Filters








1,416 Hits in 4.3 sec

Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks [chapter]

Sergiy Bogomolov, Thomas A. Henzinger, Andreas Podelski, Jakob Ruess, Christian Schilling
2015 Lecture Notes in Computer Science  
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes  ...  The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations.  ...  In Section 4, we propose our automated adaptive parameter inference method. In Section 5, we study the performance of our method for some benchmark reaction networks.  ... 
doi:10.1007/978-3-319-23401-4_8 fatcat:fhvrznqssfdmndx4mvlxllnrqa

Adaptive moment closure for parameter inference of biochemical reaction networks

Christian Schilling, Sergiy Bogomolov, Thomas A. Henzinger, Andreas Podelski, Jakob Ruess
2016 Biosystems (Amsterdam. Print)  
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes  ...  The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations.  ...  Acknowledgments This work is based on the CMSB 2015 paper "Adaptive moment closure for parameter inference of biochemical reaction networks" (Bogomolov et al., 2015)  ... 
doi:10.1016/j.biosystems.2016.07.005 pmid:27461396 fatcat:ijnlhenre5eehgf27ql34vmfja

Parameter estimation for biochemical reaction networks using Wasserstein distances [article]

Kaan Öcal, Ramon Grima, Guido Sanguinetti
2019 arXiv   pre-print
We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances.  ...  We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters.  ...  Acknowledgments We would like to thank our reviewers for their suggestions and careful reading of the manuscript.  ... 
arXiv:1907.07986v2 fatcat:qzgvos7khrepha2ozrwes5rhvy

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

Anna Klimovskaia, Stefan Ganscha, Manfred Claassen, Daniel A Beard
2016 PLoS Computational Biology  
networks by implicitly accounting for billions of topology variants.  ...  structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks.  ...  Acknowledgments We acknowledge Justin Feigelman, Will Macnair, Eirini Arvaniti and Dimitris Christodoulou for helpful discussions and feedback on the manuscript. Formal analysis: AK MC.  ... 
doi:10.1371/journal.pcbi.1005234 pmid:27923064 pmcid:PMC5140059 fatcat:mmsid5eoo5fb3js5aeh7yglrru

Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models [article]

Kaan Ocal, Michael U Gutmann, Guido Sanguinetti, Ramon Grima
2022 bioRxiv   pre-print
We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback.  ...  trade accuracy for tractability.  ...  As a consequence Bayesian inference for biochemical reaction networks often relies on a variety of approximations to the likelihood function [8, 10] .  ... 
doi:10.1101/2022.01.25.477666 fatcat:l5i2in2zxzcyjhygqtbl7tpbaq

Generalized method of moments for estimating parameters of stochastic reaction networks

Alexander Lück, Verena Wolf
2016 BMC Systems Biology  
We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model  ...  The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered.  ...  In this paper we explore the usefulness of the GMM for moment-based simulations of stochastic reaction networks.  ... 
doi:10.1186/s12918-016-0342-8 pmid:27769280 pmcid:PMC5073941 fatcat:uismfqeyxjfntdlop4tb73l6fi

Approximation and inference methods for stochastic biochemical kinetics—a tutorial review

David Schnoerr, Guido Sanguinetti, Ramon Grima
2017 Journal of Physics A: Mathematical and Theoretical  
Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation.  ...  First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods.  ...  Computational methods for Bayesian inference in stochastic chemical reaction networks 6.4.1. Methods for general networks.  ... 
doi:10.1088/1751-8121/aa54d9 fatcat:bdds2szh5zhlpc6j3kesftujgi

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

Fabian Fröhlich, Philipp Thomas, Atefeh Kazeroonian, Fabian J. Theis, Ramon Grima, Jan Hasenauer, Daniel A Beard
2016 PLoS Computational Biology  
In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise  ...  Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems.  ...  Parameter estimation To infer the parameters of biochemical reaction networks we employ maximum likelihood and Bayesian parameter estimation.  ... 
doi:10.1371/journal.pcbi.1005030 pmid:27447730 pmcid:PMC4957800 fatcat:y5sor4k225bypfm7gqaw66d75a

Molecular circuits for dynamic noise filtering

Christoph Zechner, Georg Seelig, Marc Rullan, Mustafa Khammash
2016 Proceedings of the National Academy of Sciences of the United States of America  
Here we develop an optimal filtering theory that is suitable for noisy biochemical networks.  ...  filtering | noise cancellation | adaptive design  ...  The authors are grateful to Yuan Chen and Sundipta Rao for providing technical assistance to the first author while he was performing the experiments.  ... 
doi:10.1073/pnas.1517109113 pmid:27078094 pmcid:PMC4855548 fatcat:m2turhi62fcg3cvqojvkrytqq4

Model reduction for stochastic CaMKII reaction kinetics in synapses by graph-constrained correlation dynamics

Todd Johnson, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
2015 Physical Biology  
A stochastic reaction network model of Ca(2+) dynamics in synapses (Pepke et al PLoS Comput.  ...  Data from numerically intensive simulations is used to train a reduced model that, out of sample, correctly predicts the evolution of interaction parameters characterizing the instantaneous probability  ...  Introduction Given a stochastic reaction network, even one specified by high-level 'parameterized reactions' or 'rule-based' notation [2] [3] [4] [5] [6] , there is a corresponding chemical master equation  ... 
doi:10.1088/1478-3975/12/4/045005 pmid:26086598 pmcid:PMC4489159 fatcat:bpqivawfdzchtaoza3rb3cezmu

Multivariate moment closure techniques for stochastic kinetic models

Eszter Lakatos, Angelique Ale, Paul D. W. Kirk, Michael P. H. Stumpf
2015 Journal of Chemical Physics  
Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems.  ...  Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution.  ...  The reduced system size can be beneficial in most inference tasks, as these algorithms involve evaluating the system for thousands of parameter sets.  ... 
doi:10.1063/1.4929837 pmid:26342359 fatcat:doyt7wkc7jc4hl2cd3syi4tfpq

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

Pavel Loskot, Komlan Atitey, Lyudmila Mihaylova
2019 Frontiers in Genetics  
Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably.  ...  The key processes in biological and chemical systems are described by networks of chemical reactions.  ...  ., 2011) , or as networks of biochemical reactions (Ashyraliyev et al., 2009) .  ... 
doi:10.3389/fgene.2019.00549 pmid:31258548 pmcid:PMC6588029 fatcat:moxooil5argjhgcb6bpv7zz2gu

Comprehensive review of models and methods for inferences in bio-chemical reaction networks [article]

Pavel Loskot and Komlan Atitey and Lyudmila Mihaylova
2019 arXiv   pre-print
Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably.  ...  Key processes in biological and chemical systems are described by networks of chemical reactions.  ...  ., 2011) , or as networks of biochemical reactions (Ashyraliyev et al., 2009) .  ... 
arXiv:1902.05828v1 fatcat:sjb4jm53a5h3jf2swbiz7vk6hu

Model-Checking Based Approaches to Parameter Estimation of Gene Regulatory Networks

Andrzej Mizera, Jun Pang, Qixia Yuan
2014 2014 19th International Conference on Engineering of Complex Computer Systems  
Our goal is to develop new algorithms and tools, which are tailored for the modelling and analysis of gene regulatory networks, by exploring model checking techniques that have been developed and widely  ...  of gene regulatory networks.  ...  There exist a number of methods for parameter estimation for Markov models describing networks of biochemical reactions.  ... 
doi:10.1109/iceccs.2014.38 dblp:conf/iceccs/MizeraPY14 fatcat:ejgc4esdqne5hh6eejbyzaukwq

Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data

Eugenio Cinquemani
2018 Processes  
Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions.  ...  Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems.  ...  In this paper, we discuss identifiability and reconstruction of unknown parameters and interactions in biochemical reaction networks.  ... 
doi:10.3390/pr6090136 fatcat:hddkkz6e5rfxpgx7brv6ibdo4y
« Previous Showing results 1 — 15 out of 1,416 results