Comment by JunkDNA | Hacker News Reader

Comment by JunkDNA | Hacker News Reader

While it's nice from a technical perspective, this is unlikely to lead to a cancer cure. Having worked in cancer drug development, I can tell you, there is no shortage of cancer targets. Researchers have a list of targets they want to hit, and chemists are pretty darn good at designing small molecule compounds to hit them.

The problem in cancer is not that we don't understand individual proteins or the way that drugs bind to them (the problem being solved in this article). It is that the biology of cancer is a crazy web of highly complex interactions and feedback loops of which we have a pathetically rudimentary understanding. So even when you think you're hitting a target that should kill the cancer, you find out there's some side pathway that spools up and limits the drug's efficacy (or worse, the cancer cells actively pump your compound out). If I recall, something like >95% of new cancer therapies fail in clinical trials. Most of the failure in this drug category is due to lack of efficacy (even though they hit a target, they don't do jack for treating the cancer). If you could make that number 85%, you'd probably be a Nobel contender.

Unfortunately, there's no digital route here. What needs to be done is a lot of slow, messy "wet bench" biology. There is no electronic shortcut to understanding how living cells work.

(Bioinformatician here). Although I think bench work is the most obvious route and the most likely way these problems will be solved, there are in principle some computational ways they could be addressed.

If we had good computational models of how perturbations would affect transcription networks, for example, we could predict these "side pathways" that so often occur in humans but not in mice.

But you're right, the reality is complex, and you have do deal not only with transcription, but translation, post-translational modification, non-coding RNAs, the list goes on... And most current models of this type don't delve into 3D simulation. Some people are working on whole-cell modeling but that's in its infancy.

Ultimately I believe the breakthroughs will come fastest if we can "close the feedback loop" by automating a lot of bench biology, and then have computers both generate and test hypotheses.

(former bioinformatician here)

I agree, the totality of the interactions for a single cell is so many orders of magnitude above what we are capable of currently modeling that I fear these computational approaches are dangerously over-hyped. Having been privy to the state-of-the-art projects in a lab with a ~5,000 node cluster it was still disappointing to see how rough the whole cell modeling approaches were. It's really tough just to model a small corner of the cytoplasm and get the diffusion of different protein and metabolites right, let alone address organelles or chromosomal folding and surface availability. It's a mess.

Over-hyped? In my neck of the woods these approaches are treated with extreme skepticism for just the reasons you mention.

For instance: De novo protein folding is not a solved problem, so how can a simulation predict dynamics for a protein whose conformation isn't even known?

I'm sure in the year 2150 when my grandchildren go to the doctor to be scanned by the tricorder, the results will go to the full-cell (and full-body) simulator...but for now, I think bioinformatics is better served by sticking to higher levels of abstraction like transcript and protein counts (for disease modeling purposes).

The main message I got from talking to people involved in systems biology is that we don't even get qualitative agreement between simulation and experiment, nevermind any sort of quantitative data, on extremely well studied systems like the lac operon. It seems like we're at least 25 years away from decent models.

Sorry to be late to the party-- xaa, could you explain a little more what you mean by

"Ultimately I believe the breakthroughs will come fastest if we can "close the feedback loop" by automating a lot of bench biology, and then have computers both generate and test hypotheses."

I don't know anything about bioinformatics, so I'm trying to see how this differs from vanilla automated model selection. I'm really interested, so please feel free to send me an email if you feel that's more appropriate.

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.

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.

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.

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.

I think it's important to note that although biological systems are still well out of our reach, chemical systems (using quantum chemical methods) is being done increasingly better, and we can learn a lot about various systems, ranging from graphene to organic semiconductors to metal-clusters in enzymes using computational methods. So one day, we'll slowly get there, and we shouldn't write it off. Though yes, the current state of the art is hopeless for cancer drug development--but the field is dynamically changing.

(Naive layperson here). Could the manual microsocpes-and-pipets work being done by lab biologists be mechanized, so that you're generating drug candidates in software, testing them in living cells, and using automatically-gathered observations to generate new candidates?

It is already done to an extent (high-throuhput machines), however, there's still a lot of old timers in biology who spent too long pipetting and haven't invested yet.

It would be good even for not-old-timers. I know a couple of bioinformatics people and they're definitely experts on one topic: RSI.

Unfortunately not everything is done on a massive scale so not everything is automated.

While I agree with you that there is a ton of automation, it's a bit flippant to suggest the manual work in biology is because of old-timers. You can't exactly automate necropsy on a rat liver to see if the compound you just gave it caused liver failure. There are a ton of experiments that are not automatable with current robotics technologies. Plus, even when you can automate, biology can't be rushed. If you're waiting for a tumor to grow in a mouse model, you have to wait real wall clock time.

I'm in no position to argue about the mechanics of rat autopsy. But I do know how to get high throughput when you have high latency: shotgun parallelism. Try tons of things, the vast majority of which you assume will turn up with nothing, starting at the same time, in parallel.

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