Sampling methods for solving Bayesian model updating problems: A tutorial
This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …
context of Bayesian model updating for engineering applications. Markov Chain Monte …
Studying stochastic systems biology of the cell with single-cell genomics data
Recent experimental developments in genome-wide RNA quantification hold considerable
promise for systems biology. However, rigorously probing the biology of living cells requires …
promise for systems biology. However, rigorously probing the biology of living cells requires …
Monte Carlo samplers for efficient network inference
Accessing information on an underlying network driving a biological process often involves
interrupting the process and collecting snapshot data. When snapshot data are stochastic …
interrupting the process and collecting snapshot data. When snapshot data are stochastic …
Gene expression model inference from snapshot RNA data using Bayesian non-parametrics
Gene expression models, which are key towards understanding cellular regulatory
response, underlie observations of single-cell transcriptional dynamics. Although RNA …
response, underlie observations of single-cell transcriptional dynamics. Although RNA …
The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments
Modern optical imaging experiments not only measure single-cell and single-molecule
dynamics with high precision, but they can also perturb the cellular environment in myriad …
dynamics with high precision, but they can also perturb the cellular environment in myriad …
Avoiding matrix exponentials for large transition rate matrices
Exact methods for the exponentiation of matrices of dimension N can be computationally
expensive in terms of execution time (N3) and memory requirements (N2), not to mention …
expensive in terms of execution time (N3) and memory requirements (N2), not to mention …
Bayesian inference of stochastic reaction networks using multifidelity sequential tempered Markov chain Monte Carlo
TA Catanach, HD Vo, B Munsky - International journal for …, 2020 - dl.begellhouse.com
Stochastic reaction network models are often used to explain and predict the dynamics of
gene regulation in single cells. These models usually involve several parameters, such as …
gene regulation in single cells. These models usually involve several parameters, such as …
Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation
Quantitative stochastic models of gene regulatory networks are important tools for studying
cellular regulation. Such models can be formulated at many different levels of fidelity. A …
cellular regulation. Such models can be formulated at many different levels of fidelity. A …
Bayesian Parameter Inference in Stochastic Biochemical Models Using Moment Approximations
The chemical master equation (CME) is a mathematical tool utilized to model the
stochasticity of the complex biochemical reaction networks. As the direct solution of the CME …
stochasticity of the complex biochemical reaction networks. As the direct solution of the CME …
Exploiting Intrinsic Noise for Heterogeneous Cell Control Under Time Delays and Model Uncertainties
MP May, B Munsky - bioRxiv, 2023 - biorxiv.org
The majority of previous research in synthetic biology has focused on enabling robust
control performance despite the presence of noise, while the understanding for how …
control performance despite the presence of noise, while the understanding for how …