Amoeba Life Cycle Simulation

This Node.js module simulates the life cycle of an amoeba at the molecular level, including DNA replication, RNA transcription, and protein translation.

Usage

To use the module, first install it with npm:

npm install amoeba-life-cycle

Then, you can use the following code to simulate the life cycle of an amoeba:

const amoeba = require('amoeba-life-cycle');

// Generate a random DNA sequence let dna = amoeba.generateDNA(30); console.log(DNA: ${dna});

// Replicate the DNA let replicatedDNA = amoeba.replicateDNA(dna); console.log(Replicated DNA: ${replicatedDNA});

// Transcribe the DNA into RNA let rna = amoeba.transcribeDNA(dna); console.log(RNA: ${rna});

// Translate the RNA into a protein let protein = amoeba.translateRNA(rna); console.log(Protein: ${protein});

You can also simulate the entire life cycle of an amoeba with a single function call:

amoeba.simulateAmoebaLifeCycle();

This will generate a random DNA sequence, replicate it, transcribe it into RNA, and translate the RNA into a protein.

Installation

To install the module, you can use npm:

npm install amoeba-life-cycle

This module has a dependency on the `trng2` module for generating random numbers.

## Author

This module was created by Harshad Joshi (GitHub user: hj91).

Application

This simulation could be used in a variety of contexts, including:

1. Education: This could be a useful tool for teaching students about the basics of molecular biology, including DNA replication, RNA transcription, and protein translation. It could be used in a classroom setting, or as part of an online course or tutorial.

2. Research: Researchers in bioinformatics or computational biology might use a more advanced version of this simulation to model biological processes and generate hypotheses for further study.

3. Software Development: This simulation could be incorporated into a larger software application or tool for bioinformatics, such as a DNA sequence analyzer or a genetic data visualization tool.

4. Genetic Engineering: In the field of genetic engineering, simulations like this could be used to predict the outcomes of genetic modifications.