I had a meeting with BiGCaT this week and we discussed DREAM Breast Cancer Challenge. I presented the challenge and also some ways that I have found to solve the first sub-challenge network inference. Tina, from BiGCaT, suggested starting with in silico data which is much simpler than breast cancer data. Later, I can use the methods I develop for in silico data in experimental data. in silico data contains 20 antibodies, 3 inhibitors and 2 ligand stimuli with 2 different concentration for each.

With CellNOptR, we will try to construct network models for the challenge. For this, the tool needs two inputs. First one is a special data object called CNOlist that stores vectors and matrices of data. Second one is a .SIF file that contains prior knowledge network which can be obtained from pathway database and analysis tools. CNOlist contains following fields: namesSignals, namesCues, namesStimuli and namesInhibitors, which are vectors storing the names of measurements.

This sub-challenge has several requirements: Directed and causal edges on the models (32 models - 4 cell lines × 8 stimuli) Edges should be scored (normalizing to range between 0 and 1) that will show confidence Nodes will be phosphoproteins from the data Prior knowledge network (that can be constructed using pathway databases) is allowed to be used (actually this is a must for some network inference tools) First thing was to look for existing tools.

The inference of causal edges are described as the change on a node seen after the intervention of another node. If the curves obtained over time overlap (under intervention or no intervention), then there is no relation. Otherwise, we can draw an edge between those nodes and according to the level, up or down, the edge will be activating or inhibiting. These causal edges are context-specific so in different cell line data, we may have different relations.