Comment by alexholehouse | Hacker News Reader

Comment by alexholehouse | Hacker News Reader

This is very exciting progress, but let's remember that there is a rather important caveat with this sort of thing;

"For their computer simulation, the researchers had the advantage of extensive scientific literature on the bacterium. They were able to use data taken from more than 900 scientific papers to validate the accuracy of their software model."

This is not (necessarily) a model of M. genitalium - it's a model of our understanding of M. genitalium, and as such incorporates in everything that current technology in biological sciences allows us to look at. It takes a huge amount of data from many sources and tries to bring it together on a scale not previously done. However, that data may have significant flaws, biases and ultimately considers only what we're good at looking at (technologically/scientifically speaking). It's awesome, and 100% the right direction for the field, but equally it is not a, "synthetic life form being simulated" as much as, "a very very complicated model which uses huge amounts of multidimensional data to try and replicate the behavior seen in that data".

That's true of any simulation or model - it's only as good as the data that goes into it.

The exciting thing is that with this model, they can now rapidly iterate between the behavior of the model and the behavior of the real organism, see where the gaps in knowledge are, and work to fill them in.

(I'm happy to see M. Genitalium getting attention, since I made a Java genome display for it 14 years ago http://www.righto.com/java/genome/MG.html)

>That's true of any simulation or model -it's only as good as the data that goes into it. //

Models can be predictive, indeed that's one of the main reasons to create a model to observe behaviour that otherwise might not have been expected.

A theory in physics for example is a model with proven predictive powers, the model [theory] 'knows' some aspects of what can be observed before those things can be confirmed empirically.

But as you indicate new empirical data can shows flaws in a model just as it can in a scientific theory.

The model is "M. genitalium" in the sense that it attempts to predict M. genitalium's phenotype from its genotype. In order to build the model we needed to use data from many additional organisms. In that sense, the model represents a hybrid of all of these organisms.

Agreed, and, for the record (as someone who's worked on modelling at various scales) I didn't for a second want to detract from this achievement - it really is incredible, and, as per usual, the discussion here shows that the HN community is more than au fait with the implicit assumptions made.

Also, and this is something I personally feel very strongly about, the paper is incredibly well written and accessible. There is no point making science difficult to understand. Science is (itself) not difficult - it's complicated, encompassing, and confusing, but nothing is actually difficult. There is a trend to use big words and complex language in scientific papers, which I worry must be incredibly off-putting for those less used to them, and are almost always totally unnecessary. This paper was just beautiful (I devoured it on my subway ride home) and certainly one I'll be recommending to friends interested in the systems biology field.

“The best ... model of a cat is another, or preferably the same, cat.” Rosenblueth and Wiener, 1945. http://www.csee.wvu.edu/~xinl/papers/role_model.pdf

Pithy but flawed.

The best representation of a thing is that same thing in the state you're trying to represent. The cat is not a model, a dead cat is not a great model of a live one.

With a data base consisting of hundreds of largely independent findings, it'd take one hell of a systematic bias to not get at least something right. Granted, they're obviously abstracting here, but we should avoid underplaying this achievement as much as overestimating it.

It sounds like the model is more postdictive than predictive. Not useless, but more like measuring a huge number of right triangles to determine the ratio of side lengths, versus deriving the pythagorean theorem.

There is perspective and there is selling a work short. This is much more than a blind statistical model of the parameters. In fact, your criticism would apply just as well if not more to the Higgs search methodology.

They develop a number of submodules that each use an appropriate algorithm or branch of mathematics to best represent the interaction. To integrate them, results from the previous time-step feed each model as appropriate. The derivation of the correct mathematics and algorithm to use and the best way to connect them is anything but measuring a bunch of sticks.

Plus they were able to do some predictive modelling and pointed a direction for a novel result. From the abstract: "experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions." This is not about simulating organisms, it is for accelerating experimental discovery of interactions, dynamics, parameters etc. This is a proof of concept that a technique which may one day contribute to saving lives is viable.

Exactly. We can't even calculate the exact 3d shape of any large protein, much less exactly how it would interact with all other proteins inside a cell. This is an approximation. A great scientific success for sure, but not an exact simulation of all the parts of a single cell.

"It's awesome, and 100% the right direction for the field"

Awesome, maybe. But "100% the right direction for the field" is a stretch. This is press-release bait. What hypothesis did it address? What theory is it advancing? (other than "we theorize that we can simulate a cell in a computer...ohlook, we did it!", of course.)

This is interesting from a technology perspective, perhaps, but it's hard to call it science.

"This is interesting from a technology perspective, perhaps, but it's hard to call it science."

As a physicist and scientist myself, I feel extremely inclined to say 'potato, potahto' at this one. This technology allows us to test many more hypotheses at a much quicker pace, which is good enough for me to call it science.

This technology allows us to test many more hypotheses at a much quicker pace"

I think you're seriously overestimating the quality of the model. This isn't particle physics -- it's not as if there's an equation that precisely predicts the results of any particular molecular interaction. We don't even know if we know the full set of possible interactions in a system of this size. How can we possibly simulate it?

This is an example of "we ran the fancy machine for a while, it spit out some data, and we cherry-picked 'interesting' results from the output. Some of them even show up in experiments!" These sorts of breathless announcements are common in computational biology, but they usually don't amount to much. The best work is extremely reductionist. As others have already noted, it's easy to spend 10x the computational resources simulating a single protein on timescales far, far shorter than the ones simulated here.

"I think you're seriously overestimating the quality of the model. This isn't particle physics -- it's not as if there's an equation that precisely predicts the results of any particular molecular interaction. We don't even know if we know the full set of possible interactions in a system of this size. How can we possibly simulate it?"

(Edit: there was a whole bunch of stuff here, but it's pretty much irrelevant given that we seem to agree on the basic points.)

"This is an example of "we ran the fancy machine for a while, it spit out some data, and we cherry-picked 'interesting' results from the output. Some of them even show up in experiments!" These sorts of breathless announcements are common in computational biology, but they usually don't amount to much. The best work is extremely reductionist. As others have already noted, it's easy to spend 10x the computational resources simulating a single protein on timescales far, far shorter than the ones simulated here."

That's fair enough. I checked your profile and saw that you have a PhD in computational biology, which is not my field of expertise, so I'm inclined to take your word on that one. In that case, my comments do, of course, not apply.

But the hypothesis is being tested against the model and not against reality. The model can help to indicate the result but in my opinion it's not scientific advance until tested against reality.

Now there may be a question as to whether we can ever truly test a hypothesis against reality or if we're stuck with models ...

It would start to get interesting if the simulation could predict results outside the scope of the input data. E.g. no-one tried putting the cell into environment X and using stimuli Y, see how well the simulation can predict the results.

This is the main point of interest for me personally.

That's true for anything that exists in the real world. Essentially you are suggesting that reproducing something empirically is flawed because empiricism is flawed. No?

Baby steps.

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