BMX: Biological modelling and interface exchange

Abstract

High performance computing has a great potential to provide a range of significant benefits for investigating biological systems. These systems often present large modelling problems with many coupled subsystems, such as when studying colonies of bacteria cells. The aim to understand cell colonies has generated substantial interest as they can have strong economic and societal impacts through their roles in in industrial bioreactors and complex community structures, called biofilms, found in clinical settings. Investigating these communities through realistic models can rapidly exceed the capabilities of current serial software. Here, we introduce BMX, a software system developed for the high performance modelling of large cell communities by utilising GPU acceleration. BMX builds upon the AMRex adaptive mesh refinement package to efficiently model cell colony formation under realistic laboratory conditions. Using simple test scenarios with varying nutrient availability, we show that BMX is capable of correctly reproducing observed behavior of bacterial colonies on realistic time scales demonstrating a potential application of high performance computing to colony modelling. The open source software is available from the zenodo repository https://doi.org/10.5281/zenodo.8084270 under the BSD-2-Clause licence.

Stochastic biological system-of-systems modelling for iPSC culture

“…In a broader scope, several multiscale bioprocess models have been developed for studying aggregate structures in clinical studies, such as cancer tissues 22 , 23 , and biofilms 24 . Notable examples include software packages like PhysiCell 25 , Chaste 26 , ChemChaste 27 , BMX 28 , and Morpheus 29 . These tools fall under the category of agent-based models, which integrate cell-based metabolic reactions, reaction-diffusion processes, and individual cell growth dynamics.…”

Sample-efficient reinforcement learning and its applications

“…In a broader scope, several multiscale bioprocess models have been developed for studying aggregate structures in clinical studies, such as cancer tissues [307,308], and biofilms [309]. Notable examples include software packages like PhysiCell [310], Chaste [311], ChemChaste [312], BMX [313], and Morpheus [314]. These tools fall under the category of agent-based models, which integrate cell-based metabolic reactions, reaction-diffusion processes, and individual cell growth dynamics.…”

“…Third, the proposed Bio-SoS simulation provides a comprehensive and efficient way to account for the complex and stochastic nature of iPSC aggregate and monolayer cultures. In contrast to existing agent-based simulation tools [307,308,309,310,311,312,313,314], the Bio-SoS model is a specialized simulation tool tailored for iPSC cultures. It can improve simulation efficiency through (1) modeling iPSC aggregation using population balance equations to ensure a more efficient representation of cellular dynamics during aggregation; (2) constructing a coarse-grained approach that divides each aggregate into small spherical shells and assumes cellular metabolisms and microenvironment are homogeneous in each spherical shell; and (3) utilizing a single-cell SMN model to predict the cell metabolic response to environmental variation.…”