We provide CUDA code for Nvidia GPUs of a 3D model of epidermal development. The model includes GPU implementation of the subcellular element method for 3D spatial cells, an intracellular gene network for each cell represented by a set of ODEs, cell-cell neighbor communication through Notch signaling as part of each cell's internal gene network, and cell behaviors of growth and division.
We provide our Objective-C (Cocoa for Mac, GNUstep for Linux/Windows) optimization framework to learn linear gene regulatory networks from various types of gene expression data. The optimization incorporates network sparsity constraint through L1 regularization as well as incorporation of existing network information. The framework can handle wild type, perturbation, gene knockout and heterozygous knockdown gene expression data.
Download: Gene Network Inference Tool
We provide Matlab and C codes based on a novel and efficient algorithm for Reaction-Diffusion equations that model spatial dynamics of complex biological systems. The numerical method used in the code is designed for effective treatment of stiff reactions in spatial systems.
This is an exact method for stochastic simulation of chemical reaction networks (Exact R-Leap) to accelerate the Stochastic Simulation Algorithm (SSA).