Reducing lung cancer mortality through modeling and simulations

By | Feature, Research

Lung cancer remains the leading cause of cancer related mortality in the US, and globally, accounting for 1.8 million deaths annually. Many of these deaths are preventable by the implementation of prevention strategies, including tobacco control policies and lung cancer screening recommendations, and by improvements in lung cancer treatment.  In the US, these policies have generally been implemented based on the analysis and outcomes of the population as a whole, although data analyses have shown that smoking and lung cancer rates, and access to healthcare and interventions, vary significantly by education, income, and race/ethnicity.

The Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Working Group (LWG), led by Rafael Meza, associate professor of Epidemiology from the School of Public Health and MICDE member, has been awarded a new $8.5M grant to investigate the synergistic impacts of tobacco control policies, lung cancer screening and treatment interventions in the US and in middle-income nations. For the past 15 years, the CISNET LWG has contributed to the development of US national strategies for reducing the lung cancer burden by quantifying, through modeling and simulation, the impact of tobacco control on smoking, lung cancer, and overall mortality, as well as the population benefits and harms of lung cancer screening. This new grant will allow the group to expand their work to consider the impact of treatment improvements, including targeted therapies and immunotherapies,  and the synergies between treatment and prevention interventions. It also will enable the researchers to continue their work in addressing smoking and lung cancer disparities. The consortium uses a comparative modeling approach, where multiple, but distinct, models use the same data inputs, and aim to answer a common question with different approaches. This allows the group to assess the strengths and weaknesses of the different models, and aid the decision making process.

Established in 2000, CISNET is a consortium of NCI-sponsored investigators who use modeling and simulation to improve their understanding of cancer control interventions in prevention, screening, and treatment and their effects on population trends in incidence and mortality. CISNET is committed to bringing the most sophisticated evidence-based planning tools to population health and public policy. These models have been used to guide public health research and priorities, and have aided the development of optimal cancer control strategies. Besides lung cancer, CISNET also includes breast, colon, cervical esophageal and prostate cancer groups. 

Alternatives Research & Development Foundation to Support Research on COVID-19, Aiming for Advancement in Non-animal Methods of Drug Discovery

By | News, Research

Pharmaceutical companies across the globe are racing to introduce clinically tested and approved therapeutic drugs that fight COVID-19 virus to market. As is typical in drug discovery research, animals have played a critical role in the development and testing of COVID-19 therapeutics. A proposal by U-M Professor Rudy J. Richardson, Dow Professor Emeritus of Toxicology, Professor Emeritus of Environmental Health Sciences, and Associate Professor Emeritus of Neurology at the University of Michigan, titled “Discovering host factor inhibitors in silico for SARS-CoV-2 entry and replication” has been awarded funding to identify compounds that bind to human proteins that facilitate entry and/or replication of the SARS-CoV-2 virus. Awarded, in part, because of its potential to develop alternative methods to advance science and replace or reduce animal use, this research will employ in silico ligand protein docking to discover existing drugs (repurposing) and/or new drug candidates capable of inhibiting host proteins involved in infection pathways for the COVID-19 virus, SARS-CoV-2.

Protein docking targets include four serine hydrolases. Using these targets, researchers will reversibly dock approximately 40,000 ligands from the Binding Database comprising FDA-approved drugs along with serine protease and PLA2 inhibitors, including organoboron compounds. Then, covalent docking will be conducted on a ligand subset containing pharmacophores capable of covalently binding serine hydrolases. Consensus ranking from four docking programs will be used to generate a penultimate list of candidate compounds. Those showing high predicted potency against off-target serine hydrolases will be excluded. The final list of compounds will be made publicly available for further evaluation in bioassays.

Professor Richardson’s grant, awarded by the Alternatives Research & Development Foundation, is a part of the ARDF’s 2020 Open Grants program, funding research projects that develop alternate methods to advance science and replace or reduce animal use. Although the immediate goal of this computational study supports the identification or development of a COVID-19 vaccine, the long-range vision is to advance computational and in vitro approaches to eliminate animal use from drug discovery for humans and other species. 

MICDE Affiliated Faculty member Rudy J. Richardson is a Dow Professor Emeritus of Toxicology and Professor Emeritus of Environmental Health Sciences within the School of Public Health, and Associate Professor Emeritus of Neurology within the Medical School at the University of Michigan.

MICDE funds wide-ranging computational discovery in galactic formation, drug discovery, bacterial biofilm colonies and turbulence simulations

By | News, Research

Since 2017 the Michigan Institute for Computational Discovery & Engineering (MICDE) Catalyst Grants program has funded a wide spectrum of cutting-edge research that combines science, engineering, mathematics and computer science. This year the program will fund four new projects that continue this tradition: Prof. Aaron Frank (Chemistry) and his group will spearhead efficient strategies to rapidly develop treatments for emerging diseases– a need made more compelling by the current COVID-19 Pandemic. Their approach combines generative artificial intelligence models and molecular docking to rapidly explore the space of chemical structures and generate target-specific virtual libraries for drug discovery. Prof. Marisa Eisenberg (Epidemiology, Mathematics, and Complex Systems) and Prof. Alexander Rickard’s (Epidemiology) groups will develop novel computational techniques to study biofilm architectures.  Biofilms are complex assemblages of microbial cells that form on almost any natural and man-made surface. They cause several debilitating diseases, and can even damage machinery and equipment, elevating the understanding of their behaviour to a critical need. Prof. Oleg Gnedin (Astronomy) will develop novel techniques to tailor the mathematical initial conditions from which to simulate chosen regions of the universe. The resulting insights will help uncover the origins of our own galaxy, the Milky Way. Finally, Prof. Aaron Towne (Mechanical Engineering) will advance the modeling of complex, turbulent flows and other large-scale systems in engineering science. His research will enable orders of magnitude of acceleration in the computation of extremely large scale flows in a number of engineering systems.

“These four projects have the potential to catalyze and  reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science”, said Krishna Garikipati, Professor of Mechanical Engineering and of Mathematics, and MICDE’s Director. “MICDE’s mission is to lead the advances in computational science research by bringing together interdisciplinary teams at U of M, and these projects embody that vision.” 

More about MICDE’s catalyst grant program and the projects can be found at micde.umich.edu/catalyst.

Microsoft AI for Health Program to support an AI-facilitated Optimization Framework for Improving COVID-19 Testing

By | News, Research

With the recent resurgence of COVID-19 infections, testing has become central to an integrated, global response to the pandemic. Accurate, effective, and efficient testing can lead to early detection and prompt an agile response by public health authorities. Strategic testing systems are critical for providing data that will inform disease prevention, preparation, and intervention. MICDE Associate Director and Associate Professor of Industrial and Operations Engineering and of Civil and Environmental Engineering, Siqian Shen, has recently published an article pin-pointing a number of pivotal operations research and industrial engineering tools that can be brought to  the fight against COVID-19. One of the key lessons from her research is the importance of expanding the availability of COVID-19 testing and making the resulting data transparent to the public as anonymized, summary statistics. This enables informed decision making by individuals, public health officials, and governments.  

Based on these high-impact findings, Professor Shen is striding ahead to design a comprehensive COVID-19 testing framework to efficiently serve the urgent needs of diverse population groups . A grant from Microsoft’s AI for Health program, part of the AI for Good initiative, will provide credits to use Microsoft’s Azure service.  With this cyber resource, Professor Shen and her team will integrate and coordinate decision-making models and data analytics tools that they have developed for testing on a Cloud-based platform. In addition, their AI framework is dynamic, and collects daily infection data to improve testing-related decisions. Such a platform could have significant impacts on three major problems that exist with current testing design strategies:

1) Where to locate testing facilities and how to allocate test kits and other resources.
2) How to effectively triage different population groups through effective appointment scheduling.
3) How to visualize real-time testing capacities to better inform the public and serve ad-hoc needs of patients. 

Prof. Shen’s research will integrate AI techniques with optimization to dynamically refine existing testing design methods for gathering and analyzing data from unexplored populations and regions around the globe. The development and refinement of these new models with the support of Microsoft Azure will create a transparent, data-informed testing system that will allow public health and government authorities to make agile, data-driven decisions to aid in the prevention, preparation, intervention, and management of COVID-19 and other outbreaks of infectious diseases.

Siqian Shen is a  Professor of Industrial and Operations Engineering, and of Civil and Environmental Engineering at the University of Michigan, an Associate Director of the Michigan Institute for Computational Discovery & Engineering, and an affiliated faculty member in the Michigan Institute for Data Science. Her research group works on both theoretical and applied aspects of problems by combining stochastic programming, integer programming, network optimization,  machine learning and statistics.

What is the right model? Different MRIO models yield very different carbon footprints estimates in China

By | Research

Appropriate accounting of greenhouse gas emissions is the first step to assign mitigation responsibilities and develop effective mitigation strategies. Consistent methods are required to fairly assess a region’s impact on climate change. Two leading reasons for the existence of different accounting systems are the political pressures, and the actual costs of climate mitigation to local governments. At the international level there has been consensus, and global environmentally extended multi-regional input-output (EE-MRIO) models that capture the interdependence of and their environmental impacts have been constructed.  However in China, the largest greenhouse gas emitter, where accurate interregional trade-related emission accounts are critical to develop mitigation strategies and monitor progresses at the regional level, this information is sporadic and inconsistent. Prof. Ming Xu from the School of Environment and Sustainability, and his research group, analyzed the available data from China, which dates back to 2012. They showed that the results varied wildly depending on the MRIO model used. For example, they found two MRIO models differed as much as 208 metric tons in a single region, which is equivalent to the emissions of Argentina, United Arab Emirates, or the Netherlands. Their results show the need to prioritize future efforts to harmonize greenhouse gas emissions accounting within China.

Ming Xu is an Associate Professor in the School for Environment and Sustainability and in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. His research focuses on the broad fields of sustainable engineering and industrial ecology. 

Read the full article.

Modeling the transmission of infectious aerosols

By | Feature, Research

Inhalation of micron-sized droplets represents the dominant transmission mechanism for influenza and rhinovirus, and recent research shows that it is likely also the case for the novel coronavirus.  Increasing evidence suggests that the transmission of infectious aerosols is more complex than previously thought. Coughing, sneezing and even talking yield a gaseous flow field near the infected person that is dynamic and turbulent in nature. Existing models commonly employed in simulations of aerosol transmission attempt to represent the effect of turbulence using random walk models that are often phenomenological in nature, employing adjustable parameters and inherently assuming the turbulent fluctuations ‘felt’ by a droplet do not depend upon direction. To design physics-informed guidelines to minimize the spread of this virus, improved predictive modeling capabilities for effectively tracking the aerosol paths are needed. Dr. Aaron M. Lattanzi and Prof. Jesse Capecelatro, from Mechanical Engineering and MICDE are tackling this problem by focusing on mathematical modeling of aerosol dispersion. They derived analytical solutions for the mean-squared-displacement resulting from systems of stochastic differential equations. A key element of their methodology is that the solution connects stochastic theory inputs to statistics present in high-fidelity simulations or experiments, providing a framework for developing improved models.

Simple simulation of aerosol dispersion from a single-point source. The grey, cone-like surface is the approximation using Force Langevin (FL) theory and the colored particles are from integration of Newton’s equations with stochastic drag forces.

Prof. Capecelatro’s research group develops physics-based models and numerical algorithms to leverage supercomputers for prediction and optimization of the complex flows relevant to energy and the environment. The main focus of their research involves developing robust and scalable numerical tools to investigate the multiphysics and multiscale phenomena under various flow conditions, like those that they study here. They recently submitted their findings to the Journal of Fluid Mechanics, and are continuing to work on this problem hoping it will help understand the transmission of COVID-19 and therefore help optimize current guidelines.

U-M draws global attention for MOOC: Problem Solving using Computational Thinking

By | Educational, Feature, Research

Problem Solving using Computational Thinking, a Massive Open Online Course (MOOC) launched by the University of Michigan in November of 2019, has already drawn more than 1,200 learners from around the globe. The Michigan Institute for Computational Discovery & Engineering (MICDE) and the University of Michigan Center for Academic Innovation partnered to create this course. The idea for this MOOC arose from the team’s recognition of the ubiquity of computation. However, the developers were equally keen to distinguish this offering from MOOCs on programming and to instead highlight how broader computational thinking also makes its presence felt in somewhat unexpected domains.

Using computational thinking, the MOOC challenges learners with a series of real-world examples, including how it is possible to help plan and prepare for a flu season–a subject that has gained particular relevance in the months following the launch of this MOOC, track human rights violations or monitor the safety of crowds.

While enrollment numbers are encouraging, the work being done by learners within the MOOC is most inspiring. For their final project, learners have applied the computational thinking strategies discussed throughout the MOOC to a wide array of noble social problems in hopes of finding cogent solutions.

Not surprisingly, there have been several projects that seek to address challenges related to COVID 19.

The MOOC’s Epidemiology Case Study walks the student through the process of building a communicable disease transmission model.

One learner wrote: “For the final project, I am assuming the role of a member of the team responsible to combat COVID-19 from India and I have to decide on what should be our strategy to fight coronavirus in India, be it the extension of a lockdown or any other important decision related to this pandemic.”

In another project, a learner assuming the role of a Wuhan pathologist wrote that they must “decide what the Chinese government’s strategy against coronaviruses” should be.

Learners addressing today’s most pressing societal concerns, such as COVID-19, exemplifies the transformative potential of open-access, digital, and distance education made possible by a MOOC.

Across the board, the MOOC has received tremendously positive reviews, with an overall course rating of 5 out of 5 stars. One learner, in particular, wrote in their course review: “I really enjoyed this course! It got me prepared to study for an entry into a career working with computers!!” Another learner simply stated: “Fantastic, loved it!”

The developers of this MOOC are drawn from the School of Public Health, the College of Engineering, the School of Education and MICDE. Problem Solving using Computational Thinking is available in Coursera through Michigan Online. To learn more please visit online.umich.edu/courses/problem-solving-using-computational-thinking/.

U-M modeling epidemiologists helping navigate the COVID-19 pandemic

By | Feature, News, Research

[top] Screenshoot of the Michigan COVID-19 Modeling Dashboard (epimath.github.io/covid-19-modeling/); [bottom left] Marisa Eisenberg (Epidemiology, Complex Systems and Mathematics); [bottom right] Jonathan Zelner (Epidemiology).

The COVID-19 pandemic is producing massive amounts of information that more often than not lead to different interpretations. The accurate analysis of this daily input of data is crucial to predict possible outcomes and design solutions rapidly. These can only be achieved with expertise in modeling infectious diseases, and with the power of computational science theory and infrastructure. U-M’s Epidemiology Department, in the School of Public Health, has a very strong cohort of researchers who work on mathematically modeling the dynamics of infectious diseases, the analysis of these models, and large scale computer simulations — all to understand the spread and mitigation of pandemics. They are applying their long experience and expertise to the current COVID-19 outbreak, aiding the government make informed decisions, and helping media outlets produce accurate reports for the general public. Marisa Eisenberg, Associate Professor of Epidemiology, of Complex Systems, and of Mathematics, and her colleagues are using a differential equation transmission modeling approach to analyze scenarios and generate short-term forecasts for the COVID-19 epidemic in State of Michigan. They are communicating directly with the Michigan Department of Health and Human Services and providing critical tools, like the Michigan COVID-19 Modeling Dashboard, to inform decision-making. Prof. Eisenberg’s team is helping to forecast the numbers of laboratory-confirmed cases, fatalities, hospitalized patients, and hospital capacity issues (such as ICU beds needed), and examining how social distancing can impact the spread of the epidemic. Prof. Jonathan Zelner, whose research is focused on using spatial and social network analysis and dynamic modeling to prevent infectious diseases, is part of a group helping map the outbreak in Michigan. He also has provided valuable insights to journalists contributing to a better understanding of the situation, including what made New York City so vulnerable to the coronavirus (NYT), the role of wealth inequality during epidemics (CNBC) and what professions and communities are particularly vulnerable to infection (NG). 

Professors Eisenberg and Zelner are not alone in this fight. Many more researchers from U-M’s School of Public Health and throughout campus have risen to the challenges posed by this pandemic. 

Combat COVID-19 using newly available HPC resources: COVID-19 High Performance Computing Consortium

By | Feature, HPC, News, Research

COVID-19 High Performance Computing Consortium

On March 23, 3030 the White House announced the launch of a new partnership that aims to unleash U.S. supercomputing resources to fight COVID-19: the COVID-19 High Performance Computing Consortium. The goal of the Consortium is to bring together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. The access to these resources has the potential to significantly advance the pace of scientific discovery in the fight to stop the virus.

To request access to resources of the COVID-19 HPC Consortium, you must prepare a description, no longer than two pages, of your proposed work. To ensure your request is directed to the appropriate resource(s), your description should include the following sections. Do not include any proprietary information in proposals, since your request will be reviewed by staff from a number of consortium sites. It is expected that teams who receive Consortium access will publish their results in the open scientific literature.

Learn more at https://covid19-hpc.mybluemix.net .

 

 

 

 

The NSF Computational Mechanics Vision Workshop

By | Events, Research

Over October 31 and November 1, 2019 MICDE hosted the 2019 Computational Mechanics Vision workshop that aimed to gather and synthesize future directions for computational mechanics research in the United States. Attended by more than 50 experts in various sub-disciplines of computational mechanics from across the country, including five National Science Foundation Program Directors, the group spent a day and a half brainstorming about the future of computational mechanics and defining new paradigms, methodologies and trends in this exciting and vast field. The workshop focused on four emerging areas in Computational Mechanics: Machine Learning, Additive Manufacturing, Computational Medicine, and Risk and Uncertainty Quantification. Operating through open discussions on talks by experts from within and beyond Computational Mechanics, and breakout sessions on the above four topics, the workshop participants arrived at a series of recommendations that could drive NSF’s investments in this field for the next decade and beyond.

To learn more about the event please visit micde.umich.edu/nsf-compmech-workshop-2019/.