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I agree mostly with your post but there are computational approaches being developed that may help improve our understanding of these cancer networks. They wil

I agree mostly with your post but there are computational approaches being developed that may help improve our understanding of these cancer networks. They will in no way eliminates or reduce the need for wet lab biology but hopefully it will couple with improvements in high throughput experimental technology to help us design and make sense of experiments targeted at understanding the whole phenomena.

epistasis 14y ago
I work on computational approaches to using known, wet-bench validated interactions along with high-throughput cancer data, and agree that much more high-throughput data is needed, more so than small-scale approaches. Quantitative measurements of interactions rates in vitro are no more trustworthy than computational methods on high-throughput data, because you never know what other cofactors may be affecting the interaction, or what compartmentalization or localization you missed in your model system that's different in a real system. I see the only scientifically defensible way forward is to do large, data-rich networks like Eric Schadt does them. Individual wet-bench work is good to use as prior data, but it's just a hint at a part of a large, complex system, and overly reductive approaches are going to completely miss the big picture.
- xaa 14y ago
  Unfortunately, for non-computational peers and reviewers, "high-throughput" is often a synonym for "fishing expedition". This perception is gradually changing, though. Furthermore high-throughput assays are more expensive so people often cut corners on sample size.
JunkDNA 14y ago
I am well aware of these approaches, having worked on some of them myself in prior work I've done. You always run into limitations of what was known about the biology and how various things interact. The network models will probably work some day, but my point above was that this will only happen after a lot of hard wet bench work happens. There's just too much we don't know right now to use these techniques to develop deep understanding. Sometimes, when we were lucky, they would support an existing hypothesis. But that was only for activity in a single cancer cell line in a specific experiment, not a whole organism which is what matters for a drug.

epistasis 14y ago

I work on computational approaches to using known, wet-bench validated interactions along with high-throughput cancer data, and agree that much more high-throughput data is needed, more so than small-scale approaches. Quantitative measurements of interactions rates in vitro are no more trustworthy than computational methods on high-throughput data, because you never know what other cofactors may be affecting the interaction, or what compartmentalization or localization you missed in your model system that's different in a real system. I see the only scientifically defensible way forward is to do large, data-rich networks like Eric Schadt does them. Individual wet-bench work is good to use as prior data, but it's just a hint at a part of a large, complex system, and overly reductive approaches are going to completely miss the big picture.

xaa 14y ago
Unfortunately, for non-computational peers and reviewers, "high-throughput" is often a synonym for "fishing expedition". This perception is gradually changing, though. Furthermore high-throughput assays are more expensive so people often cut corners on sample size.

JunkDNA 14y ago

I am well aware of these approaches, having worked on some of them myself in prior work I've done. You always run into limitations of what was known about the biology and how various things interact. The network models will probably work some day, but my point above was that this will only happen after a lot of hard wet bench work happens. There's just too much we don't know right now to use these techniques to develop deep understanding. Sometimes, when we were lucky, they would support an existing hypothesis. But that was only for activity in a single cancer cell line in a specific experiment, not a whole organism which is what matters for a drug.