104 citations · 171 across the 17 of their papers we have counts for
11 papers · 1 filter
Using PyBioNetFit to Leverage Qualitative and Quantitative Data in Biological Model Parameterization and Uncertainty Quantification
Ely F. Miller, Abhishek Mallela, Jacob Neumann +3
Data generated in studies of cellular regulatory systems are often qualitative. For example, measurements of signaling readouts in the presence and absence of mutations may reveal…
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit
Jacob Neumann, Yen Ting Lin, Abhishek Mallela +6
Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not anal…
Parameter Estimation and Uncertainty Quantification for Systems Biology Models
Eshan D. Mitra, William S. Hlavacek
Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions.…
PyBioNetFit and the Biological Property Specification Language
Eshan D. Mitra, Ryan Suderman, Joshua Colvin +5
In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modeler…
Scaling methods for accelerating kinetic Monte Carlo simulations of chemical reaction networks
Yen Ting Lin, Song Feng, William S. Hlavacek
Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit ti…
A Step-by-Step Guide to Using BioNetFit
William S. Hlavacek, Jennifer Longo, Lewis R. Baker +9
BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitti…