Infectio: a Generic Framework for Computational Simulation of Virus Transmission between Cells

Area of Science:

• Virology
• Computational Biology
• Cell Biology

Background:

• Viruses spread via cell-free and cell-cell mechanisms, influencing disease and therapy outcomes.
• Distinguishing these transmission modes in cell cultures is challenging despite live-cell imaging.
• Understanding viral spread is crucial for developing effective antiviral and gene therapies.

Purpose of the Study:

• To introduce Infectio, a cross-platform software framework for simulating viral transmission phenotypes in tissue culture.
• To enable the differentiation between cell-cell and cell-free virus spreading mechanisms.
• To provide a tool for elucidating virus transmission and informing therapeutic strategies.

Main Methods:

• Developed a generalized, open-source software framework (Infectio) for simulating viral spread.
• Enabled estimation of interdependent biological parameters for virus infections.
• Designed the software for low complexity, stable, and error-free output on major operating systems.

Main Results:

• Infectio simulates plaque phenotypes from image-based assays, differentiating between cell-cell and cell-free transmission.
• The software allows in silico testing of hypotheses about viral spreading mechanisms using experimental parameters.
• Facilitates characterization of therapeutic agent modes of action, including oncolytic viruses.

Conclusions:

• Infectio offers a generalized platform for analyzing virus infection spread in cell cultures.
• The software aids in understanding the biophysical mechanisms of viral transmission.
• Infectio supports the design of strategies to perturb or enhance viral transmission and evaluate therapeutic agents.