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Abstract

As the life sciences become more quantitative, particle-based simulation tools can be used to model the complex spatiotemporal dynamics of biological systems with single particle resolution. In particular, they naturally account for the stochastic nature of molecular reactions. Here I apply this approach to three different biological systems that are intrinsically stochastic. As an example for cellular information processing, we investigate the receptor dynamics of the interferon type I system and show that asymmetric dimerization reactions between signaling receptors in the plasma membrane enable cells to discriminate between different ligands. Using an information theoretic framework, we show why the binding asymmetry enables this system to become robust against ligand concentration fluctuations. As an example for structure formation, we analyze the role of stochasticity and geometrical confinement for the Min oscillations that bacteria use to determine their middle. We predict mode selection as a function of geometry in excellent agreement with recent experiments and quantify the stochastic switching of oscillation modes leading to multistable oscillation patterns. As an ex- ample for self-assembly, we use a multiparticle collision dynamics (MPCD) approach to address how shear flow modulates the assembly of rings and capsids. We find that an intermediate level of shear flow can help to suppress the emergence of malformed structures. Together, these projects demonstrate the power and wide applicability of particle-based computer simulations of biological reaction-diffusion systems.