Can Graph Theory Help Predict Cancer Progression?

Click For Summary

Discussion Overview

The discussion revolves around the potential application of graph theory to model enzyme networks involved in cancer progression. Participants explore how to incorporate parameters such as the rate of traffic through these networks to make predictions about the final products that may assist in cancer development. The scope includes theoretical modeling, interdisciplinary approaches, and practical applications in systems biology.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant proposes modeling a network of interconnected enzymes using graph theory, emphasizing the need to include the rate of traffic through the network.
  • Another participant suggests that the "rate of traffic" could be represented as weights on the edges of the graph, while noting that time-dependence may require additional parameters.
  • A third participant mentions the existence of a field called network science that integrates graph theory with other disciplines to address similar problems, recommending resources from Barabasi's lab.
  • A later reply expresses appreciation for the responses and seeks specific papers to streamline the research process, reflecting on the interdisciplinary nature of the participant's background and aspirations in modeling and treatment.

Areas of Agreement / Disagreement

Participants generally agree on the applicability of graph theory to model enzyme networks, but there is no consensus on the specific methodologies or the extent of graph theory's application. The discussion remains exploratory with various perspectives on how to approach the problem.

Contextual Notes

Limitations include the potential complexity of incorporating time-dependence into the model and the need for further exploration of existing literature to find relevant studies. There is also a recognition of the interdisciplinary challenges faced by participants.

Who May Find This Useful

Researchers and students interested in systems biology, network science, cancer research, and the application of mathematical modeling in biological contexts may find this discussion relevant.

gravenewworld
Messages
1,129
Reaction score
27
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?
 
Mathematics news on Phys.org
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 ;).
 
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
 
Last edited:
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.
 

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 2 ·
Replies
2
Views
4K
  • · Replies 7 ·
Replies
7
Views
4K
  • · Replies 33 ·
2
Replies
33
Views
3K
  • · Replies 11 ·
Replies
11
Views
5K
Replies
6
Views
5K
  • · Replies 0 ·
Replies
0
Views
3K
  • · Replies 15 ·
Replies
15
Views
6K
  • · Replies 1 ·
Replies
1
Views
3K