Can Graph Theory Help Predict Cancer Progression?

[gravenewworld]

Let's say I had a network of enzymes that are all interconnected that may be involved in cancer progression. Each enzyme produces a chemical product that might be used by some other member in this network, but each enzyme might produce a product at different rates. Is there a way I could possibly use graph theory to model this network, along with the rate traffic through this enzyme network, in order to make some predictions on the final "product" of this network (the final product of which assists in cancer)?

I've had some graph theory before, but is there some way to incorporate the "rate of traffic" parameter into such a graph? So not just figuring the number of possible ways it might be possible to synthesize a final product, but how much and how fast we expect it to happen? What are some topics I can look up to point me in the right direction with regards to graph theory?

 

[mfb]

Can you model "rate of traffic" as weight for the edges?
Time-dependence might be tricky, unless you include some additional parameters for the edges.

It is possible to model your network as graph, the question is how much graph theory do you want to apply to it ;).

 

[Jack21222]

There's a whole field dedicated to this sort of thing; network science. They combine graph theory and some other disciplines to solve problems exactly like you describe.

So, you don't need to reinvent the wheel here. Check out some of the papers on Barabasi's website (I think it's www.barabasilab.com). You might find papers that have already done what you propose.

EDIT: Here is one paper that kind of scratches the surface and might be a decent starting point: http://jeb.biologists.org/content/210/9/1548.short

 

[gravenewworld]

Hmm thanks for the responses and paper leads. I realize there's a whole area in systems biology dedicated to this sort of thing, but was wondering if someone had a lead on a paper like the one you posted that would save me time on where to start. Quite an interesting read.

Been going through a sort of identity crisis lately. Am I a chemist? No. Am I a biologist? No. Am I an engineer/mathematician? No. I'm basically a guy that knows how to do some things from all of those fields. It would be neat to models something I'm studying mathematically to make predictions, test it biologically, and then pharmacologically treat a suspected network with a smarter designed molecule.

Cheers.

 

[CellCoree]

I can say that yes, graph theory can definitely be used to model and predict the progression of cancer in a network of enzymes. Graph theory is a mathematical framework that is used to study and analyze networks, such as the network of enzymes you mentioned. By representing the enzymes and their interactions as nodes and edges in a graph, we can apply various graph theoretical measures and algorithms to understand the behavior of the network.

In your case, the rate of traffic through the enzyme network can be incorporated into the graph by assigning weights to the edges. These weights can represent the rate at which the chemical products are produced and used by other enzymes in the network. This information can then be used to make predictions about the final product and its role in cancer progression.

Some topics that you can explore in relation to graph theory and cancer progression are centrality measures, which can help identify the most important enzymes in the network, and network dynamics, which can help understand how the network changes over time. Other areas of research that are relevant to your question include network clustering and community detection, which can help identify groups of enzymes that work together, and network resilience, which can help predict the impact of perturbations in the network.

I would suggest looking into papers and research studies that have used graph theory to study cancer progression and related topics. This will give you a better understanding of how graph theory can be applied in this context and provide you with some specific techniques and methods that you can use in your own research. Overall, graph theory can be a powerful tool for predicting and understanding the progression of cancer, and I encourage you to explore this topic further.