Cudone, E. | Search bioRxiv

bioRxiv2026

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

Mechanisms that lead to viral chronicity are poorly understood, but cell-to-cell spread has been implicated in the establishment of chronic infections. We previously developed mathematical models to explore the nature of hepatitis C virus (HCV) cell-to-cell spread in vitro and quantified the effect of inhibiting individual host factors involved. However, the previous models were not designed to (i) address cell proliferation, (ii) account for differences in cell size, and (iii) did not include possible foci merging. Herein we have developed an agent-based model (ABM) to simulate HCV cell-to-cell spread in vitro by modeling individual cell behaviors. This model recapitulates the natural increase of cell confluence that occurs in vitro accompanied by a concomitant decrease in cell size by allowing for independent proliferation cycles of individual cells within a restricted space. The model fits the experimental foci expansion data well and allows assessment of foci merging while reproducing the irregular HCV foci shape observed in cell culture. Altogether, the new more inclusive model has the potential to help elucidate the dynamics of HCV cell-to-cell spread and provide accurate predictions regarding the efficacy of antiviral drugs. Author SummaryDespite remarkable progress in treatments for hepatitis C virus (HCV), HCV infection remains a global public health burden with over 50 million chronic HCV infections and about 1 million new infections occurring annually. Once infection occurs, HCV can spread in the liver multiple ways. One mechanism is cell-to-cell (CTC) spread where the virus moves directly from one infected cell to an adjacent cell without moving through the extracellular space. Previous mathematical models of HCV CTC spread were not designed to incorporate cell proliferation, cell size, or foci merging. Therefore, to better mimic cells in culture, we developed a novel agent-based model (ABM) that allows the simulated cells to proliferate resulting in an increased number of cells with concomitant decrease in cell/agent size analogous to what happens as cells become tightly packed in culture. This new ABM not only can be used to estimate efficacy values of HCV cell-to-cell spread inhibitors (e.g., when different factors involved in cell-to-cell spread are blocked), but also should enable modeling of HCV CTC spread under a wider variety of cell culture conditions and thus help elucidate the impact of different viral-host dynamics on HCV CTC spread.

microbiology↗

bioRxiv2023

Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories

Biophysically detailed simulations attempting to reproduce neuronal activity often rely on solving large systems of differential equations; in some models, these systems have tens of thousands of states per cell. Numerically solving these equations is computationally intensive and requires making assumptions about the initial cell states. Additional realism from incorporating more biological detail is achieved at the cost of increasingly more states, more computational resources, and more modeling assumptions. We show that for both point and morphologically-detailed cell models, the presence and timing of future action potentials is probabilistically well-characterized by the relative timings of a small number of recent synaptic events alone. Knowledge of initial conditions or full synaptic input history is not a requirement. While model time constants, etc. impact the specifics, we demonstrate that for both individual spikes and sustained cellular activity, the uncertainty in spike response decreases to the point of approximate determinism. Further, we show cellular model states are reconstructable from ongoing synaptic events, despite unknown initial conditions. We propose that a strictly event-based modeling framework is capable of representing the full complexity of cellular dynamics of the differential-equations models with significantly less per-cell state variables, thus offering a pathway toward utilizing modern data-driven modeling to scale up to larger network models while preserving individual cellular biophysics.

neuroscience↗