simulate {ceRNAnetsim} | R Documentation

## Utilizes the change in expression value/s as triggering.

### Description

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

### Usage

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

### Arguments

`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

### Details

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.

### Value

The graph.

### Examples

    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)

[Package ceRNAnetsim version 1.0.0 Index]