Jonathan Ozik

Modeling in vitro cell-to-cell spread of hepatitis C viral infection using an agent-based approach

This paper develops an agent-based model to simulate HCV cell-to-cell spread in vitro, incorporating cell proliferation, size changes, and foci merging, and shows it fits experimental data well.

Modeling of hepatitis B virus infection spread in primary human hepatocytes

The paper models HBV infection spread in primary human hepatocytes using agent-based modeling, identifying viral kinetic patterns and estimating the efficacy of an entry inhibitor.

Multiphasic hepatitis B virus kinetic patterns in humanized chimeric mice can be explained via stochastic agent-based modeling of intracellular virion production cycles

The paper develops a stochastic agent-based model to explain multiphasic HBV kinetics in humanized mice by simulating intracellular virion production cycles.

Nested Active Learning for Efficient Model Contextualization and Parameterization

This paper uses nested active learning to efficiently parameterize and contextualize an agent-based model of systemic inflammation, reducing required simulations by 99%.

Iterative community-driven development of a SARS-CoV-2 tissue simulator

Open-source agent-based PhysiCell model of SARS-CoV-2 infection in lung epithelium; version 5 clears virus in about 6 days in simulation, and delayed T cell arrival increases epithelial loss.

Learning-accelerated Discovery of Immune-Tumour Interactions

The paper presents a framework combining agent-based simulation and active learning to discover optimal cancer regression regions in immunotherapy models.

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

This paper introduces a high-throughput framework integrating PhysiCell and EMEWS to test cancer hypotheses through mechanistic simulations and optimize therapeutic strategies.