Multiscale Modeling and Viral Pandemics

Multiscale Modeling and Viral Pandemics

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To signup for subgroups please complete this form

For the November 18 at 3-4:30 PM Eastern time teleconference, please join at
https://iu.zoom.us/meeting/register/tZYqd-2srD8tGtCXDem4Cka08rBz5fDW0EQR

Presentations:

  1. Christian Forst, Icahn School of Medicine at Mount Sinai. Title: The interplay between the human microbiome and respiratory viruses: A multi scale story of influenza and COVID-19. Abstract: The ongoing SARS-CoV-2 pandemic poses a threat to public health and economy, thus urges the scientific community to join efforts in the search of cures. Meanwhile, both influenza and COVID-19 are respiratory diseases caused by airborne RNA viruses. Microbes in the respiratory system have been proven to contribute to the outcome of the diseases. However, scientific advances from studying influenza infection have potentials to benefit the search of cure for SARS-CoV-2 infections. Here we present a comprehensive, multi-scale network analysis of the systems response to the virus. We have developed methods that integrate single-cell and bulk transcriptomic data. These integrated data were further related to the microbiome and clinical outcomes. By this approach we were able to identify cell-population specific key-regulators and host-processes that are hijacked by the virus for its advantage and that contribute to the severity of these infectious diseases.
  2. Elebeoba E. May, University of Houston. Title: Model-based Investigation of the Proinflammatory Microenvironment and Response to Gram-negative Bacteria. Abstract:  Intracellular pathogens like Francisella tularensis (Ft), a gram-negative Class A biothreat agent can trigger the release of cytokines, chemokines, and effector molecules into the microenvironment surrounding the infected cell, contributing to the formation of a proinflammatory microenvironment (PME). Immune cells recruited into the PME can be primed and activated by cytokine exposure promoting a more robust interaction between infiltrating immune cells and infected cells or, in the case of phagocytic cells, priming the cell to more effectively eliminate subsequent Ft infection. Macrophages and NK cells are central to the innate immune response to Ft and primary producers of TNF-α and IFN-γ, respective.  Focusing on these key PME cytokines, which are found to modulate the in vivo response to Ft, we developed in silico and in vitro models to investigate the role of PME in macrophage activation and outcome of infection.

After the presentations, we will have breakout rooms for each of the speakers
where you may discuss technical questions.

Upcoming Seminars:

      November 25: 3 PM (Eastern)

      1. Thanksgiving Break, no seminar.

      December 2: 3 PM (Eastern)

      1. Clare Bryant, University of Cambridge.
      2. Reinhard Laubenbacher, Title: New focus, New subgroup

      December 9: 3 PM (Eastern)

      1. Slim Fourati, Emory University School of Medicine.
      2. Bingyang Wei, Texas Christian University. Draft Title: Requirements engineering, i.e. how to develop software specifications that result in a product that the users want.

      For details of the past and future teleconferences, including YouTube videos and slides,
      please visit this page.

      IMAG's COVID-19 modeling and data resources page is here.

      Three new VP Wiki pages: Publications, Random but Relevant Things and Viral Pandemics WG Runnable Models Page

      Recent Publications from group members:

      1. Waites W, Cavaliere M, Danos V, Datta R, Eggo RM, Hallett TB, Manheim D,  Panovska-Griffiths J, Russell TW, Zarnitsyna VI. "Compositional modelling of immune response and virus transmission dynamics." https://arxiv.org/abs/2111.02510.
      2. Sego TJ, Mochan ED, Ermentrout GB, Glazier JA. "A multiscale multicellular spatiotemporal model of local influenza infection and immune response." J Theor Biol. 2021 Sep 27;532:110918. doi: 10.1016/j.jtbi.2021.110918. Epub ahead of print. PMID: 34592264; PMCID: PMC8478073.
      3. Sego TJ, Aponte-Serrano JO, Gianlupi JF, Glazier JA. "Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection". BMC Biol. 2021 Sep 8;19(1):196. doi: 10.1186/s12915-021-01115-z. PMID: 34496857.
      4. Veronika I Zarnitsyna, Juliano Ferrari Gianlupi, Amit Hagar, TJ Sego, James A Glazier. "Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response." Current Opinion in Virology, 50, October 2021, Pages 103-109.
      5. Masison, J., J. Beezley, Y. Mei, H. a. L. Ribeiro, A. C. Knapp, L. Sordo Vieira, B. Adhikari,  Y. Scindia, M. Grauer, B. Helba,W. Schroeder, B. Mehrad,  R. Laubenbacher. “A Modular Computational Framework for Medical Digital Twins.” Proceedings of the National Academy of Sciences 118, no. 20 (May 18, 2021). https://doi.org/10.1073/pnas.2024287118.
      6. Sarah M Bartsch, Patrick T Wedlock, Kelly J O’Shea, Sarah N Cox, Ulrich Strych, Jennifer B Nuzzo, Marie C Ferguson, Maria Elena Bottazzi, Sheryl S Siegmund, Peter J Hotez, Bruce Y Lee, Lives and Costs Saved by Expanding and Expediting Coronavirus Disease 2019 Vaccination, The Journal of Infectious Diseases, Volume 224, Issue 6, 15 September 2021, Pages 938–948, https://academic.oup.com/jid/article/224/6/938/6267841 .
      7. Laubenbacher R, Sluka JP, Glazier JA. Using digital twins in viral infection. Science. 2021 Mar 12;371(6534):1105-1106. doi: 10.1126/science.abf3370.
      8. Getz M, Wang Y, An G, Asthana M, Becker A, Cockrell C, Collier N, Craig M, Davis CL, Faeder JR, Versypt ANF, Mapder T, Gianlupi JF, Glazier JA, Hamis S, Heiland R, Hillen T, Hou D, Islam MA, Jenner AL, Kurtoglu F, Larkin CI, Liu B, Macfarlane F, Maygrundter P, Morel PA, Narayanan A, Ozik J, Pienaar E, Rangamani P, Saglam AS, Shoemaker JE, Smith AM, Weaver JJA, Macklin P. Iterative community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv [Preprint]. 2021 Apr 29:2020.04.02.019075. doi: 10.1101/2020.04.02.019075. PMID: 32511322; PMCID: PMC7239052.

      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 immune response to 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.

      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.

      Member List is available here.

      Activities

      Subgroup pages

      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

      Media and Links:

      Slack Channel: msm-working-group.slack.com
      YouTube Channel: https://www.youtube.com/channel/UCuDFvhgFziRRDcpRnT3vlrw
      Twitter: https://twitter.com/MsmViral

      If you would like access to the above please contact us at mailto:viralpandemMSM@gmail.com or via the Twitter handle https://twitter.com/MsmViral.

      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

      Meetings and Teleconferences

      We are holding zoom meetings for the entire working group every Thursday afternoon at 3PM Eastern US time. The complete schedule of past and future meetings is here.

      Handy Links

      We have a page of handy links for activities related to the working group here.

      This page, our top level page for the Viral Pandemics WG has the tiny URL https://tinyurl.com/hkr97vfe .

      Our initial charge to the working groups is outlined below:

      Based on the proposal submitted to the IMAG/MSM Steering Committee, the objectives for the working group as a whole and each of the subgroups are as follows:

      Objectives for subgroups for the first 9 months:

      1. Develop a plan to achieve the objectives listed below. (Target delivery December 31, 2020) 

      2. Identify people working in this area and include a paragraph on their work. (Target delivery February 28, 2021)

        • Recruit members

        • Assemble a directory of researchers with bibliography

        • Identify other subgroups that you should coordinate with

      3. Prepare a white paper, approx. 5pp in length, excluding references that does the following: (Target delivery May 31, 2021)

        • Describe the focus of the subgroup, the major open problems to address, and the role modeling can play

        • Describe what models and data are available, and the extent of our biological knowledge, available experimental systems, etc.

        • Describe what is needed to address these problems that does not exist yet: models, data, experimental approaches, etc.

        • Outline any action items that could get us to solutions to these problems.
          These white papers can form the basis of a collective publication on the topic of multiscale modeling and viral pandemics.

      4. Catalyze research projects through presentations, exchange of ideas, search for strategic opportunities. (Target delivery August 30, 2021)

      Note: The list below only includes individuals that are members of both the working group and  members of IMAG/MSM. For a complete list of all members of the working group please see the Member List is here.