Biocellion: accelerating computer simulation of multicellular biological system models

Authored by Seunghwa Kang, Simon Kahan, Jason McDermott, Nicholas Flann, Ilya Shmulevich

Date Published: 2014-11-01

DOI: 10.1093/bioinformatics/btu498

Sponsors: United States Department of Energy (DOE)

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Pseudocode

Model Code URLs: Model code not found

Abstract

Motivation: Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. Results: We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies.

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