Fig 1.
The polymorphic Gompertzian model’s fit to in vitro data.
Fit of the polymorphic Gompertzian model to in vitro dynamics of sensitive, resistant and total cancer cell populations across four experimental conditions and different initial ratios of sensitive cells. Each graph displays the measured cell counts (crosses) and the model fit (lines) to one well. Sensitive, resistant, and total populations are colored in green, orange, and blue, respectively. For each well measured proportion of sensitive cells at time point t3 = 12h (start of the model’s fit) is stated above the graph. Columns represent experimental conditions (presence or absence of drug and CAF). Rows contain wells in different conditions with similar initial proportions of sensitive cells.
Fig 2.
Mean Absolute Percentage Error (MAPE) of the polymorphic Gompertzian model’s fit to in vitro data.
MAPEs of the model’s fit to dynamics of sensitive, resistant and total cancer cell populations are presented for a range of initial proportion of sensitive cells. Data points are colored according to experimental conditions of the well (presence or absence of drug and CAF).
Fig 3.
Distribution of the polymorphic Gompertzian model’s parameters across in vitro wells.
Parameters of the polymorphic Gompertzian model fitted to in vitro data across four experimental conditions (indicated in columns) and range of initial proportions of sensitive cells. Each dot in the graphs corresponds to the parameter value of the model fitted to dynamics from one well. The orange line indicates the mean value of treatment sensitivity λ in a given experimental condition.
Fig 4.
The fit of the polymorphic Gompertzian model to in vivo data across five trend categories.
Each row shows five patient cases corresponding to one of the trend categories: “Growth”, “Decline”, “Delayed response”, “U-shape” or “Fluctuate”. Measured tumor volume over time is marked with orange crosses. The blue line represents the model’s fit to the total tumor size dynamics, green and orange lines represent predicted dynamics of sensitive and resistant populations sizes respectively.
Table 1.
Comparison of the models’ fit errors in trend categories.
The table presents p-values of t-tests between two groups of nMSE. Bold text indicates statistically significant p-values.