Matrix analysis
Aggregation/disaggregation methods for computing the stationary distribution of a Markov chain
SIAM Journal on Numerical Analysis
Expokit: a software package for computing matrix exponentials
ACM Transactions on Mathematical Software (TOMS)
Hybrid method for the chemical master equation
Journal of Computational Physics
Adaptive solution of the master equation in low dimensions
Applied Numerical Mathematics
Spectral approximation of solutions to the chemical master equation
Journal of Computational and Applied Mathematics
A modified uniformization method for the solution of the chemical master equation
Computers & Mathematics with Applications
Solving chemical master equations by adaptive wavelet compression
Journal of Computational Physics
Hybrid numerical solution of the chemical master equation
Proceedings of the 8th International Conference on Computational Methods in Systems Biology
An Adaptive Wavelet Method for the Chemical Master Equation
SIAM Journal on Scientific Computing
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Stochastic Model Simulation Using Kronecker Product Analysis and Zassenhaus Formula Approximation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hi-index | 7.31 |
An important driver of gene regulatory networks is noise arising from the stochastic nature of interactions of genes, their products and regulators. Thus, such systems are stochastic and can be modelled by the chemical master equations. A major challenge is the curse of dimensionality which occurs when one attempts to integrate these equations. While stochastic simulation techniques effectively address the curse, many repeated simulations are required to provide precise information about stationary points, bifurcation phenomena and other properties of the stochastic processes. An alternative way to address the curse of dimensionality is provided by sparse grid approximations. The sparse grid methodology is applied and the application demonstrated to work efficiently for up to 10 proteins. As sparse grid methods have been developed for the approximation of smooth functions, a variant for infinite sequences had to be developed together with a multiresolution analysis similar to Haar wavelets. Error bounds are provided which confirm the effectiveness of sparse grid approximations for smooth high-dimensional probability distributions.