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Open Access Methodology article

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

Scott Christley134*, Briana Lee24, Xing Dai24 and Qing Nie134*

Author Affiliations

1 Department of Mathematics, University of California, Irvine, CA 92697, USA

2 Department of Biological Chemistry, University of California, Irvine, CA 92697, USA

3 Center for Mathematical and Computational Biology, University of California, Irvine, CA 92697, USA

4 Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA

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BMC Systems Biology 2010, 4:107  doi:10.1186/1752-0509-4-107

Published: 9 August 2010

Abstract

Background

Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.

Results

We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture.

Conclusions

We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community.