Cockrell, R. C. | Search bioRxiv

bioRxiv2019

The Cellular Immunity Agent-Based Model (CIABM): A computational model to examine the role of cellular immunity in viral infection

Viral respiratory infections, such as influenza, result in over 1 million deaths worldwide each year. To date, there are few therapeutic interventions able to affect the course of the disease once acquired, a deficit with stark consequences that were readily evident in the current COVID-19 pandemic. We present the Cellular Immune Agent Based Model (CIABM) as a flexible framework for modeling acute viral infection and cellular immune memory development. The mechanism/rule-based nature of the CIABM allows for interrogation of the complex dynamics of the human immune system during various types of viral infections. The CIABM is an extension of a prior agent-based model of the innate immune response, incorporating additional cellular types and mediators involved in the response to viral infection. The CIABM simulates the dynamics of viral respiratory infection in terms of epithelial invasion, immune cellular population changes and cytokine measurements. Validation of the CIABM involved effectively replicating in vivo measurements of circulating mediator levels from a clinical cohort of influenza patients. The general purpose nature of the CIABM allows for both the representation of various types of known viral infections and facilitates the exploration of hypothetical, novel viral pathogens.

immunology↗

bioRxiv2019

Nested Active Learning for Efficient Model Contextualization and Parameterization

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free-parameters. In this work, we utilize a nested active-learning workflow to efficiently parameterize and contextualize an agent-based model (ABM) of systemic inflammation used to examine sepsis. MethodsContextual parameter space was examined using four parameters external to the models rule-set. The models internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested active learning approach in which the clinically relevant model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN which uses active learning to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. ResultsWe have reduced the number of simulations required to efficiently map the clinically relevant parameter space of this model by approximately 99%. Additionally, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

systems biology↗