in learning more about Ebola, Irene recommends a video overview of the 2014 outbreak from Penn State University Exit icon and a slide presentation on the myths and realities of the disease from Nigeria’s Kaduna State University Exit icon.