Multi-scale Modeling and Viral Pandemics (3/17/2022)

Solly Sieberts, Sage Bionetworks. Crowdsourcing and benchmarking to understand viral susceptibility.

Julia Arciero, Indiana University. Predicting experimental sepsis survival with a mathematical model of acute inflammation.
Institution/ Affiliation
Solly Sieberts, Sage Bionetworks.

Julia Arciero, Indiana University.
Presentation Details (date, conference, etc.)

March 17, 2022, IMAG/MSM WG on Multiscale Modeling and Viral Pandemics


  1. Solly Sieberts, Sage Bionetworks. Crowdsourcing and benchmarking to understand viral susceptibility: 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. YouTube and Slides.
  2. Julia Arciero, Indiana University. Predicting experimental sepsis survival with a mathematical model of acute inflammation: 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. YouTube and Slides.