Passive Microrheology Simulations Using Stochastic Particle Methods

Particle-based stochastic simulations

Most of the modelling approaches used in Computational Systems Biology are population-based, discarding reactant individuality. While those methods have been, and still are, tremendously useful, the complexity of cellular reactions calls for another type of modelling approach, based on particle-based simulations. The discrete stochastic approaches permit to track the state and position of each ...

Binning for Efficient Stochastic Particle Simulations

Gillespie’s Stochastic Simulation Algorithm (SSA) is an exact procedure for simulating the evolution of a collection of discrete, interacting entities, such as coalescing aerosol particles or reacting chemical species. The high computational cost of SSA has motivated the development of more efficient variants, such as Tau-Leaping, which sacrifices the exactness of SSA. For models whose interact...

Binning for Efficient Stochastic Multiscale Particle Simulations

Particle Dynamics Methods of Blood Flow Simulations

Various particle methods are widely used to model dynamics of complex media. In this work molecular dynamics and dissipative particles dynamics are applied to model blood flows composed of plasma and erythrocytes. The properties of the homogeneous particle fluid are studied. Capillary flows with erythrocytes are investigated.

Python algorithms in particle tracking microrheology

UNLABELLED BACKGROUND Particle tracking passive microrheology relates recorded trajectories of microbeads, embedded in soft samples, to the local mechanical properties of the sample. The method requires intensive numerical data processing and tools allowing control of the calculation errors. RESULTS We report the development of a software package collecting functions and scripts written in...