Leveraging compute clusters for large-scale parametric screens of reaction-diffusion systems

EasyChair Preprint 16

Abstract

Reaction-diffusion (RD) models are widely used to study the spatio-temporal evolution of pattern formation during development. Nonlinear RD models are usually analytically intractable, and require numerical solution methods. Interrogation of RD models for a large physiological range of parameters covers many orders of magnitude- establishing situations where solutions are stiff and solvers fail to provide accurate results to the time-dependent problem. The spatial dependence of these parameters, and the nonlinearity of the underlying dynamics, impose additional challenges. We developed an efficient approach of simulating stiff RD models of pattern formation and we used supercomputer clusters to carry out a large scale screen of spatially varying parameters for biological pattern formation. The approaches outlined herein are applicable to any systems biology problem requiring numerical approximation of RD equations with spatially non-uniform properties and stiff non-linear reactions.

Keyphrases: Cluster Computing, In-silico data, reaction-diffusion models, systems biology

@booklet{EasyChair:16,
  author    = {Md Shahriar Karim and Hans G. Othmer and David M. Umulis},
  title     = {Leveraging compute clusters for large-scale parametric screens of reaction-diffusion systems},
  doi       = {10.29007/p5z8},
  howpublished = {EasyChair Preprint 16},
  year      = {EasyChair, 2018}}