IMAG/MSM Working Group on Multiscale Modeling and Viral Pandemics Mini Seminars
March 17, 2022
Welcome - The meeting will start at 3PM ET
NOTE: THE MEETING WILL BE RECORDED, STREAMED AND PUBLICLY AVAILABLE�FOR THOSE MEMBERS UNABLE TO ATTEND
Agenda
People
Co-Lead: Reinhard Laubenbacher, PhD
Department of Medicine
Laboratory for Systems Medicine
University of Florida
reinhard.laubenbacher@medicine.ufl.edu
Co-Lead: James A. Glazier, PhD
Dept. of Intelligent Systems Engineering and Biocomplexity Institute
Indiana University, Bloomington
Web Administration, Slack: James P. Sluka, PhD
Dept. of Intelligent Systems Engineering and Biocomplexity Institute
Indiana University, Bloomington
Activities Coordination: Lorenzo Veschini, PhD
King’s College London
Slack Channel
https://Msm-working-group.slack.com
Our IMAG/MSM Wiki page
https://www.imagwiki.nibib.nih.gov/working-groups/multiscale-modeling-and-viral-pandemics Feel free to suggest additional content!
Or, use the Tiny URL: https://tinyurl.com/hkr97vfe
IMAG’s LinkedIn
YouTube “MSM Working Group on Multiscale Modeling” https://www.youtube.com/channel/UCuDFvhgFziRRDcpRnT3vlrw �see also the links on our seminar page at https://tinyurl.com/5fra7jjd
Please follow the�group on Twitter!
If you could re-tweet the weekly announcements �(there are usually two, one for each speaker) �that would help boost attendance and community awareness.
Announcements
�Any short (~1 minute) items such as;
Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2. Ferrari Gianlupi, J.; Mapder, T.; Sego, T.J.; Sluka, J.P.; Quinney, S.K.; Craig, M.; Stratford, R.E., Jr.; Glazier, J.A. Viruses 2022, 14, 605. https://doi.org/10.3390/v14030605
Schedule for Upcoming Meetings and mini-Seminars
March 24:
March 31:
April 7:
Request for future speakers (March 24, 31, …)
Rules of the Meeting
Please mute your microphone and hold questions until after the presentations
Mini-Seminar�Crowdsourcing and benchmarking to �understand viral susceptibility
Solly Siebert�Sage Bionetworks
Data sharing requirements by funders and publishers has unprecedented amounts of genomic and biomedical data available and democratized access by researchers. This, in turn, has likely been a contributing factor to the massive growth in publications over the last decades, despite relatively stable funding dollars. However, in this increasingly computation-heavy world, identification of optimal solutions and methods becomes difficult when researchers use different data and employ different approaches to evaluation, often choosing those that paint their methods in the best light. Crowd sourcing through challenges is one approach to solve important biomedical problems and identify optimal solutions using unbiased apples-to-apples evaluation. Here I describe the approach taken by DREAM Challenges. I highlight several examples, including insights from a 2016-2017 challenge to identify gene expression-based prediction of susceptibility to respiratory viral infection.
Mini-Seminar�Predicting experimental sepsis survival with a �mathematical model of acute inflammation
Julia Arciero�Indiana University
Sepsis is characterized by an overactive, dysregulated inflammatory response that drives organ dysfunction and often results in death. Here, a system of four ordinary differential equations (ODEs) was used to simulate the dynamics of bacteria, the pro- and anti-inflammatory responses, and tissue damage. The ODE model was calibrated to experimental data from E. coli infection in genetically identical rats and was validated with mortality data for these animals. The model demonstrated recovery, aseptic death, or septic death outcomes for a simulated infection while varying the initial inoculum, pathogen growth rate, strength of the local immune response, and activation of the pro-inflammatory response in the system. The model demonstrated that small changes in parameter values, such as those governing the pathogen or the immune response, could explain the experimentally observed variability in mortality rates among septic rats. A local sensitivity analysis was conducted to understand the magnitude of such parameter effects on system dynamics. Despite successful predictions of mortality, simulated trajectories of bacteria, inflammatory responses, and damage were closely clustered during the initial stages of infection, suggesting that uncertainty in initial conditions could lead to difficulty in predicting outcomes of sepsis by using inflammation biomarker levels.
Requests for Input/Suggestions
We would like the subgroup leads to prepare brief presentations for the Thursday meetings, please let us know when you would like to present
Ideas/help for publicising our Thursday mini-seminars more effectively and for speakers to invite
Suggestions for agenda items and approaches to organizing the Steering Committee Meetings more effectively
There have also been a number of requests for more explicit statements of goals and tasks from the WG leadership, we would appreciate your suggestions
Please contact Reinhard Laubenbacher, James Glazier, James Sluka or Bruce Shapiro with your ideas on all of these issues
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