Abstract

BackgroundThe intracellular environment is a complex and crowded medium where the diffusion of proteins, metabolites and other molecules can be decreased. One of the most popular methodologies for the simulation of diffusion in crowding systems is the Monte Carlo algorithm (MC) which tracks the movement of each particle. This can, however, be computationally expensive for a system comprising a large number of molecules. On the other hand, the Lattice Boltzmann Method (LBM) tracks the movement of collections of molecules, which represents significant savings in computational time. Nevertheless in the classical manifestation of such scheme the crowding conditions are neglected.MethodsIn this paper we use Scaled Particle Theory (SPT) to approximate the probability to find free space for the displacement of hard-disk molecules and in this way to incorporate the crowding effect to the LBM. This new methodology which couples SPT and LBM is validated using a kinetic Monte Carlo (kMC) algorithm, which is used here as our "computational experiment".ResultsThe results indicate that LBM over-predicts the diffusion in 2D crowded systems, while the proposed coupled SPT-LBM predicts the same behaviour as the kinetic Monte Carlo (kMC) algorithm but with a significantly reduced computational effort. Despite the fact that small deviations between the two methods were observed, in part due to the mesoscopic and microscopic nature of each method, respectively, the agreement was satisfactory both from a qualitative and a quantitative point of view.ConclusionsA crowding-adaptation to LBM has been developed using SPT, allowing fast simulations of diffusion-systems of different size hard-disk molecules in two-dimensional space. This methodology takes into account crowding conditions; not only the space fraction occupied by the crowder molecules but also the influence of the size of the crowder which can affect the displacement of molecules across the lattice system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0769-8) contains supplementary material, which is available to authorized users.

Highlights

  • The intracellular environment is a complex and crowded medium where the diffusion of proteins, metabolites and other molecules can be decreased

  • According to a drawing proposed by Goodsell [5], if the cytoplasm of Escherichia coli is divided into 600 cubes of (100 nm)3, an average of 130 glycolytic enzymes and 100 from the Krebbs cycle are present in each cube in addition to the metabolites and other compounds, which all together comprise a very large number of molecules for the simulation of the intracellular environment

  • The methodologies Lattice Boltzmann Method (LBM) and crowding-Lattice Boltzmann Method (cLBM) were programed in MATLAB R2011a (The MathWorks, Natick, MA), while the lattice kinetic Monte Carlo (kMC) was implemented in Fortran 90

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Summary

Introduction

The intracellular environment is a complex and crowded medium where the diffusion of proteins, metabolites and other molecules can be decreased. One of the most popular methodologies for the simulation of diffusion in crowding systems is the Monte Carlo algorithm (MC) which tracks the movement of each particle This can, be computationally expensive for a system comprising a large number of molecules. The analysis and simulation of the factors affecting the metabolism of these organisms allow the further identification of the strategies needed for its manipulation in order to increase the formation of the metabolite of interest over other by-products. As it is known the environmental conditions and the properties of the medium play an important role in the metabolism. We will use the terms molecule and particle interchangeably to refer to the intracellular macromolecules, e.g., proteins

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