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DBL%20Hendrix%20small.png College chemistry, 1983

Derek Lowe The 2002 Model

Dbl%20new%20portrait%20B%26W.png After 10 years of blogging. . .

Derek Lowe, an Arkansan by birth, got his BA from Hendrix College and his PhD in organic chemistry from Duke before spending time in Germany on a Humboldt Fellowship on his post-doc. He's worked for several major pharmaceutical companies since 1989 on drug discovery projects against schizophrenia, Alzheimer's, diabetes, osteoporosis and other diseases. To contact Derek email him directly: Twitter: Dereklowe

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August 15, 2004

FullCell 1.0?

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Posted by Derek

Reader Maynard Handley, in a comment to the most recent post below, asks:

". . .how far are we from doing at least a substantial fraction of this stuff in silico? I've read that some amazing computational models of full cells now exist, but even so, this author didn't expect that drugs could be usefully tested computationally until 2030 which seems awfully far out."

I don't know the article that he's referring to, but "awfully far out" pretty much sums up my reaction, too. I just don't think we have enough data to do any real whole-cell modeling yet. It's coming, and perhaps for a few very well-worked-out subsystems we could do it now, but I'm sceptical even of that.

A few days reading the current cell biology literature will illustrate the problem. All sorts of proteins are found, all the time, to be players in systems that no one suspected them of being involved it. Kinases are found to phosphorylate things that no one had seen them do before, lipases are found to accept substrates that no one had realized they could. A given signaling peptide is gradually found to have more uses than a Swiss army knife. We don't even really understand the basic mechanisms (like G-protein-coupled receptor signaling) enough to model them to any useful level.

The process of finding these things out doesn't seem like it's going to end soon, and there have to be many fundamental surprises waiting for us. Modeling the system in their absence is going to be risky - interesting, no doubt, and potentially lucrative (if you find a useful approximation), but risky. It's going to take some pretty convincing stuff for the drug industry to ever depend on it.

And all of this applies to single cells, which come in, naturally, an uncounted variety, each with its own peculiarities, the great majority of which we don't have any clue about. And then you come to the interactions between cells, which are highly significant and (in many ways) a closed book to us at present. If we knew more about these things, we'd be able, for example, to culture human cell lines that acted just like their primary tissue progenitors - but we can't do it, not yet.

No, although I have every belief that these things are susceptible to modeling, I just don't think we'll see it (on a useful scale) any time soon. Over the next twenty years, I'd expect to have some of the easier-to-handle cellular subsystems worked out to give robust in silico treatments, but a whole cell? And all the types of whole cells? Much longer than that. More than that I can't guess.

Comments (3) + TrackBacks (0) | Category: In Silico


1. Ron on August 16, 2004 3:30 PM writes...

Derek, if I read you right -- and trust me, I am the lamest of the lay -- would it not be fair to say that cellular biochemistry gets even more complicated the more we learn about it?

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2. otey on August 16, 2004 4:05 PM writes...

I would love to hear you discuss the "gap" between knowing the DNA for a disease and how you use that information in your lab to design a drug for that disease. How much of a leg-up does knowing the DNA really give you when designing a drug?

You hear all this talk about DNA discoveries, but the rubber doesn't seem to meet the road. Or am I just beimg callow and impatient?

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3. Eric Pobirs on August 31, 2004 9:30 AM writes...

This has been seen in other fields, too. The modeling is useful but primarily for pointing out the massive gaps to be filled and if you're really having a good day show you which gap might be the next preferred target.

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