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Updated: Jul 18, 2026

Following Cell-fate in E. coli After Infection by Phage Lambda

Following Cell-fate in E. coli After Infection by Phage Lambda

Published on: October 14, 2011

Knowledge-based simulation of genetic regulation in bacteriophage lambda.

S Meyers, P Friedland

Nucleic Acids Research

|January 11, 1984

Summary

This study introduces a knowledge-based computer program for simulating regulatory genetics. The tool accurately models the Bacteriophage Lambda

Area of Science:

Computational Biology
Genetics
Bioinformatics

Background:

Regulatory genetics governs gene expression.
Simulating complex genetic networks is crucial for understanding biological systems.
Existing simulation tools may lack flexibility or knowledge-based reasoning.

Purpose of the Study:

To develop a general-purpose, knowledge-based computer program for functional simulation of regulatory genetics.
To describe the simulator's architecture and advantages of its knowledge-based design.
To evaluate the simulator's performance using a specific biological case study.

Main Methods:

Developed a knowledge-based simulator using the Unit System for knowledge acquisition, representation, and manipulation.
Designed a hierarchical knowledge structure for the simulation.

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Understanding the Impact of Temperate Bacteriophages on Their Lysogens Through Transcriptomics

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Last Updated: Jul 18, 2026

Following Cell-fate in E. coli After Infection by Phage Lambda

Following Cell-fate in E. coli After Infection by Phage Lambda

Published on: October 14, 2011

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Published on: May 26, 2013

Understanding the Impact of Temperate Bacteriophages on Their Lysogens Through Transcriptomics

Understanding the Impact of Temperate Bacteriophages on Their Lysogens Through Transcriptomics

Published on: January 5, 2024

Applied the simulator to model the decision-making process in Bacteriophage Lambda's lytic and lysogenic growth.

Main Results:

The developed simulator is knowledge-based and utilizes a hierarchical structure.
The Unit System facilitates efficient knowledge management.
The simulator successfully modeled the Bacteriophage Lambda growth decision.

Conclusions:

A versatile, knowledge-based simulation program for regulatory genetics has been created.
The simulator's architecture supports efficient and accurate modeling of genetic regulatory networks.
This tool provides a valuable platform for studying genetic control mechanisms, exemplified by Bacteriophage Lambda.