  WT simulations  KO Simulations 

Parameter  Changepoint sensitivity  Changepoint PPV  Edges sensitivity  Edges PPV  Changepoint sensitivity  Changepoint PPV  Edges sensitivity  Edges PPV 


0.2
 94.2  95.1  73.9  99.2  100  100  100  98.5 

0.4
 90.8  94.1  79.3  97.9  100  99  100  98.2 

0.6
 87.8  92.5  73.9  96.9  96  97.1  99.2  95.4 

0.8
 75.4  96.3  78.8  96.4  81.4  97.8  97.6  91.4 
Noise

1
 80.5  96.6  74.7  97.5  69.1  95  95.1  88.8 

1.2
 71.7  96.2  78.4  97.6  28.1  82.4  97.2  87.6 

1.4
 58.7  94.6  79  95.9  23.6  89.7  92.7  87.5 

1.6
 52.9  91.8  74.4  97.2  10.8  75.9  79.1  86.8 

1.8
 60.8  94.5  76  95.6  4.8  81.8  76.8  85.6 

1
 78.5  97.5  1.3  100  79  98.8  18.1  82.4 

2
 92  92  24.7  98.5  97  96.5  98.7  92.5 
Phase size

3
 90  94.2  50.8  98.6  99.5  99  100  93.6 

4
 94.5  94  74.5  98.6  99.5  99.5  100  96.4 

5
 96  99  76.9  96.8  99.5  97.5  100  94.7 

12
 100  99  92.6  98  99  97.1  100  97.8 

5
 93.7  95.5  82.4  99.1  _  _  _  _ 
# of parent

10
 81.1  88.3  69.7  96.4  _  _  _  _ 
genes

20
 62.4  83.4  51.2  97.3  _  _  _  _ 

40
 54  77.2  33.1  96.4  _  _  _  _ 
 To evaluate ARTIVA performances, two types of data were used: WT simulations corresponding to timeseries expression data with no knowledge of potential transcription factors and KO simulations corresponding to several timeseries expression data of a simulated wildtype strain and knockout strains for each known transcription factor (see Methods and Additional file 2 for more details). The default values of the parameters used for the simulation study are: # of timepoints n = 12; # of changepoints k~\mathcal{U}(\{1,\mathrm{..},n/4\}); maximal # of edges = 5; parent to target coefficient ~\mathcal{U}([2,0.1]\cup [0.1,2]); phase sizes ~\mathcal{U}(\{3,\mathrm{..},n\});\# # of replicates r = 8 for WT simulations, r = 4 for KO simulations; noise standard deviation ~\mathcal{N}(0,0.5); total # of simulations = 200 for each condition. In this table, the value of noise intensity, phase size and number of parent genes change according to the parameter under study (all other parameters were set to default). In each condition, the ability of ARTIVA to detect all true phase changepoints and model edges (Sensitivity) and to detect only true positives (Positive Predictive Value, PPV) was calculated. Overall, this simulation study allows us to gain confidence in ARTIVA results (≃ 80% of PPV and sensitivity) for a given set of parameters (noise standard error is on the order of the mean value of the regression coefficients, number of measurements in a regulatory phase > 8 and less than 20 parent genes).