77 citations · 164 across the 9 of their papers we have counts for
12 papers
BioSimulators: a central registry of simulation engines and services for recommending specific tools
Bilal Shaikh, Lucian P. Smith, Dan Vasilescu +68
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, becau…
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
Fredrik Wrede, Robin Eriksson, Richard Jiang +4
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. Existing ap…
Implicit Hamiltonian Monte Carlo for Sampling Multiscale Distributions
Arya A. Pourzanjani, Linda R. Petzold
Hamiltonian Monte Carlo (HMC) has been widely adopted in the statistics community because of its ability to sample high-dimensional distributions much more efficiently than other M…
Selecting the Metric in Hamiltonian Monte Carlo
Ben Bales, Arya Pourzanjani, Aki Vehtari +1
We present a selection criterion for the Euclidean metric adapted during warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a sampler to automatically pick the…
Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning
Stefan Hellander, Andreas Hellander, Linda Petzold
The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice…
Research and Education in Computational Science and Engineering
Ulrich Rüde, Karen Willcox, Lois Curfman McInnes +30
Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance…
Reaction rates for reaction-diffusion kinetics on unstructured meshes
Stefan Hellander, Linda Petzold
The reaction-diffusion master equation is a stochastic model often utilized in the study of biochemical reaction networks in living cells. It is applied when the spatial distributi…
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Brian Drawert, Michael Trogdon, Salman Toor +2
Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well…
Reaction rates for a generalized reaction-diffusion master equation
Stefan Hellander, Linda Petzold
It has been established that there is an inherent limit to the accuracy of the reaction-diffusion master equation. Specifically, there exists a fundamental lower bound on the mesh…
Reaction rates for mesoscopic reaction-diffusion kinetics
Stefan Hellander, Andreas Hellander, Linda Petzold
The mesoscopic reaction-diffusion master equation (RDME) is a popular modeling framework, frequently applied to stochastic reaction-diffusion kinetics in systems biology. The RDME…
Local error estimates for adaptive simulation of the Reaction-Diffusion Master Equation via operator splitting
Andreas Hellander, Michael Lawson, Brian Drawert +1
The efficiency of exact simulation methods for the reaction-diffusion master equation (RDME) is severely limited by the large number of diffusion events if the mesh is fine or if d…
On the Reaction Diffusion Master Equation in the Microscopic Limit
Stefan Hellander, Andreas Hellander, Linda Petzold
Stochastic modeling of reaction-diffusion kinetics has emerged as a powerful theoretical tool in the study of biochemical reaction networks. Two frequently employed models are the…