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Stochastic modelling is the development of mathematical models for non-deterministic physical systems, which can adopt many possible behaviours starting from any given initial condition. Monte-Carlo simulations, for example, consist of exploring the various possible states of a complex probabilistic system through random sampling of initial conditions and repeated computer simulations.
Plasmid copy number and gene circuit design together shape how genetic mutations emerge at the phenotypic level in bacteria. Here the authors characterize how the interplay of gene dosage via plasmid copy number and regulatory architecture affect the phenotypic mutation rate.
Vinyard et al. present a generative method to model cell dynamics using neural stochastic differential equations that learn state-dependent drift and diffusion, outperforming existing approaches and enabling perturbation studies of development and disease.
Physical modeling is increasingly important for generating insights into intracellular processes. We describe situations in which combined spatial and stochastic aspects of chemical reactions are needed to capture the relevant dynamics of biochemical systems.
Biological systems can adapt to changes in their environment over a wide range of conditions, but responding quickly and accurately is energetically costly. A study pins down the relationship between energy, speed and accuracy.
