Multiscale Modeling and Viral Pandemics

MULTISCALE MODELING AND VIRAL PANDEMICS

Co-Leads:

James A. Glazier, PhD, Indiana University, JAGlazier@gmail.com

Reinhard Laubenbacher, PhD, University of Florida, Reinhard.Laubenbacher@medicine.ufl.edu

Rationale: The ongoing COVID-19 pandemic has provided a striking example of the real-world importance of mathematical and computational modeling. Epidemiological simulations have become key technologies for optimizing responses by clinicians and policy makers around the world. Because epidemiological models had already been developed and validated for other infectious diseases before the appearance of SARS-CoV-2, these models were available for rapid repurposing when the COVID-19 crisis started. As a result, sophisticated epidemiological models of COVID-19 are informing healthcare professionals and public leaders as they decide on social restrictions or resource allocation.

However, once a patient is infected with SARS-CoV-2 (or another virus), current modeling technology is not sufficiently advanced to be of much help in assessing risk or guiding treatment. The spread of infection within the body and the immune response to respiratory and other viral pathogens is still poorly understood. The factors determining the beneficial (viral clearance) and harmful (cytokine storm) effects of COVID-19 are poorly understood. COVID-19 is also a very complex disease, with pathologies developing in organs beyond the sites of primary infection, and thus requiring understanding of the responses of multiple organ systems (especially the vasculature and blood) and their interactions. As the disease progresses over the course of weeks and months, coinfection between respiratory viruses is likely and its significance poorly understood. Therapies under consideration are also complex, with possible combination therapies combining phased dosages of novel and existing antivirals, pro-and anti-inflammatory drugs and antibodies. Such therapies will need to be personalized, and their combinatorial complexity makes evaluation with clinical studies which do not employ modeling for prioritization prohibitively time consuming and costly. The complex pattern of comorbidities to COVID-19 is suggestive but their significance is still unclear. Minority populations are at much greater risk both of being infected with SARS-CoV-2 and of dying from COVID-19, once infected. It is an urgent matter of health equity to understand and remediate these vulnerabilities. While the former (susceptibility) are primarily the subject of epidemiological models, the latter (death rates) are appropriate topics for the models the Working Group will consider. The COVID-19 epidemic has also brought modeling of infections into popular consciousness, education at all levels and policy making to an unprecedented degree. The development of models of human infection and response provide great opportunities for education and dissemination.

We currently lack platform tools for the evaluation of viral infections, their responses, and treatment opportunities like the epidemiology models that were available to customize for COVID-19. With partial exceptions for influenza and HIV/AIDS, few computational models attempt to collectively understand and harness the key drivers of infection progression for prognosis and optimal design of interventions. Even the most sophisticated current models do not usually include the details of immune response and inter-organ interactions that seem critical to COVID-19 and will be essential if the models are to serve as a platform for response to future viruses. Another obstacle to using modeling as a guide to treatment is the relative lack of ability to personalize such models using readily available data, such as lung CT scans, immune profiles from repeated blood draws, or comorbidities and other information from patient health records. This personalization of predefined and calibrated models is key, as COVID-19 has shown with its unpredictability of patient response. Added challenges are a better understanding of host-pathogen interactions, and the connection between the population and patient-level scales. These and other challenges make clear that the determination of an effective response to viral pandemics is a multiscale many-faceted problem whose solution has to rely on multiscale mathematical and computational models. The IMAG community is ideally positioned to lead an initiative to develop and help execute a strategy for developing and applying this technology.

Focus and structure of the working group: The community of modelers developing epidemiological and population-scale models is already extensive and well-integrated, in part due to the NIGMS MIDAS program. Within-host modeling of viral pathogens is much more limited. Therefore, the working group will initially focus on within-host scales, in particular the complex interactions between viral infection, host physiology, and the immune system. A main long-term deliverable of the working group will be an overall strategy for a coordinated multi-scale modeling effort which becomes a customizable translational technological platform for rapidly creating improved personalized prognoses and therapies in response to emerging viral pandemics. It will also include a plan on how to mobilize and coordinate the modeling community to support this effort.

The working group will be organized as follows:

A steering group, consisting of approximately 20 scientists, including modelers, data scientists, experimental and clinical domain experts, such as immunologists and virologists and representatives of affected communities and potential tool users, with special emphasis on the effects of the disease on disproportionately affected populations.

A collection of subgroups, broadly covering the following topics:

  • innate immune response
  • adaptive immune response
  • host-pathogen interactions
  • drug development
  • vaccine development
  • individual organ systems, in particular the lungs, given the importance of respiratory viral infections
  • vascular response
  • transport models in the lungs, lymph and blood
  • integration of scales
  • personalization of models, data requirements
  • integration of the within-host and population scales
  • modeling technologies, requirements for model credibility, reproducibility, model integration challenges
  • optimization of model design and delivery for target users, including policy makers and clinicians
  • training and outreach for both scientific and general communities

The steering group and subgroups will contain scientists from academia, the private sector, and government, also relying heavily on the members of the IMAG working groups. The working group will be widely advertised, and membership is open to all scientists. In addition, the co-leads will proactively invite scientists to join the steering group.

Initial Member List

 

First Name

Last Name

Institution

Department

  1.  

Frederick

Adler

University of Utah

Mathematics and Biology

  1.  

Richard

Alo

Florida A&M University

College of Science and Technology

  1.  

Gary

An

University of Vermont

Surgery

  1.  

Josua

Aponte-Serrano

Indiana University

Intelligent Systems Engineering

  1.  

John

Bachman

Harvard Medical School

Laboratory of Systems Pharmacology

  1.  

Jacob

Barhak

Jacob Barhak

 

  1.  

Joshua

Behr

Virginia Modeling, Analysis and Simulation Center at Old domnion University

VMASC

  1.  

Rahul

Bhadani

The University of Arizona

Electrical Engineering

  1.  

Ramray

Bhat

Indian Institute of Science

Division of Biological Sciences

  1.  

Ruth

Bowness

University of Bath

Department of Mathematical Sciences

  1.  

Tomer

Brandes

Tel aviv university

Mathematics

  1.  

Markus

Buehler

MIT

Laboratory for Atomistic and Molecular Mechanics

  1.  

Filippo

Castiglione

National Research Council of Italy

Institute for Applied Computing

  1.  

Filippo

Castiglione

National Research Council of Italy

Institute for Applied Computing

  1.  

Filippo

Castiglione

National Research Council of Italy

Institute for Applied Computing

  1.  

Tom

Chou

UCLA

Biomathematics

  1.  

Morgan

Craig

Université de Montréal/CHU Sainte-Justine Research Centre

 

  1.  

Željko

Čupić

University of Belgrade, Institute of Chemistry, Technology and Metallurgy

Department for Catalysis and Chemical Engineering

  1.  

Chantal

Darquenne

University of California, San Diego

Medicine

  1.  

Rita

de Almeida

Universidade Federal do Rio Grande do Sul

Physics

  1.  

Yuefan

Deng

Stony Brook University

Applied Mathematics

  1.  

Mamadou

Diallo

National Science Foundation

Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET)

  1.  

Steffen

Docken

UNSW

The Kirby Institute

  1.  

Graham

Donovan

University of Auckland

Mathematics

  1.  

Brian

Drawert

UNCA

Computer Science

  1.  

Zhanwei

Du

University of Texas at Austin

Department of Integrative Biology

  1.  

Bard

Ermentrout

University of Pittsburgh

Math

  1.  

James

Faeder

University of Pittsburgh

Computational and Systems Biology

  1.  

Juliano

Ferrari Gianlupi

IU

ISE

  1.  

Ashlee

Ford Versypt

Oklahoma State University

School of Chemical Engineering

  1.  

M. Gregory

Forest

University of North Carolina at Chapel Hill

Mathematics

  1.  

Eric

Forgoston

Montclair State University

Applied Mathematics and Statistics

  1.  

Slim

Fourati

Emory

Pathology

  1.  

Uduak

George

San Diego State University

Mathematics

  1.  

Olivier

Gevaert

Stanford University

Medicine

  1.  

James

Glazier

Indiana University

Intelligent Systems Engineering

  1.  

Gilles

Gnacadja

Amgen

 

  1.  

Gilberto

Gonzalez-Parra

New Mexico Tech

Mathematics

  1.  

Abba

Gumel

Arizona State University

 

  1.  

Benjamin

Gyori

Harvard Medical School

Laboratory of Systems Pharmacology

  1.  

Amit

Hagar

Indiana University Bloomington

ISE

  1.  

Leonard

Harris

University of Arkansas

Biomedical Engineering

  1.  

William

Hart

University of Oxford

Mathematical Institute

  1.  

Jotun

Hein

University of Oxford

Statistics

  1.  

ESTEBAN

HERNANDEZ VARGAS

UNAM

 

  1.  

Alexander

Hoffmann

UCLA

Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics

  1.  

Tony

Humphries

McGill University

Mathematics & Statistics, and, Physiology

  1.  

Mac

Hyman

Tulane University

Mathematics

  1.  

Srividya

Iyer-Biswas

Purdue

Physics

  1.  

Yi

Jiang

Georgia State University

Mathematics and Statistics

  1.  

Michael

Johansson

Centers for Disease Control and Prevention

 

  1.  

Daniel

Jonas

Colorado State University

Mathematics

  1.  

M. Humayun

Kabir

Jahangirnagar University

Mathematics

  1.  

Roger

Kamm

MIT

Biological Engineering

  1.  

Chris

Kang

Washington State University

Department of Mathematics and Statistics

  1.  

George

Karniadakis

Brown University

Division of Applied Mathematics

  1.  

Yannis

Kevrekidis

Johns Hopkins University

Chemical and Biomolecular Engineering

  1.  

Eunyoung

Kim

Northwestern University

Medicine

  1.  

Kristian

Kiradjiev

University of Oxford

Mathematical Institute

  1.  

Isaac

Klapper

Temple University

Mathematics

  1.  

James

Klaunig

Indiana university

Environmental health

  1.  

Ellen

Kuhl

Stanford University

Mechanical Engineering

  1.  

Bruce

Lee

City University of New York (CUNY)

 

  1.  

Chen

Liao

Memorial Sloan-Kettering Cancer Center

 

  1.  

Guang

Lin

Purdue University

Department of Mathematics

  1.  

Yaling

Liu

Lehigh University

 

  1.  

Carlos

Lopez

Vanderbilt University

Biochemistry

  1.  

Cecil

Lynch

Accenture

Analytics

  1.  

Miranda

Lynch

Hauptman-Woodward Medical Research Institute

 

  1.  

Paul

Macklin

Indiana University

Intelligent Systems Engineering

  1.  

Bobby

Madamanchi

University of Michigan

School of Information

  1.  

Balázs

Madas

Centre for Energy Research

Environmental Physics Department

  1.  

Chitaranjan

Mahapatra

University of California San Francisco

Cardio Vascular Research Institute

  1.  

Rachael

Mansbach

Concordia University

Physics

  1.  

Tarunendu

Mapder

Indiana University School of Medicine

Medicine

  1.  

Elebeoba

May

University of Houston

 

  1.  

Christian

Mazza

University of Fribourg

Mathematics

  1.  

Ericka

Mochan

Carlow University

Applied, Physical and Social Sciences

  1.  

Saurabh

Mogre

University of California San Diego

Physics

  1.  

Jonathan

Monk

UC San Diego

Bioengineering

  1.  

James

Moore

Imperial College London

Department of Bioengineering

  1.  

Anthony

Morciglio

GSU

Mathematics

  1.  

Aristides

Moustakas

University of Crete

Natural History Museum of Crete

  1.  

Syed Muhammad Raza

Naqvi

Superior University

CS & IT

  1.  

Rossana

Occhipinti

Case Western Reserve University

Physiology & Biophysics

  1.  

Katherine

Ogurtsova

German Diabetes Center

Institute for Health Services Research and Health Economics

  1.  

Damilola

Olabode

Washington State University

Mathematics and statistics

  1.  

James

Osborne

University of Melbourne

School of Mathematics and Statistics

  1.  

Nieko

Punt

University Medical Center Groningen

Pharmacy

  1.  

Zhuolin

Qu

University of Texas at San Antonio

Mathematics

  1.  

Miriam

Rafailovich

Stony Brook University

materials science and engineering

  1.  

Padmini

Rangamani

UCSD

Mechanical and aerospace engineering

  1.  

John

Rice

Retired DOD

Navy

  1.  

Rodrigo

Santibáñez

Universidad Mayor

Center for Genomics and Bioinformatics

  1.  

gusztav

schay

Semmelweis University

Biophysics and Radiation Biology

  1.  

Santiago

Schnell

University of Michigan Medical School

Department of Molecular & Integrative Physiology

  1.  

Elissa

Schwartz

Washington State University

 

  1.  

Ira

Schwartz

US Naval Research laboratory

Code 6792, Plasma Physics Division

  1.  

T.J.

Sego

Indiana University Bloomington

Intelligent Systems Engineering

  1.  

Bruce

Shapiro

University of Florida

Department of Medicine

  1.  

Jason

Shoemaker

University of Pittsburgh

Chemical & Petroleum Engineering

  1.  

James

Sluka

Indiana University

Intelligent Systems Engineering

  1.  

Amber

Smith

University of Tennessee Health Science Center

Pediatrics

  1.  

Robert

Stratford

Indiana University School of Medicine

Medicine

  1.  

Tingting

Tang

San Diego State University

Department of Mathematics and Statistics

  1.  

Merryn

Tawhai

University of Auckland

Auckland Bioengineering Institute

  1.  

Juilee

Thakar

University of Rochester

Microbiology and Immunology

  1.  

Gilberto

Thomas

Universidade Federal do Rio Grande do Sul

Dep, de Física

  1.  

Robin

Thompson

University of Oxford

Mathematical Institute

  1.  

Marcella

Torres

University of Richmond

Mathematics and Computer Science

  1.  

Paula

Vasquez

University of South Carolina

Mathematics

  1.  

Jorge

Velasco-Hernandez

Universidad Nacional Autonoma de Mexico

Institute of Mathematics

  1.  

Srinivasan

Venkatramanan

University of Virginia

Biocomplexity Institute

  1.  

Lin

Wang

University of Cambridge

Department of Genetics

  1.  

Xujing

Wang

NIDDK/NIH

 

  1.  

Yafei

Wang

Indiana University Bloomington

Intelligent Systems Engineering

  1.  

Joanna

Wares

University of Richmond

Mathematics and Computer Science

  1.  

Dan

Yamin

Tel Aviv university

 

  1.  

Kaiming

Ye

Binghamton University

Biomedical Engineering

  1.  

Matan

Yechezkel

Tel Aviv University

Industrial Engineering

  1.  

Veronika

Zarnitsyna

Emory University

Microbiology and Immunology

  1.  

Anton

Zilman

University of Toronto

Physics and Institute for Biomedical Engineering

122 total, updated Oct 30, 2020 5:15PM

 

Participants
James A. Glazier
Reinhard Laubenbacher
James P. Sluka
Activities

We have created a Slack channel for this working group. If you would like access please contact us at mailto:viralpandemMSM@gmail.com or via the Twitter handle https://twitter.com/MsmViral.

Goals and Objectives

Initial list of deliverables and goals:

Six months:

  • Advertise for and recruit members as widely as possible.
  • Establishment of the steering group and subgroups with all needed areas of expertise, as well as an effective communication structure, including regular meetings and information sharing resources
  • A report that includes a map of the main processes to be modeled, available models, available data, and main laboratories around the world doing related research
  • List of most important models, data, techniques that are still missing
  • Plan of how to integrate existing as well as future models
  • Plan for approaches to validation and uncertainty quantification when model components are developed simultaneously and independently
  • Article about the initiative for a medical audience, published in NEJM, JAMA, or similar journal
  • Article about the initiative for a modeling audience, published in Nature Digital Medicine, or similar journal

One year:

  • Refinement of the six-month deliverables
  • Virtual one-day workshop on the report and next steps

Two years:

  • Comprehensive strategic plan for the development of a “digital twin” for therapeutics for an individual patient suffering from a viral infection
  • Plan for training of computational scientists who want to contribute to the initiative
  • Plan for use of models in training of STEM students and the general population
  • First steps toward implementation of this plan, including the identification of challenges and required resources

Five years:

  • Well-established organization of an international integrated research community in modeling of viral pandemics across scales
Additional Information

Publications:

From members of Multiscale Modeling and Viral Pandemics Working Group, prior to group formation:

  1. Sego TJ, Aponte-Serrano JO, Ferrari Gianlupi J, Heaps SR, Breithaupt K, Brusch L, Osborne JM, Quardokus EM, Plemper RK, Glazier JA. A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness, bioRxiv 2020.04.27.064139; doi: https://doi.org/10.1101/2020.04.27.064139.
  2. Getz M, Wang Y, An G, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder J, Ford Versypt AN, Ferrari Gianlupi J, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Aminul Islam M, Jenner A, Kurtoglu F, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Shoemaker JE, Smith AM, Macklin P. Rapid community-driven development of a SARS-CoV-2 tissue simulator, bioRxiv 2020.04.02.019075; doi: https://doi.org/10.1101/2020.04.02.019075.
  3. de Almeida RMC, Thomas GL, Glazier JA, Transcriptogram analysis reveals relationship between viral titer and gene sets responses during Corona-virus infection, bioRxiv 2020.06.16.155267; doi: https://doi.org/10.1101/2020.06.16.155267.
Working Group Activities

The initial teleconference was Thursday October 22, 2020 at 3PM US Eastern Time. The meeting had 72 attendees.  The recording of the first meeting via Zoom is available at:
https://drive.google.com/file/d/1GOHG0ZA0khngnp-DH8Z31-LaBkag4D42/view?usp=sharing