Abstract
Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems.
Highlights
Gene expression and signaling events in single cells are stochastic processes
We used the proposed agent-based models (ABMs) simulation to investigate the response to viral infection of monocyte-derived human dendritic cells (DCs), which are the primary response cells that detect infection and trigger the initial innate immune response [25,26]
Activated Rig-I molecules set in motion a signaling cascade that leads to the induction of the interferon beta gene, which upon translation into protein is secreted from the cell
Summary
Gene expression and signaling events in single cells are stochastic processes. When stochastic processes are considered, analyses of single cell systems are normally performed assuming that the cells do not interact, and simulations are done using either Gillespie’s algorithm [8], or Langevin equations [9]. Gillespie simulations follow the number of molecules present in a cell for several molecular species, and quickly become inefficient as the number of molecules becomes large. Langevin equations can be solved efficiently numerically, but can only be used accurately under a set of restrictive assumptions. Several modifications to the classical Gillespie algorithm have been proposed, some of which lead to more efficient computation [10,11,12,13]; others offer parallelization of the algorithm [14,15], and others separation of time scales through the use of hybrid deterministic-stochastic approaches [16,17], or by allowing to estimate the effect of processes that may occur multiple times during a time-step ( known as tau-leaping) [18]
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