Jason Shoemaker, University of Pittsburgh. Title: Computational modeling to reveal the molecular mechanisms of immunopathology in flu and COVID-19. Abstract: Computational modeling and controls engineering-based analytics offer the opportunity to drive drug development and treatment optimization for severe respiratory infection. Respiratory infection is a top 10 cause of death in the US in a “normal” year, and COVID-19 has propelled respiratory infection to the 3rd leading cause of death in the US. In the past decade, mounting evidence has suggested that deadly respiratory virus infections, such infections with H5N1 virus, the 1918 Spanish Flu, the 2009 pandemic H1N1 virus and SARS-Cov-2, are associated with distinct immune system dynamics. Complementing this, immunomodulation studies have demonstrated that modifying the immune system can improve tissue pathology and disease outcome. Since June of 2020, immunomodulatory treatment via select corticosteroids have become the primary approach to treating severe COVID-19 infection. However, the immune response is a highly complicated, self-regulating system. Systems engineering approaches are well suited to modeling immune complexity and can provide in silico tools for drug target candidate prioritization and immunomodulatory treatment optimization. Here, I will discuss our work on computational modeling of immunodynamics during influenza infection and COVID-19. Ordinary differential equations (ODE) and agent-based models (ABMs) will be introduced, and we will discuss how these models have been employed to show that: -- For interferon production, paracrine signaling may be more significant than interferon induction mediated directly by the virus for causing aggressive immune responses. -- Some properties of immune signaling systems, such as cellular heterogeneity and stochastic responses, allow the immune system to minimize the number of responsive cells while still maintaining a robust immune response. -- And H1N1 and H5N1 viruses seem to differently induce interferon production. Time permitting, we will discuss network-based modeling approaches, including using global network controllability, for predicting host proteins (i.e. factors) that are essential for influenza virus replication. Using an siRNA screen to validate network predictions, we demonstrate that data-driven subnetwork construction is a successful approach for predicting novel drug target candidates. This approach has recently been applied to COVID-19 data, suggesting new possible molecules for therapeutic consideration. YouTube and Slides.

Tiam Heydari, PhD candidate at Zandstra lab, School of Biomedical Engineering, University of British Columbia, Title: IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNAseq data. Abstract: The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to a scRNA-seq dataset from early mouse T-cell development and show that it can infer a priori over 75% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of the known gene perturbations on the T-cell developmental trajectory. Finally, we implement a IQCELL gene selection pipeline that allows us to select, without prior knowledge, candidate genes and demonstrate that GRN simulations based on this set yield results similar to the original curated list. In summary the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems. YouTube and Slides.