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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: derekb.lowe@gmail.com Twitter: Dereklowe

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November 7, 2008

System Biology: Ready, or Not?

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Posted by Derek

Systems biology – depending on your orientation, this may be a term that you haven’t heard yet, or one from the cutting edge of research, or something that’s already making you roll your eyes at its unfulfilled promise. There’s a good spread of possible reactions.

Broadly, I’d say that the field is concerned with trying to model the interactions of whole biological systems, in an attempt to come up with come explanatory power. It’s the sort of thing that you could only imagine trying to do with modern biological and computational techniques, but whether these are up to the job is still an open question. This gets back to a common theme that I stress around here, that biochemical networks are hideously, inhumanly complex. There’s really no everyday analogy that works to describe what they’re like, and if you think you really understand them, then you’re in the same position as all those financial people who thought they understood their exposure to mortgage-backed security risks.

You’ll have this enzyme, you see, that phosphorylates another enzyme, which increases its activity. But that product of that second enzyme inhibits another enzyme that acts to activate the first one, and each of them also interacts with fourteen (or forty-three) others, some of which are only expressed under certain conditions that we don’t quite understand, or are localized in the cell in patterns that aren’t yet clear, and then someone discovers a completely new enzyme in the middle of the pathway that makes hash out of what we thought we knew about

So my first test for listening to systems biology people is whether they approach things with the proper humility. There’s a good article in Nature on the state of the field, which does point out that some of the early big-deal-big-noise articles in the field alienated many potential supporters through just this effect. But work continues, and a lot of drug companies are putting money into it, under the inarguable “we need all the help we can get” heading.

One of the biggest investors has been Merck, a big part of that being their purchase a few years ago of Rosetta Inpharmatics. That group published an interesting paper earlier this year (also in Nature) on some of the genetic underpinnings of metabolic disease. A phrase from the article's abstract emphasizes the difficulties of doing this work: "Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors." Yes, indeed.

But here’s a worrisome thing that didn’t make the article: Merck recently closed the Seattle base of the Rosetta team, in its latest round of restructuring and layoffs. One assumes that many of them are being transitioned to the Merck mothership, and that the company is still putting money into this approach, but there is room to wonder. Update: here's an article on this very subject). There is this quote from the recent overview:

Stephen Friend, Merck's vice-president for oncology, thinks that any hesitancy will be overcome when the modelling becomes so predictive that the toxicity and efficacy of a potential drug can be forecast very accurately even before an experimental animal is brought out if its cage. "The next three to five years will provide a couple such landmark predictions and wake everyone up," he says.

Well, we’ll see if he’s right about that timeframe, and I hope he is. I fear that the problem is one of those that appears large, and as you get closer to it, does nothing but get even larger. My opinion, for what it’s worth, is that it’s very likely too early to be able to come up with any big insights from the systems approach. But I can’t estimate the chances that I’m wrong about that, and the potential payoffs are large. For now, I think the best odds are in the smaller studies, narrowing down on single targets or signaling networks. That cuts down on the possibility that you’re going to find something revolutionary, but it increases the chance that anything you find is actually real. Talk of “virtual cells” and “virtual genomes” is, to my mind, way premature, and anyone who sells the technology in those terms should, I think, be regarded with caution.

But that said, any improvement is a big one. Our failure rates due to tox and efficacy problems are so horrendous that just taking some of these things down 10% (in real terms) would be a startling breakthrough. And we’re definitely not going to get this approach to work if we don’t plow money and effort into it; it’s not going to discover itself. So press on, systems people, and good luck. You’re going to need it; we all do.

Comments (33) + TrackBacks (0) | Category: Biological News


COMMENTS

1. RB Woodweird on November 7, 2008 9:17 AM writes...

"The next three to five years will provide a couple such landmark predictions and wake everyone up," he says. "Then we will build our fusion-powered factory on Mars to produce the drugs. And we will advertise them on giant 3D interactive billboards that the consumer will be able to see as they fly to work in their jetcars."

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2. Jose on November 7, 2008 10:05 AM writes...

Clearly a VP at Merck understands computational tox is a joke, and will be for a few decades at least? Maybe he's just a mole for PETA?

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3. processchemist on November 7, 2008 10:15 AM writes...

What's the right english word? Ludicrous?

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4. CJ Francis on November 7, 2008 11:31 AM writes...

The great news for large (and small) pharmaceutical companies is that systems biology will have intermediate successes from static approaches (target identification), and longer term successes as we get better at measuring and modeling dynamics.

Understanding where treating disease approximates unscrambling an egg (thinking in terms of phase transitions) will be a mixed blessing. I guess chronically relapsing diseases may have financially attractive endpoints - in this regard.

The other obvious questions come back to combination therapies (possibly tuned to an individual) and the financial barriers (from payers) therein.

Cool article in Clin. Pharmacol. Ther. this week on a new metric for target identification in networks (Hwang et. al.).

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5. Anonymous on November 7, 2008 11:42 AM writes...

Predictive modeling and simulation (M&S) has been in the use since long time by many other fields of research such as aerospace industry. It is only recently the pharmaceutical industry started understanding the importance of M&S in developing new molecules. Recently, NIH organised a two day work shop on systems pharmacology which has drawn good attention. More information about the workshop can be found at the following address, http://meetings.nigms.nih.gov/?ID=3447
As Derek said it is very difficult to model the processes at molecular level since what we know is very little and what we don't know is very much. However, we can explain the whole systems behavior with much accuracy and precision with mechanistic/semimechanistic/empirical models. I am not sure how long it will take to predict the compounds toxicity/efficacy without taking the animal out of its cage, but I am sure that if we can harness the available data from in vitro studies, animals and phaseI & II studies we can surely predict the possible success/failure of the compound in phase III trials. That it self is a huge progress, I feel.

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6. Ben on November 7, 2008 11:46 AM writes...

Had I not gone into consulting, I would've gone to grad school to do systems biology -- so take this opinion with a grain of salt (as I'm somewhat biased)...

I think the last couple of years of the pharmaceutical industry has shown that the typical reductionist approach to biology is not working. Too often, clinical trails fail b/c of bizarre interactions that no one could have envisioned, tox issues, PK issues, differences in the genetic background of patients, etc (and sometimes, in a worse case scenario, a combination where the sum of the pieces is greater than the whole).

With that said, though, to make a precise forecast of the time frame we're talking about makes on sense at all -- remember the 1980s when everyone was talking about the wonders of computer-aided drug design? In the tech industry there's something called a hype cycle, and I'm concerned that it's too early in the Systems Biology hype cycle to really judge its true value, especially as much of current research is still working out basic theoretical principles about gene networks which have yet to have very clear material consequences to a field as applied as pharma/biotech research. Too often, we hear "conclusions" much like the Rosetta Inpharmatics article: "We believe [insert trait here] is controlled by a [insert synonym for complex or large or impossible to understand] ["network"/"system"] of biological factors."

So, to answer your question -- I don't think we're quite ready yet, but it would be super-awesome if this panned out!

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7. pete on November 7, 2008 12:14 PM writes...

Dis the field if you must fellow commenters, but I think there's hope for it still. We study biological signaling systems with a belief that nature is logical. So if the experiment is done thoughtfully, our data may tell us something about natural design. Problem is, we didn't design the system so it's a bitch to understand, much less model. And, yes, going from kinase-x ==> kinase-y ==> target-43 likely encompasses a lot of unappreciated complexity. But does that mean we chuck up our hands and call it all a fool's errand ?

Systems Biology is simply a tool. A way of dropping a trail of bread crumbs (via computing) through the dense forest. And if, after walking down 100 forest paths, the connections we draw are crude and presumptuous, well that's a problem. Our trail map is the design of a monster.

But the crude beast in the computer can evolve as we get a better handle on the natural connections. I'm here to cast my vote that we don't have to understand the structure and function of every biomolecule in order to get some useful predictions from our collection of bread crumb trails.

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8. Jose on November 7, 2008 1:01 PM writes...

"We study biological signaling systems with a belief that nature is logical."

I disagree most seriously. A system that results from evolutionary pressures will not be a logically constructed one. It doesn't mean the entire field is a fool's errand, but the hype is way out of proportion to reality.

Look at the graveyard of obesity targets- CAMKK2, ghrelin, AMPK, CB-1, and NPY and, and....

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9. Anon on November 7, 2008 2:55 PM writes...

Jose, your non sequitur, while ironic, isn't very convincing.

If evolved systems are not logical, what are they?

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10. Wavefunction on November 7, 2008 3:13 PM writes...

Evolved systems may be logical but they also may be chaotic (and I use the word as it's meant in chaos theory). In which case there may be scant possibility of doing prediction at the level of new drug discovery. But no, I don't think that prediction is futile. At the very least there could be a windfall of other discoveries that could come from studies.

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11. Ed on November 7, 2008 3:15 PM writes...

Perhaps they are emergent?

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12. Sili on November 7, 2008 3:19 PM writes...

I see I wasn't the only one to think of PETA and their whining.

I guess the reason they claim that the technology is already here, is an attempt at avoiding being asked to put their money where their mouths are and fund the damn thing.

You can make a lot of vilificatory ads with that sorta funding.

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13. Daniel Newby on November 7, 2008 4:01 PM writes...

Evolved systems are whimsical in organization. Their signal processors are richly and baroquely interconnected, far beyond the minimum need. Their failure modes and limits are often unpredictable. While they often have general functional schemes, their design is not logical. I say this as an electrical engineer and computer programmer who designs digital logic systems.

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14. drewaight on November 7, 2008 4:36 PM writes...

systems biology is pure unadulterated hogwash.
the only thing worse is translational medicine.

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15. DLIB on November 7, 2008 4:37 PM writes...

Evolved systems are thermodynamic...Done!

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16. MolecularGeek on November 7, 2008 5:18 PM writes...

Drewaight, why do I get the feeling that you long for the good old days when people went around collecting dirt and sewage samples, and hoping to get action on a crude fraction screen?

Evolved systems CAN be baroque, but there is often also a logic that arises from the actual mechanisms of evolution. Look at all the bits and pieces of molecular architecture that get reused and elaborated on across biochemistry, or the convergence of common "currency" units of electron and carbon transfer. Once a certain level of complexity is reached in a biological system, it becomes far more likely that an existing pathway/receptor/enzyme is going to be the starting point for new functionality/adaptation, rather than a truly random point mutation.

I suspect that one's views on systems biology are likely to be tied to one's views on models in general. If you are happy with empirical models that are well-predictive, systems biology probably seems like a boondoggle right about now. For those who prefer models that are based on underlying mechanisms, systems biology holds hope for models with fewer black-box assumptions built into them.

MG

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17. milkshake on November 7, 2008 6:49 PM writes...

Evolution-derived designs are like Windows operating systems, English spelling or US immigration law.

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18. Anonymous BMS Researcher on November 7, 2008 8:10 PM writes...

I'm in Genomics now, and in a previous life I was an engineer. From my experience in both fields, I can say it is much much much easier to build predictive models of engineering systems than it is to build predictive models of biological systems. Biological signaling pathways make the ugliest Rube Goldberg kludges in the software universe seem utterly straightforward. What we are basically doing now in Genomics is the hairiest reverse-engineering problem in the history of technology.

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19. Anonymous BMS Researcher on November 7, 2008 8:13 PM writes...

When I read my previous posting aloud to my wife who is sitting at the other end of the sofa, she said, "fortunately, when we read the Genome God didn't make us click on a license agreement that we would not attempt to disassemble or otherwise reverse-engineer the code." I replied, "no, God simply uses security through obscurity."

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20. drug_hunter on November 7, 2008 9:51 PM writes...

I'm with Pete (#7): you don't have to 100% understand a system to be able to model it in a useful (albeit imperfect) way. And I'm with Derek that relatively small, simple "sysbio" applications are the way to start. So for example, understanding how changes in gut flora affect Crohn's disease: possible. Understanding how GI disorders are related to autism: don't hold your breath.

www.thoughtfulhouse.org/autism-gastrointestinal.htm

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21. jgualt on November 8, 2008 3:05 PM writes...

The problem with systems biology is where do you get the primary data from to make your systems predictions? My company has been in this field 9 years in the metabolics area and while I think there are useful outcomes from looking at a systems approach and generallities which can enhance our understanding of disease, the limits to predictability are significant because the basic inputs to design the models are based upon the published pharmacology (or worse genomics)literature, a large proportion of which is crap. How is a systems biologist supposed to sort through tens of thousands of published data reports and decide which data should be incorporated in the model and which are from unreliable compounds or research groups. I know from working with them they don't, and this leads to models with poor predictibility.

Secondly let's say this approach is successful.
"Well J. Chemist my systems models predicts that if you make a full agonist of receptor A, that also is an inhibitor of Kinase B, and a use dependant blocker of channel C we will have a perfect drug. Have fun..."

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22. FrankW on November 8, 2008 6:04 PM writes...

Sydney Brenner in

Discover magazine

" You see, everybody’s running around talking about systems biology and integrative biology. It’s nothing new. It’s called physiology."

Nature Reviews;

"It is what I call 'low input, high throughput, no output science'! The proponents of this kind of science claim that by generating descriptions of the behaviour of biological systems they'll be able to generate models of what's going on in them, and then refine these models. They call this 'systems biology'. To use a simple analogy of this type of science, consider that one is sitting outside a room in which someone is playing a drum. The room is wired for sound, and using only the recording of the sounds one is trying to reconstitute the physical properties of the drum. In my mind one cannot succeed because in this classic inverse problem, information is lost and measurements are inaccurate. The best thing to do is to tackle the problem directly by studying the drum — then one can play it oneself. That is what molecular biology is about. It is mechanism based and causation based. But nowadays scientists aren't asking as many mechanistic and causative questions. "

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23. richarda on November 8, 2008 10:44 PM writes...

I think there's an apparent logic to systems biology--one that be anthromorphically explained with 20/20 hindsight. The best way to get meaningful understanding of biological processes in relation to the whole organism is to do basic science research and not through science through drug discovery and translational research. It is easier to "connect the dots" between reductionists model than it is with footnotes from failed drugs.

This means that academic and research institutions should stop acting like drug companies and start doing real basic scientific research.

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24. Great Molecular Crapshoot on November 9, 2008 2:45 PM writes...

I think we’re a long way off being able to simulate physiology at a sub-cellular resolution. There are number of problems. First cells and membranes are very crowded, anisotropic places and the low level processes themselves are not well enough understood to be simulated meaningfully. Even if we did understand the processes, our lack of knowledge of numbers of receptors and concentrations (may vary with location in cell) of proteins, nucleic acids and other molecules will make it difficult to set numerical parameters for the simulations and models. As Wavefunction points out, the behaviour of the system may even be chaotic in that small changes in parameters will result in large differences in endpoint.

I recall a presentation on an aspect systems biology. The presenter was crapping on about rate constants and I was genuinely interested in how these would be derived. It looked like the process involved protein molecules associating in the cell membrane and to try to understand it better, I asked what the units of the rate constants were. The presenter appeared to have no idea of the answer to the question or even why I was asking it. A little worrying, to say the least

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25. WallStreet on November 9, 2008 7:38 PM writes...

What we need is at least 3.2 billion more scientists out of our federal reserve owned factory universities. Biology isn't a knowledge industry, its merely tedious work that can be done by anyone.

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26. Kay on November 10, 2008 7:45 AM writes...

Since Derek indicates that the PK predictability problem has been solved – hence leading to a huge increase in NME output – simply solving either the tox or efficacy problem should propel us to a sustainable business model.

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27. Don Taylor on November 10, 2008 8:16 AM writes...

The single gene, single protein, single target concept for drug discovery has expired. This is not to say some new drugs won’t fit this paradigm, but I believe a systems approach is required to propel the industry into solving the more challenging diseases. Take cancer for example. A tumor is a system inside a system with normal cells, abnormal cells, and the infusion of the migratory immune system, all playing a critical role in potentially both trying to destroy and trying to save the tumor.

At the company I work for, Cellumen (http://www.cellumen.com), we look at the cell as an integrated, interacting network of genes, proteins and metabolites that give rise to normal and abnormal function. We're calling it the Cellular Systems Biology (CSB) approach, serving as the interface between the “omics” technologies and the ultimate goal of whole-organism systems understanding. Unfortunately pharma primarily have infrastructures equipped to screen single, or less complex targets, but we are trying to change that by introducing them to cellular systems tools for lead generation and toxicity screening.

Don Taylor
Senior Director of Marketing & Corporate Development at Cellumen

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28. Jonadab on November 10, 2008 8:25 AM writes...

It's like approaching a large mountain range on foot. At first you see the mountains on the horizon, and you know they're a ways off, but you think maybe you can reach them in a couple of hours. Two days later you know you were wrong about the timeframe, but the mountains look much closer now, and you think maybe you can reach them on the third or fourth day. Two weeks later you realize you were *really* wrong about the timeframe, but the mountains look much closer now...

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29. Ty on November 10, 2008 8:55 AM writes...

I am fine with the idea of systems biology as an academic discipline, but. Why pharma industry pour money into it at this stage is beyond me. Greed and fear? Ah, history repeats itself and they just don't learn from their own mistakes.

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30. Jose on November 10, 2008 11:01 AM writes...

Any ballpark guesses for what percentage of signaling pathways have been investigated at even a gross level for say, C. elegans? for humans?

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31. Cellbio on November 10, 2008 12:58 PM writes...

To Don, #27

You say, "we look at the cell as an integrated, interacting network of genes, proteins and metabolites that give rise to normal and abnormal function. We're calling it the Cellular Systems Biology (CSB) approach, serving as the interface between the “omics” technologies and the ultimate goal of whole-organism systems understanding", and you've got a marketing job, really!

The problem is not what we call things or how we look at them. A trick of the human mind has us believe that if we rename something, we have changed the fundamental nature of the beast, but we have not. The best post in this thread is the one about systems biology being physiology. In drug discovery, the best analogy is, IMO, pharmacology. I have done what you promote, in the context of drug discovery, so has BioSeek. It can be useful. It is not, however, a replacement for the "pharma" approach, but a simple upgrade.

What I mean is that technology (liquid handling, analyte detection, mutliplexing, cell enrichment) now enables us to make many measures of drug impact (pharmacology), when previously few were made. It also enables us to make them on large sets of compounds, providing better SAR analysis or enabling screening of libraries. The achievable scale of cellular screening also provides large data sets that can reveal drug profiles, including on-target impacts and toxicities.

Some of the work we have done revealed profiles that matched known drugs, revealing novel chemical matter. Other efforts resulted in profiling a med chem series plagued by tox by distinguishing compounds by biological profile (binning) and selecting new leads. So I see the potential value, as do others that are transitioning from biochemical HTS to cellular HTS within pharma.

I would be wary of using engineered systems (reporter assays, FRET) for much value. One thing we learned is that a laboratory cell line bears little to no resemblance of a normal human cell. 700K compounds were screened in a macrophage cell line, but none of the 1000 hits mapped to primary macrophage function. Instead, the hits were more likely to impair T cell function, and many mapped to tyrosine kinase inhibitors. The cell line was totally rewired from ser/thr kinase driven responses to tyr kinase driven survival.

Good luck at Cellumen. Others have come and failed in this space. I hope you make it, but it is a tough business model to sell. I don't know your model, but the two companies I worked with as potential vendors either wanted royalties on any drug that came out of programs they touched, or a very large fee. Both were non-starters for working together.

A the end of the day, what wins is good chemical matter. How one describes the pharmacology of the molecule (heat maps, profiles) matters less than summoning the company courage to spend the development dollars. If systems biology can provide greater confidence, then it has a chance. So far, the systems biology crowd (I'd place myself in one form of SB) is leading in optimism, but failing to make the case on meaningful data. Time will tell if SB is just the next omics, or a real game changer. IMO, it is the path to the past, in that we will rely on pharmacology, remade with today's technology (and the requisite catchy name with no clear meaning).

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32. Smurf on November 11, 2008 12:51 AM writes...

Cellbio,

I could not agree more!

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33. S Silverstein on November 15, 2008 11:19 PM writes...

Understanding the mechanism of complex disease states via simulations in silico? I have my doubts. I wrote the following cautionary little essay in 2002.

By the way, many in Merck's original internal bioinformatics dept. found themselves laid off in the "equinox" layoffs of 4,400 in 11/2003.


http://www.bio-itworld.com/archive/081302/letters.html

Medical Informatics MIA

I enjoyed reading the article "Informatics Moves to the Head of the Class" (June Bio·IT World). Thank you for spotlighting the National Library of Medicine (NLM) training programs in medical informatics and bioinformatics, of which I am a graduate (Yale, 1994).

Bioinformatics appears to receive more media attention and offer more status, career opportunities, and compensation than the less-prestigious medical informatics.

This disparity, however, may impede the development of next-generation medicines. Bioinformatics discoveries may be more likely to result in new medicines, for example via pharmacogenomics, when they are coupled with large-scale, concurrent, ongoing clinical data collection. At the same time, applied medical informatics, as a distinct specialty, is essential to the success of extensive clinical data collection efforts, especially at the point of care.

Hospital and provider MIS personnel are best equipped for implementing business-oriented IT, not clinical IT. Implementing clinical IT in patient-care settings constitutes one of the core competencies of applied medical informaticists.

Informatics specialists with a bioinformatics focus — even those coming from the new joint programs — usually are not proficient in hospital business and management issues that impede adoption of clinical IT in patient care settings. Such organizational and territorial issues are in no small way responsible for the low utilization of clinical IT in patient care settings.

It will be important for medical informaticists focused in the clinical domain and bioinformaticists specializing in the molecular domain to collaborate with other specialists in order to best integrate clinical and genomic data.

Further information on these issues can be found in the book Organizational Aspects of Health Informatics: Managing Technological Change, by Nancy M. Lorenzi and Robert T. Riley (Springer-Verlag, 1995). Various publications from the medical informatics community, such as the American Medical Informatics Association (www.amia.org) and the International Medical Informatics Association (www.imia.org), are also useful.

Scot Silverstein, MD
Director, Published Information Resources & The Merck Index
Merck Research Laboratories

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