Contribution to a conference proceedings/Contribution to a book

FZJ-2024-06730

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth

Paul, R. D. (First author)FZJ* ; Seiffarth, J.FZJ* ; Scharr, H.FZJ* ; Nöh, K. (Corresponding author)FZJ*

2024
IEEE
ISBN: 979-8-3503-1333-8

This record in other databases:  

Please use a persistent id in citations: doi:10.1109/ISBI56570.2024.10635267

Abstract: Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.

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Contributing Institute(s):

1. Datenanalyse und Maschinenlernen (IAS-8)
2. Biotechnologie (IBG-1)

Research Program(s):

1. 2171 - Biological and environmental resources for sustainable use (POF4-217) (POF4-217)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2024

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