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.
Actually, I signed up Rosalind.info 8 months ago, I didn’t really play around with it. But last week, in a BiGCaT science cafe, after I learnt it, I was more interested than before and I just started solving problems.
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.
This sub-challenge has several requirements:
Last semester, I took a course from Informatics Institute at METU called “Biological Databases and Data Analysis Tools” where first we learned what is a database and how to do queries on it. Also, the technology behind databases are taught. Then, we learned many biological databases and data analysis tools available. These include gene, protein and pathway databases, tools for creating databases.
I attended one of science cafe meetings of BiGCaT group today and we discussed use of online tools for teaching bioinformatics.
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.
I have been going over the sub-challenges before attempting to solve them. As I mentioned, there are three sub-challenges and somehow they are connected.
Understanding signaling networks might bring more insights on cancer treatment because cells respond to their environment by activating these networks and phosphorylation reactions play important roles in these networks.
This year, 8th Dream Challenge takes place and I will be working on this project as my internship job in BiGCaT, Bioinformatics, UM. The challenge brings scientists to catalyze the interaction between experiment and theory in the area of cellular network inference and quantitative model building in systems biology (as said on their webpage).