Related Experiment Video
Updated: Jul 11, 2026

Single-cell Microfluidic Analysis of Bacillus subtilis
Published on: January 26, 2018
Probabilistic neural networks using Bayesian decision strategies and a modified Gompertz model for growth phase
1Department of Chemical and Bioresource Engineering, Colorado State University, 100 Glover Building, 80523, Fort Collins, CO, USA
Abstract:
Probabilistic neural networks (PNNs) were used in conjunction with the Gompertz model for bacterial growth to classify the lag, logarithmic, and stationary phases in a batch process. Using the fermentation time and the optical density of diluted cell suspensions, sampled from a culture of Bacillus subtilis, PNNs enabled a reliable determination of the growth phases. Based on a Bayesian decision strategy, the Gompertz based PNN used newly proposed definition of the lag and logarithmic phases to estimate the latent, logarithmic and stationary phases. This network topology has the potential for use with on-line turbidimeter for the automation and control of cultivation processes.
More Related Videos
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
08:04Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
Published on: June 6, 2025
Related Concept Videos
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Bacterial Growth Curve
Exponential Growth
Batch vs Continuous Culture
Fed-Batch Culture