Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Estimating single-cell lag times via a Bayesian scheme.

P K Malakar¹, G C Barker

¹Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom. pradeep.malakar@bbsrc.ac.uk

Applied and Environmental Microbiology

|September 23, 2008

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Quantifying the uncertainty of transfer of Cronobacter spp. between fomites and floors and touch points in dairy processing plants.

Food microbiology·2019

Same author

Machine learning to refine decision making within a syndromic surveillance service.

BMC public health·2019

Same author

Climate Change, Foodborne Pathogens and Illness in Higher-Income Countries.

Current environmental health reports·2018

Same author

Management of Gastroschisis in a Peripheral Hospital.

Medical journal, Armed Forces India·2016

Same author

The pattern of growth observed for Clostridium botulinum type A1 strain ATCC 19397 is influenced by nutritional status and quorum sensing: a modelling perspective.

Pathogens and disease·2015

Same author

New Elements To Consider When Modeling the Hazards Associated with Botulinum Neurotoxin in Food.

Journal of bacteriology·2015

Network models provide efficient tools for analyzing single-cell lag phases. A new Bayesian network model overcomes technical challenges with uncertain inocula, improving variability estimation.

Area of Science:

Computational biology
Microbiology
Statistical modeling

Background:

Estimating single-cell lag phase variability is crucial for understanding microbial population dynamics.
Current optical methods are technically challenging due to requirements for single-cell inocula.

Purpose of the Study:

To develop a computationally efficient and technically feasible method for estimating single-cell lag phase variability.
To address limitations of existing optical methods by incorporating uncertain inocula.

Main Methods:

Development of a Bayesian network model.
Incorporation of small, uncertain inocula into the network model.
Application of the model for estimating single-cell lag phase variability.

More Related Videos

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Related Experiment Videos

Last Updated: Jun 30, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Main Results:

The Bayesian network model offers a computationally efficient approach.
The model successfully estimates single-cell lag phase variability even with uncertain inocula.
This method overcomes the technical challenges associated with single-cell inocula.

Conclusions:

Bayesian network models provide a viable alternative for analyzing single-cell lag phases.
The proposed model enhances the estimation of lag phase variability in microbial studies.
This approach simplifies experimental requirements and improves computational efficiency.