Simulation of the kinetics of neuromuscular block: implications for speed of onset

Molecular mechanisms of muscular and non-muscular actions of neuromuscular blocking agents in critical illness: a narrative review

Despite frequent use of neuromuscular blocking agents in critical illness, changes in neuromuscular transmission with critical illness are not well appreciated. Recent studies have provided greater insights into the molecular mechanisms for beneficial muscular effects and non-muscular anti-inflammatory properties of neuromuscular blocking agents. This narrative review summarises the normal structure and function of the neuromuscular junction and its transformation to a 'denervation-like' state in critical illness, the underlying cause of aberrant neuromuscular blocking agent pharmacology. We also address the important favourable and adverse consequences and molecular bases for these consequences during neuromuscular blocking agent use in critical illness. This review, therefore, provides an enhanced understanding of clinical therapeutic effects and novel pathways for the salutary and aberrant effects of neuromuscular blocking agents when used during acquired pathologic states of critical illness.

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables.

Stochastic simulations (simulations where randomness plays a role) of even simple biological systems are often so computationally intensive that it is impossible, in practice, to simulate them exhaustively and gather good statistics about the likelihood of different outcomes. The difficulty is compounded for the observation of rare events in these simulations; unfortunately, rare events, such as state transitions and barrier crossings, are often those of particular interest. Using the weighted ensemble (WE) method, we are able to enhance the characterization of rare events in cell biology simulations, but in such a way that the statistics for these events remain unbiased. The histogram of outcomes that WE produces has the same shape as a naive one, but the resolution of events in the tails of the histogram is greatly improved. This improved resolution in rare event statistics can be used to infer unbiased estimates of long timescale dynamics from short simulations, and we show that using a weighted ensemble can result in a reduction in total simulation time needed to sample certain events of interest in spatial, stochastic models of biological systems.