104 citations · 171 across the 17 of their papers we have counts for
18 papers
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…
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification
Yen Ting Lin, Jacob Neumann, Ely Miller +7
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. O…
Bayesian Uncertainty Quantification for Systems Biology Models Parameterized Using Qualitative Data
Eshan D. Mitra, William S. Hlavacek
Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of s…
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…