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Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

Bernie J Daigle, Min K Roh, Linda R Petzold, Jarad Niemi
2012 BMC Bioinformatics  
, a.k.a. the maximum likelihood parameter estimates (MLEs).  ...  , a.k.a. the maximum likelihood parameter estimates (MLEs).  ...  Acknowledgements We thank Matthew Wheeler for useful suggestions and comments on this work.  ... 
doi:10.1186/1471-2105-13-68 pmid:22548918 pmcid:PMC3496601 fatcat:twpuruxr2zbdrcnjdhrl6zpmw4

Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators

Richard M. Jiang, Fredrik Wrede, Prashant Singh, Andreas Hellander, Linda R. Petzold
2021 BMC Bioinformatics  
Conclusions We provide a novel algorithm for accelerating the construction of summary statistics for stochastic biochemical systems.  ...  This can immediately be implemented to increase the overall speed of the ABC workflow for estimating parameters in complex systems.  ...  Brian Drawert (University of North Carolina at Asheville) for helpful comments.  ... 
doi:10.1186/s12859-021-04255-9 pmid:34162329 fatcat:ivzktn5y4nh6vdqo3lva2aiuxe

GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation [article]

Evgeny Tankhilevich, Jonathan Ish-Horowicz, Tara Hameed, Elisabeth Roesch, Istvan Kleijn, Michael PH Stumpf, Fei He
2019 biorxiv/medrxiv   pre-print
It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable.  ...  We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using i) standard rejection ABC or ABC-SMC, or ii) ABC with Gaussian  ...  Acknowledgements We thank the members of the Theoretical Systems Biology Group at Imperial College London for helpful discussions and enthusiastic support.  ... 
doi:10.1101/769299 fatcat:g4lhwq647bf4jhfefemlqfxpsi

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  
Author Summary In this manuscript, we introduce efficient methods for parameter estimation for stochastic processes.  ...  Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems.  ...  Optimization yielded the maximum likelihood estimates for the parameters of the biochemical process. Due to limited and noise corrupted data, these maximum likelihood estimates are often unreliable.  ... 
doi:10.1371/journal.pcbi.1005030 pmid:27447730 pmcid:PMC4957800 fatcat:y5sor4k225bypfm7gqaw66d75a

Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

Andreas Raue, Marcel Schilling, Julie Bachmann, Andrew Matteson, Max Schelke, Daniel Kaschek, Sabine Hug, Clemens Kreutz, Brian D. Harms, Fabian J. Theis, Ursula Klingmüller, Jens Timmer (+1 others)
2013 PLoS ONE  
For the estimation of unknown model parameters, the performance of different optimization algorithms was compared systematically.  ...  A Systems Biology approach that combines quantitative experimental data with dynamic mathematical modeling promises to yield deeper insights into these processes.  ...  Acknowledgments We thank Annette Schneider for critically reading the manuscript. Author Contributions  ... 
doi:10.1371/journal.pone.0074335 pmid:24098642 pmcid:PMC3787051 fatcat:nqbpyt5nsra3djvbdoopneawwe

Inference for Ecological Dynamical Systems: A Case Study of Two Endemic Diseases

Daniel A. Vasco
2012 Computational and Mathematical Methods in Medicine  
Parameter inference for a perfectly sampled open Markovian SIR is first considered. Next inference for an imperfectly observed sample path of the system is studied.  ...  A Bayesian Markov chain Monte Carlo method is used to infer parameters for an open stochastic epidemiological model: the Markovian susceptible-infected-recovered (SIR) model, which is suitable for modeling  ...  The author would like to thank Helen Wearing and Pej Rohani for comments and suggestions on earlier versions of this paper.  ... 
doi:10.1155/2012/390694 pmid:22536295 pmcid:PMC3318217 fatcat:7xygpdv4s5blnltrg4bewxn6sy

Accelerating computational Bayesian inference for stochastic biochemical reaction network models using multilevel Monte Carlo sampling [article]

David J Warne, Ruth E Baker, Matthew J Simpson
2016 bioRxiv   pre-print
Investigating the behavior of stochastic models of biochemical reaction networks generally relies upon numerical stochastic simulation methods to generate many realizations of the model.  ...  The statistical inference of reaction rate parameters based on observed data is, however, a significantly greater computational challenge; often relying upon likelihood-free methods such as approximate  ...  We thank Mike Giles for useful advice.  ... 
doi:10.1101/064170 fatcat:55hrrioa4zdtfjuwk7ocaze6zm

Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

David J. Warne, Ruth E. Baker, Matthew J. Simpson
2019 Journal of the Royal Society Interface  
Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena.  ...  Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things.  ...  It is important to note that the sampling algorithms we present are not directly relevant to statistical estimators that are not based on expectations, such as maximum-likelihood estimators or the maximum  ... 
doi:10.1098/rsif.2018.0943 pmid:30958205 pmcid:PMC6408336 fatcat:btidqocrdrfftiwv3zwhhavxni

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

Maria Rodriguez-Fernandez, Jose A Egea, Julio R Banga
2006 BMC Bioinformatics  
Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas.  ...  We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems.  ...  Maximum likelihood estimation consists of maximizing the so-called likelihood function, J ml , which is the probability density of a model for the occurrence of the measurements for given parameters.  ... 
doi:10.1186/1471-2105-7-483 pmid:17081289 pmcid:PMC1654195 fatcat:3t5nabagm5gjvl4ezayckmvnii

Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory

Yannis Pantazis, Markos A Katsoulakis, Dionisios G Vlachos
2013 BMC Bioinformatics  
Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks.  ...  Results: We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters.  ...  This observation has been already pointed out and discussed in the context of maximum likelihood estimation for the complete-data case ( [40] , Sec. 10.2).  ... 
doi:10.1186/1471-2105-14-311 pmid:24148216 pmcid:PMC4015035 fatcat:pzcqd3parze7djy73ir6h3gra4

Parametric Sensitivity Analysis for Biochemical Reaction Networks based on Pathwise Information Theory [article]

Yannis Pantazis and Markos A. Katsoulakis and Dionisios G. Vlachos
2013 arXiv   pre-print
Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks.  ...  We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters.  ...  This observation has been already pointed out and discussed in the context of maximum likelihood estimation for the complete-data case [40, Sec. 10.2] .  ... 
arXiv:1304.3962v2 fatcat:vq2phwokovgexfoxl4477pstsm

Experimental Phylogeny of Neutrally Evolving DNA Sequences Generated by a Bifurcate Series of Nested Polymerase Chain Reactions

Gerdine F. O. Sanson, Silvia Y. Kawashita, Adriana Brunstein, Marcelo R. S. Briones
2002 Molecular biology and evolution  
These results provide for the first time biochemical experimental support for phylogenies, divergence date estimates, and an irreversible substitution model based on neutrally evolving DNA sequences.  ...  Parsimony, distance, and maximum likelihood analysis of the terminal sequences reconstructed the topology of the real phylogeny and branch lengths accurately.  ...  Perez, Scientific Director of Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for sequencing equipment made available to us through the ONSA Brazilian sequencing network, and the excellent  ... 
doi:10.1093/oxfordjournals.molbev.a004069 pmid:11801745 fatcat:na77vullifecpi4lqk5wtq45v4

Classic and contemporary approaches to modeling biochemical reactions

W. W. Chen, M. Niepel, P. K. Sorger
2010 Genes & Development  
We discuss how parameters in the Michaelis-Menten approximation and in the underlying ODE network  ...  Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation.  ...  The exponent of obj(k f ,k r ,k cat ) is a x 2 error function that returns maximum likelihood estimates, and thus parameter estimation will return likelihood distributions for the rate constants.  ... 
doi:10.1101/gad.1945410 pmid:20810646 pmcid:PMC2932968 fatcat:d6nhsqygsrgpxok26lyq2g5xry

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
Model dynamics are crucially dependent on parameter values which are often estimated from observations.  ...  Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably.  ...  Accelerated maximum likelihood estimation for stochastic biochemical systems. BMC Bioinf. 13. doi:10. 1186/1471-2105-13-68 Karimi, H. and Mcauley, K. B. (2013).  ... 
arXiv:1902.05828v1 fatcat:sjb4jm53a5h3jf2swbiz7vk6hu

Pathways of Maximum Likelihood for Rare Events in Nonequilibrium Systems: Application to Nucleation in the Presence of Shear

Matthias Heymann, Eric Vanden-Eijnden
2008 Physical Review Letters  
Even in nonequilibrium systems, the mechanism of rare reactive events caused by small random noise is predictable because they occur with high probability via their maximum likelihood path (MLP).  ...  It then becomes important to identify the maximum likelihood path (MLP) of the rare event, i.e., the path which maximizes the event likelihood over all possible pathways and times this event can take to  ...  We call this path the path of maximum likelihood for the transition.  ... 
doi:10.1103/physrevlett.100.140601 pmid:18518017 fatcat:glm3c6jyqnc2rjywoypvohs6sm
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