Application of MPBNs to the Tumour invasion model by Cohen et al. 2015#
We use Most Permissive Boolean Networks to analyse the Boolean model published in (Cohen et al., 2015) which shows cell fate decision processes related to tumour development.
import ginsim
from colomoto_jupyter import tabulate
import mpbn
Model setup#
Original model#
lrg = ginsim.load("http://ginsim.org/sites/default/files/SuppMat_Model_Master_Model.zginml")
ginsim.show(lrg)
Downloading http://ginsim.org/sites/default/files/SuppMat_Model_Master_Model.zginml
Conversion to MPBN#
bn = ginsim.to_minibn(lrg)
mbn = mpbn.MPBooleanNetwork(bn)
len(mbn) # display number of nodes
32
Input and output nodes#
inputs = ["DNAdamage", "ECMicroenv"]
outputs = ["Apoptosis","CellCycleArrest","EMT","Invasion","Migration","Metastasis"]
Analysis of reachable attractors#
Complete listing of attractors#
Let us first compute the full list of attractors of the MPBN.
%time a = list(mbn.attractors())
tabulate(a, columns=outputs)
CPU times: user 9.13 ms, sys: 1.07 ms, total: 10.2 ms
Wall time: 9.39 ms
| Apoptosis | CellCycleArrest | EMT | Invasion | Migration | Metastasis | |
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| 2 | 0 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 1 | 0 | 0 | 0 | 0 |
Remark they are all fixed points and thus match exactly with the attractors using asynchronous BNs.
Rechable attractors#
Starting from all nodes but inputs to 0, we are interested in the set of reachable attractors, whatever the value of input nodes.
initial_condition = dict([(n,0) for n in bn if n not in inputs])
Wild-type#
Let us first compute the reachable attractors in the wild type model. They match with the full attract listing.
%time a = list(mbn.attractors(reachable_from=initial_condition))
tabulate(a, columns=outputs)
CPU times: user 20.9 ms, sys: 1.01 ms, total: 21.9 ms
Wall time: 20.6 ms
| Apoptosis | CellCycleArrest | EMT | Invasion | Migration | Metastasis | |
|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 0 | 1 | 1 | 0 | 0 | 0 |
| 2 | 0 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 1 | 0 | 0 | 0 | 0 |
Notch GoF mutant#
Now, we apply the Notch gain of function, and predict, as MaBoSS with asynchronous BNs, the loss of apoptotic attractor reachability.
mbn_mut = mbn.copy()
mbn_mut["NICD"] = 1
%time a = list(mbn_mut.attractors(reachable_from=initial_condition))
tabulate(a, columns=outputs)
CPU times: user 21.1 ms, sys: 37 µs, total: 21.2 ms
Wall time: 19.5 ms
| Apoptosis | CellCycleArrest | EMT | Invasion | Migration | Metastasis | |
|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 0 | 0 | 0 |
| 1 | 0 | 1 | 1 | 1 | 0 | 0 |
| 2 | 0 | 1 | 1 | 1 | 1 | 1 |
Notch GoF/p53 LoF double mutant#
Adding p53 loss of function, leads, as MaBoSS, only to the metastatic attractor.
mbn_mut["p53"] = 0
%time a = list(mbn_mut.attractors(reachable_from=initial_condition))
tabulate(a, columns=outputs)
CPU times: user 10.2 ms, sys: 949 µs, total: 11.2 ms
Wall time: 10.2 ms
| Apoptosis | CellCycleArrest | EMT | Invasion | Migration | Metastasis | |
|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 1 | 1 | 1 |