simulate | R Documentation |

simulate function uses the change in expression value/s as triggering.

```
simulate(input_graph, cycle = 1, threshold = 0, knockdown = TRUE)
```

`input_graph` |
The graph object that processed in previous steps. |

`cycle` |
Optimal iteration number for gaining steady-state. |

`threshold` |
absolute minimum amount of change required to be considered as up/down regulated element |

`knockdown` |
specifies gene knockdown with default TRUE |

The steady-state conditions of the system are disturbed after the change in the graph (with update_how or update_variables). In this case, the system tend to be steady state again. The arrangement of competetive profiles of the targets continue until all nodes are updated and steady-state nearly. Note that, If 'how' argument is specified as '0', *simulate()* and *update_how()* functions process the variables to knockdown of specified gene with default 'knockdown = TRUE' and knocked down competing RNA is kept at zero. However, if 'knockdown= FALSE' argument is applied, competing RNA which has initial expression level of zero is allowed to increase or fluctuate during calculations.

The graph.

```
data('minsamp')
data('new_counts')
## new_counts, the dataset that includes the current counts of nodes.
priming_graph(minsamp, Competing_expression, miRNA_expression)%>%
update_variables(new_counts)%>%
simulate()
priming_graph(minsamp, Competing_expression, miRNA_expression,
aff_factor = c(seed_type,energy), deg_factor = region)%>%
update_variables(new_counts)%>%
simulate(cycle = 3)
```

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