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Perspective: Stochastic algorithms for chemical kinetics

The Journal of chemical physics, 2013-05, Vol.138 (17), p.170901

2013

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Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion

PLoS computational biology, 2016-11, Vol.12 (11), p.e1005122-e1005122

2016

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Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

PLoS computational biology, 2016-12, Vol.12 (12), p.e1005220-e1005220

2016

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Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development

Nature neuroscience, 2018-12, Vol.21 (12), p.1784-1792

2018

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Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Nature communications, 2022-07, Vol.13 (1), p.4403-4403, Article 4403

2022

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Mechanical feedback coordinates cell wall expansion and assembly in yeast mating morphogenesis

PLoS computational biology, 2018-01, Vol.14 (1), p.e1005940-e1005940

2018

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The effect of cell geometry on polarization in budding yeast

PLoS computational biology, 2018-06, Vol.14 (6), p.e1006241-e1006241

2018

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Coordinating cell polarization and morphogenesis through mechanical feedback

PLoS computational biology, 2021-01, Vol.17 (1), p.e1007971-e1007971

2021

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Computational model of tranexamic acid on urokinase mediated fibrinolysis

PloS one, 2020-05, Vol.15 (5), p.e0233640-e0233640

2020

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Ontogeny of Circadian Rhythms and Synchrony in the Suprachiasmatic Nucleus

The Journal of neuroscience, 2018-02, Vol.38 (6), p.1326-1334

2018

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Efficient step size selection for the tau-leaping simulation method

The Journal of chemical physics, 2006-01, Vol.124 (4), p.044109-044109-11

2006

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Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

NPJ digital medicine, 2019-11, Vol.2 (1), p.115-115, Article 115

2019

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Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods

PLoS computational biology, 2022-01, Vol.18 (1), p.e1009830

2022

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Reaction rates for reaction-diffusion kinetics on unstructured meshes

The Journal of chemical physics, 2017-02, Vol.146 (6), p.064101-064101

2017

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The slow-scale stochastic simulation algorithm

The Journal of chemical physics, 2005-01, Vol.122 (1), p.14116-14116

2005

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Control over single-cell distribution of G1 lengths by WNT governs pluripotency

PLoS biology, 2019-09, Vol.17 (9), p.e3000453-e3000453

2019

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Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification

Journal of the American Medical Informatics Association : JAMIA, 2018-01, Vol.25 (1), p.99-108

2018

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Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

BMC bioinformatics, 2012-05, Vol.13 (1), p.68-68, Article 68

2012

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neuropeptide speeds circadian entrainment by reducing intercellular synchrony

Proceedings of the National Academy of Sciences - PNAS, 2013-11, Vol.110 (46), p.E4355-E4361

2013

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A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection–diffusion–reaction problems

Journal of computational physics, 2019-02, Vol.378, p.1-17

2019

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Functional network inference of the suprachiasmatic nucleus

Proceedings of the National Academy of Sciences - PNAS, 2016-04, Vol.113 (16), p.4512-4517

2016

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

BMC bioinformatics, 2021-06, Vol.22 (1), p.339-17, Article 339

2021

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Identification of influential proteins in the classical retinoic acid signaling pathway

Theoretical biology and medical modelling, 2018-10, Vol.15 (1), p.16-16, Article 16

2018

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Stochastic Simulation of Biochemical Systems: In Memory of Dan T. Gillespie’s contributions

Bulletin of mathematical biology, 2019-08, Vol.81 (8), p.2819-2821

2019

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On the Inference of Functional Circadian Networks Using Granger Causality

PloS one, 2015-09, Vol.10 (9), p.e0137540-e0137540

2015

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Perspective: Stochastic algorithms for chemical kinetics

Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development

Functional neuronal circuitry and oscillatory dynamics in human brain organoids

Mechanical feedback coordinates cell wall expansion and assembly in yeast mating morphogenesis

The effect of cell geometry on polarization in budding yeast

Coordinating cell polarization and morphogenesis through mechanical feedback

Computational model of tranexamic acid on urokinase mediated fibrinolysis

Ontogeny of Circadian Rhythms and Synchrony in the Suprachiasmatic Nucleus

Efficient step size selection for the tau-leaping simulation method

Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods

Reaction rates for reaction-diffusion kinetics on unstructured meshes

The slow-scale stochastic simulation algorithm

Control over single-cell distribution of G1 lengths by WNT governs pluripotency

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

neuropeptide speeds circadian entrainment by reducing intercellular synchrony

A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection–diffusion–reaction problems

Functional network inference of the suprachiasmatic nucleus

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

Identification of influential proteins in the classical retinoic acid signaling pathway

Stochastic Simulation of Biochemical Systems: In Memory of Dan T. Gillespie’s contributions

On the Inference of Functional Circadian Networks Using Granger Causality

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