A mechanistic Individual-based Model of microbial communities.

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for "bottom up" prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.

RealityGrid: an integrated approach to middleware through ICENI.

The advancement of modelling and simulation within complex scientific applications is currently constrained by the rate at which knowledge can be extracted from the data produced. As Grid computing evolves, new means of increasing the efficiency of data analysis are being explored. RealityGrid aims to enable more efficient use of scientific computing resources within the condensed matter, materials and biological science communities. The Imperial College e-Science Networked Infrastructure (ICENI) Grid middleware provides an end-to-end pipeline that simplifies the stages of computation, simulation and collaboration. The intention of this work is to allow all scientists to have access to these features without the need for heroic efforts that have been associated with this sort of work in the past. Scientists can utilise advanced scheduling mechanisms to ensure efficient planning of computations, visualize and interactively steer simulations and securely collaborate with colleagues via the Access Grid through a single integrated middleware application.