1. Introduction | 2. Installation | 3. Integration of dataset which includes only miRNA and gene expression values | 3.1. miRNA:target pairs | 3.2. Gene expression in normal and tumor samples | 3.3. miRNA expression data | 3.4. Integrating and analysing data | 3.5. The sum of two conditions: | 4. Dataset (huge_example) which includes miRNA and gene expressions and miRNA:target interaction factors | 4.1. Description of the huge_example dataset | 4.2. Select a node as trigger | 5. Finding perturbation efficiency on an experimental dataset | 6. Session Info | References

1. Introduction | 2. Installation | 3. Selection of perturbing element from dataset | 3.1. Selection of HIST1H3H gene at vignette How does the system behave in mirtarbase dataset without interaction factors? | 3.2. Selection of ACTB gene at vignette How does the system behave in mirtarbase dataset without interaction factors? | 4. Determination of ACTB gene perturbation efficiency with different expression level changes | 5. Session Info

1. Introduction | 2. Installation | 3. Comparison of gaining steady-state durations of middle and minimal datasets | 3.1. Suggestion for simulation iteration | 3.2. Find appropriate iteration number with find_iteration and then simulate accordingly | 4. What is perturbation efficiency? | 4.1. How does the calc_perturbation() work? | 4.2. A Short-cut: Finding perturbation efficiencies for whole nodes of network | 5. Session Info

1. Introduction | 2. Installation | 3. About the data | 4. Simulation via expression values of miRNAs and genes in minsamp dataset | 4.1. Handle basic dataset | 4.2. Trigger a change | 4.3. Simulate the changes in graph | 4.4. A special case: knockdown | 5. Simulation via interaction factors in addition to expression values of miRNAs and genes in minsamp dataset | 5.1. Change expression level of one or more nodes in the graph | 5.2. Update the node variables with edge variables. | 5.3. Simulate the model | 5.4. Visualisation of the graph | 6. Session Info