Driver-d model

We then modified the driver model to create the “driver-d” model, where each cell divides with a constant probability g0 and dies with a probability d. Moreover, we assumed that cell death occurs only in the case of p > 1, to prevent the simulation from halting before clonal expansion. In each cell division, each of the two daughter cells acquires kd ∼ Pois(md/2) driver mutations and can accumulate Nd driver mutations at maximum. Each driver mutation decreases the cell death probability by e fold: d = d0end, where d0 is a cell base death probability and nd is the number of driver mutations accumulated in a cell.

Information of variables and parameters are listed in Tables 1 and 2. In MASSIVE, we converted md, e and P to log scale, i.e., md' = log10 md', e' = log10 e and P' = log10 P, and then tested every combination of md' ∈ {-4, -3.5, -3,...,-1}, md ∈ {1, 2, 3, 4}, e' ∈ {0.1, 0.15, 0.2,...,1.0} and P' ∈ {3, 4, 5, 6}. All results are explorable in the focused and comparative view modes of the MASSIVE viewer.

Table 1. a list of the variabes

symbol description
kd number of driver mutations obtained in a cell division
nd number of driver mutations accumulated in a cell
p population size
t number of time steps
d cell death probability

Table 1. a list of the parameters

symbol description value
md expected number of driver mutations generated per cell division {10-4, 10-3.5, 10-3,...,10-1}
Nd maximum number of driver mutations accumulated in a cell {1, 2, 3, 4}
e decrease of the cell death probability per driver mutation {100.1, 100.15, 100.2,...,101.0}
g0 base cell division probability 10-2
d0 base cell death probability 10-2
P maximum population size {103, 104, 105, 106}
T maximum number of time steps 106