Simulate a Toy Autocatalytic Network

Creates a random network of molecular types where some molecules catalyze the production of others. This simplified model is useful for visualizing how mutually reinforcing chemical systems may grow.

Creates a random network of molecular types where some molecules catalyze the production of others. This simplified model is useful for visualizing how mutually reinforcing chemical systems may grow.

Usage

autocatalytic_network(
  n_types = 10,
  steps = 100,
  catalysis_probability = 0.15,
  base_rate = 0.05,
  catalytic_boost = 0.1,
  decay_rate = 0.02,
  seed = NULL
)

autocatalytic_network(
  n_types = 10,
  steps = 100,
  catalysis_probability = 0.15,
  base_rate = 0.05,
  catalytic_boost = 0.1,
  decay_rate = 0.02,
  seed = NULL
)

Arguments

n_types

Number of molecular types.

steps

Number of simulation steps.

catalysis_probability

Probability that one molecule type catalyzes another.

base_rate

Baseline production rate.

catalytic_boost

Additional production rate from catalysts.

decay_rate

Fraction of each molecule type lost per step.

seed

Optional random seed.

Value

A list containing a time-series tibble and the catalysis matrix.

A list containing a time-series tibble and the catalysis matrix.

Examples

net <- autocatalytic_network(n_types = 5, steps = 20, seed = 2)
head(net$time_series)
#> # A tibble: 6 × 3
#>    step molecule abundance
#>   <int> <chr>        <dbl>
#> 1     0 M1           0.540
#> 2     0 M2           0.234
#> 3     0 M3           0.421
#> 4     0 M4           0.966
#> 5     0 M5           0.219
#> 6     1 M1           0.579
net <- autocatalytic_network(n_types = 5, steps = 20, seed = 2)
head(net$time_series)
#> # A tibble: 6 × 3
#>    step molecule abundance
#>   <int> <chr>        <dbl>
#> 1     0 M1           0.540
#> 2     0 M2           0.234
#> 3     0 M3           0.421
#> 4     0 M4           0.966
#> 5     0 M5           0.219
#> 6     1 M1           0.579