A stochastic vs deterministic perspective on the timing of cellular events

Autor: Lucy Ham, Megan A. Coomer, Kaan Öcal, Ramon Grima, Michael P. H. Stumpf

Publikováno v: Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)

Abstract Cells are the fundamental units of life, and like all life forms, they change over time. Changes in cell state are driven by molecular processes; of these many are initiated when molecule numbers reach and exceed specific thresholds, a chara

Externí odkaz: https://doaj.org/article/a6d274e70b7c42eeb9bb212309241b86

Training deep neural density estimators to identify mechanistic models of neural dynamics

Autor: Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, David S Greenberg, Jakob H Macke

Publikováno v: eLife, Vol 9 (2020)

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challen

Externí odkaz: https://doaj.org/article/fe7139bde6d64681b1cc8eb1344b5903

Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models

Autor: Kaan Öcal, Michael U. Gutmann, Guido Sanguinetti, Ramon Grima

Publikováno v: Öcal, K, Gutmann, M U, Sanguinetti, G & Grima, R 2022, ' Inference and Uncertainty Quantification of Stochastic Gene Expression via Synthetic Models ', Journal of the Royal Society. Interface, vol. 19, no. 192, 20220153 . https://doi.org/10.1098/rsif.2022.0153

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15203e84febbfa0bda70a62b222f1382
https://doi.org/10.1101/2022.01.25.477666

Training deep neural density estimators to identify mechanistic models of neural dynamics

Autor: Sara A Haddad, Marcel Nonnenmacher, Tim P. Vogels, Jakob H. Macke, William F Podlaski, Chaitanya Chintaluri, Michael Deistler, David S. Greenberg, Pedro J. Gonçalves, Jan-Matthis Lueckmann, Giacomo Bassetto, Kaan Öcal

Publikováno v: Gonçalves, P.J.; Lueckmann, J.-M.; Deistler, M.; Nonnenmacher, M.; Öcal, K.; Bassetto, G.; Chintaluri, C.; Podlaski, W.F.; Haddad, S.A.; Vogels, T.P.; Greenberg, D.S.; Macke, J.H.: Training deep neural density estimators to identify mechanistic models of neural dynamics. In: eLife. Vol. 9 (2020) e56261. (DOI: /10.7554/eLife.56261)
eLife
bioRxiv : the preprint server for biology
eLife, Vol 9 (2020)

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e10da516d611f90ab9af20407e9c3e65
https://publications.hereon.de/id/50573

Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks

Autor: Kaan Öcal, Guido Sanguinetti, Ramon Grima

Publikováno v: Computational Methods in Systems Biology ISBN: 9783030313036
CMSB

Modern experimental methods such as flow cytometry and fluorescence in-situ hybridization (FISH) allow the measurement of cell-by-cell molecule numbers for RNA, proteins and other substances for large numbers of cells at a time, opening up new possib

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::257fb59bb5acbb0a697e57c7ad6f8c27
https://doi.org/10.1007/978-3-030-31304-3_24