Welcome to lifesimulatoR • lifesimulatoR

Welcome to `lifesimulatoR`

lifesimulatoR is an educational R package for exploring origin-of-life concepts using simplified computational simulations.

The package focuses on symbolic molecules, mutation, selection, replication, autocatalytic networks, protocell-like growth, and diversity metrics. It is designed for teaching, visualization, science communication, and conceptual exploration.

The models are intentionally simplified. They are not chemically complete models of abiogenesis. Instead, they provide a clear way to explore mechanisms often discussed in origin-of-life research.

What the package helps explore

lifesimulatoR can be used to ask questions such as:

How can mutation and selection change a molecular population?
What happens when molecules replicate with errors?
How can molecular diversity be summarized?
How might protocell-like compartments grow, leak, and divide?
How can autocatalytic networks create self-reinforcing dynamics?
How do toy models help clarify origin-of-life hypotheses?

Recommended learning path

If you are new to the package, start with the tutorials in this order:

getting-started.Rmd
molecular-evolution.Rmd
diversity-metrics.Rmd
protocells.Rmd
autocatalytic-networks.Rmd

This order moves from the basic package workflow to molecular evolution, diversity metrics, compartmentalization, and network-level emergence.

Concept tutorials

These tutorials focus on scientific ideas and workflows rather than individual functions.

Getting Started

File: getting-started.Rmd

This tutorial introduces the package, its purpose, and the core workflow.

Topics covered:

Loading the package
Creating a prebiotic molecular pool
Running a basic simulation
Plotting results
Exploring protocells and autocatalytic networks

Key functions:

library(lifesimulatoR)

create_prebiotic_pool()
simulate_abiogenesis()
plot_simulation()
protocell_simulation()
autocatalytic_network()

Molecular Evolution

File: molecular-evolution.Rmd

This tutorial explains how symbolic molecular populations can mutate, replicate, and undergo selection.

Topics covered:

Molecular populations
Symbolic RNA-like sequences
Mutation
Replication
Selection
Fitness landscapes
Multi-generation simulation

Key functions:

create_prebiotic_pool()
molecule_fitness()
replicate_molecules()
mutate_sequence()
mutate_population()
evolve_generation()
simulate_abiogenesis()
plot_simulation()

Diversity Metrics

File: diversity-metrics.Rmd

This tutorial explains how to summarize and interpret molecular diversity.

Topics covered:

Molecular diversity
Shannon entropy
Population summaries
Mutation-selection balance
Diversity versus complexity

Key functions:

summarize_molecules()
shannon_entropy()
simulate_abiogenesis()
plot_simulation()

Protocells

File: protocells.Rmd

This tutorial introduces simplified protocell-like compartments.

Topics covered:

Primitive compartmentalization
Growth
Leakage
Division
Population dynamics
Protocells as possible units of selection

Key functions:

protocell_simulation()
plot_simulation()

Autocatalytic Networks

File: autocatalytic-networks.Rmd

This tutorial introduces chemical networks where molecules help produce other molecules.

Topics covered:

Catalysis
Reaction networks
Feedback loops
Self-sustaining systems
Emergence of system-level behaviour

Key functions:

autocatalytic_network()
plot_simulation()

Function help pages

Individual function examples should live in the roxygen documentation inside the R/ files, not as separate small vignettes. This keeps the vignette folder focused on larger conceptual tutorials.

After adding roxygen comments and running:

devtools::document()

users can access function help pages such as:

?simulate_abiogenesis
?protocell_simulation
?autocatalytic_network
?create_prebiotic_pool

Main functions

Function	Purpose
`create_prebiotic_pool()`	Generate an initial symbolic molecular population
`molecule_fitness()`	Estimate simple toy fitness values for molecules
`replicate_molecules()`	Replicate molecules based on fitness or abundance
`mutate_sequence()`	Mutate one molecular sequence
`mutate_population()`	Mutate a population of molecules
`evolve_generation()`	Run one evolutionary generation
`simulate_abiogenesis()`	Run a multi-generation molecular evolution simulation
`shannon_entropy()`	Calculate diversity or uncertainty in a population
`summarize_molecules()`	Summarize molecular populations
`protocell_simulation()`	Simulate protocell-like growth, leakage, and division
`autocatalytic_network()`	Simulate a toy autocatalytic network
`plot_simulation()`	Plot simulation outputs

Scientific background

The package draws inspiration from several major origin-of-life research themes:

Prebiotic chemistry
Molecular self-replication
Darwinian evolution
RNA-world hypotheses
Autocatalytic set theory
Protocell models
Complexity and emergence
Mutation-selection balance
Compartmentalization
Network-first and metabolism-first perspectives

The simulations are intentionally simplified and should be viewed as educational tools rather than mechanistic models of abiogenesis.

Suggested use in teaching

lifesimulatoR can support classroom or workshop discussions by allowing users to modify parameters and observe outcomes.

Example teaching questions:

What happens when mutation rate increases?
What happens when selection becomes stronger?
What happens when leakage is too high in protocells?
What happens when catalytic networks become more connected?
Can a system become more organized while becoming less diverse?
What assumptions are hidden inside each model?

Future development

Potential future modules include:

RNA-world simulations
Lipid vesicle formation
Metabolism-first models
Hydrothermal vent environments
Energy-gradient simulations
Reaction-diffusion systems
Information-theoretic complexity metrics
Interactive Shiny applications
Animated visualizations
Comparative origin-of-life frameworks

Citation

If you use lifesimulatoR in teaching materials, presentations, workshops, or research projects, please cite the package and provide a link to the GitHub repository.

License

MIT License