Category Archives: Science

My Op/Ed in Xconomy

petri dish by kaibara87

The opinion piece I wrote for Xconomy has been published. Luke Timmerman asked me on Monday to examine the bill and the sections that impacted the Biotechnology industry. I had not even realized there were parts of the huge healthcare reform bill.

I started writing on Monday evening and got Luke my version by about 1 PM on Tuesday (I had to take my car to the shop for its 15,000 checkup or I would have been done sooner). Luke had some edits and it was ready by early evening.

Everything was done using online technologies. Even 5 years ago it would have been hard to put this all together in such a short time. I essentially started from zero on the specifics (I mean how many people have actually read any of the healthcare reform bill itself?), educated myself rapidly, used my background of 25 years in the industry to form an opinion and composed the piece. I then carried on a ‘conversation’ with Luke to get it into final shape.

I found the relevant parts using Open Congress’s interface, which allows you to link to specific paragraphs, as well as leave comments. It presents a unique way for citizens to interact with the legislation that our Congress is working on. Not only are there links to every piece of information one may want, there are also links to news stories, and other facts (Like the Senate version has over 400,000 words.)

Without this web site, it would have been very difficult to even find the sections dealing with biotechnology, much less try to understand them, It was very easy to search for the relevant sections and get an understanding of what they really said. I read a few articles online to get some other viewpoints and then wrote my opinion of the sections.

The fact that the biotechnology industry now gets 12 years of market exclusivity for its products, several years longer than for the small molecule drugs sold by pharmaceutical companies, is really a pretty big deal.

There has been uncertainty for several years over this time frame, with the FTC feeling there should be little or no market exclusivity outside of the patent time frame to the industry’s organization, BIO, which wanted at least 12 years without regard of patent considerations.

Not knowing just how long a time period a new biologic might be free of competition can have a large effect on determining which therapeutics make it to the market place. Now those who model the value of a product have much surer time frames to work with.

I do not think the bill is as friendly to those companies hoping to create ‘generic’ biologics called biosimilars. While it does delineate a path to government approval, the legislation does not make it easy. There are some substantial costs for getting approval of these products. They may not be very much cheaper than the original therapeutic itself. and they do not get any real exclusivity for their products in the end.

For many possible follow-on biologics it will simply be too expensive to take them to market. The large costs incurred while doing this will also make it harder for them to take market share away from a biologic, which has had 12 years of unfettered ability to market itself and its positive results to the customers. at least market share based on cost.

And, as I read the section dealing with patent issues, I became even more aware of the hard road for these follow-on generics. In order to get patent issues dealt with before the follow-on biologic is marketed, the patent holders/licensees of the original drug must be furnished the same information that is submitted in the application to the FDA – the results of clinical trials, assays to determine the follow-on biologic’s potency, stability, etc.

It seems to me that this could open up all sorts of shenanigans. And it appears to be more than regular generics have to do. From what I could determine, a company hoping for approval of a generic simply has to provide the patent numbers that cover the drug it is proposing to market. I could find nothing to indicate that it must turn over all the data of the generic to its direct competitor before going to market.

How many companies will be willing to provide their direct competitor with all the information present in its application to the FDA? It seems to me a place where some mischief could occur.

Now, I did not have time to review the complete history of these sections. I’m sure I could find all the committee testimonies on these parts. Perhaps someone out there has more detailed information. I’d love to pull an Emily Litella and say “Never Mind.”

So, this bill settled something really important for the biotech industry and, while bringing some clarity to the idea of biosimlars, also introduced some possible complications.

I have to say it was fun to use the power of the Web to investigate the issue and form some opinions. Using technology to move information around faster is part of what SpreadingScience does.

The difference between the creative and the commonplace

tufte by BruceTurner

Edward Tufte Presidential Appointment
[Via Daring Fireball]

President Obama has appointed Edward Tufte to the Recovery Independent Advisory Panel, “whose job is to track and explain $787 billion in recovery stimulus funds”. Outstanding.


This is pretty cool. Tufte is one of my favorite people, not only for his highly original books on data presentation but also for his sheer force of personality. He is one of the most entertaining, enlightening speakers I have ever heard.

I attended one of his workshops in Seattle probably close to 20 years ago. There was an interchange that has stuck with me ever since, because it so succinctly illustrates the divide between truly original, innovative change and the typical corporate response.

Tufte was discussing the different interfaces between the Mac OS and Windows. After going through a lot of the pluses he saw in the Mac and a lot of the minuses in Windows, he stated that the Mac looked like it had been created by one or a small group of people with a single purpose, a single view of how the information should be presented, while Windows looked like it had been done by a committee.

He then said that all the best presentations were this way – a single point of view forcefully pushed onto everyone. Someone in the audience then asked but what happens if your single point of view turns out to be wrong, to not work.

Tufte replied, simply, “You should be fired.” You could almost audibly hear the intake of everyone’s breath. That is exactly what they feared and why they would always want to retreat into committee decisions – they can’t be fired if the committee made the decision. FUD is what drives most people.

The creative, the innovative do not really fear failure, often because they are adaptable enough to ‘route around the damage’ quickly enough. They do not usually doubt the mission they are on and are certainly not uncertain about the effects. Read about the development of the Mac. They were going to change the world, no doubt about it. While you can see that there really was a focus of vision, there are also lots of ‘failures’ that had to be fixed. The key was to fail quickly, leaving time to find success.

And permitting committed individuals to find their own way to success rather than rely on committees to fix them.

Committees very seldom fail quickly, since failure is the thing they fear the most. They would rather succeed carefully than perhaps fail spectacularly. And they very seldom produce revolutionary change.

Single viewpoint, change the world, rapidly overcome obstacles, adaptable. All characteristics of successful change. They do not fear spectacular failure because the fruits of success will be so sweet.

Getting at data

Four Ways of Looking at Twitter

Data visualization is cool. It’s also becoming ever more useful, as the vibrant online community of data visualizers (programmers, designers, artists, and statisticians — sometimes all in one person) grows and the tools to execute their visions improve.

Jeff Clark is part of this community. He, like many data visualization enthusiasts, fell into it after being inspired by pioneer Martin Wattenberg‘s landmark treemap that visualized the stock market.

Clark’s latest work shows much promise. He’s built four engines that visualize that giant pile of data known as Twitter. All four basically search words used in tweets, then look for relationships to other words or to other Tweeters. They function in almost real time.

“Twitter is an obvious data source for lots of text information,” says Clark. “It’s actually proven to be a great playground for testing out data visualization ideas.” Clark readily admits not all the visualizations are the product of his design genius. It’s his programming skills that allow him to build engines that drive the visualizations. “I spend a fair amount of time looking at what’s out there. I’ll take what someone did visually and use a different data source. Twitter Spectrum was based on things people search for on Google. Chris Harrison did interesting work that looks really great and I thought, I can do something like that that’s based on live data. So I brought it to Twitter.”

His tools are definitely early stages, but even now, it’s easy to imagine where they could be taken.

Take TwitterVenn. You enter three search terms and the app returns a venn diagram showing frequency of use of each term and frequency of overlap of the terms in a single tweet. As a bonus, it shows a small word map of the most common terms related to each search term; tweets per day for each term by itself and each combination of terms; and a recent tweet. I entered “apple, google, microsoft.” Here’s what a got:


Right away I see Apple tweets are dominating, not surprisingly. But notice the high frequency of unexpected words like “win” “free” and “capacitive” used with the term “apple.” That suggests marketing (spam?) of apple products via Twitter, i.e. “Win a free iPad…”.

I was shocked at the relative infrequency of “google” tweets. In fact there were on average more tweets that included both “microsoft” and “google” than ones that just mentioned “google.”


Social media sites provide a way to not only map human networks but also to get a good idea of what the conversations are about. Here we can see not only how many tweets are discussing apple, microsoft and goggle but the combinations of each.

Now, the really interesting question is how ti really get at the data, how to examine it in order to discover really amazing things. This post examines ways to visually present the data.

Visuals – those will be some of the key revolutionary approaches that allow us to take complex data and put it into terms we can understand. These are some nice begining points.

An interesting juxtaposition

data by blprnt_van

Reaching Agreement On The Public Domain For Science
[Via Common Knowledge]

Photo outside the Panton Arms pub in Cambridge, UK, licensed to the public under Creative Commons Attribution-ShareAlike by jwyg (Jonathan Gray).

Today marked the public announcement of a set of principles on how to treat data, from a legal context, in the sciences. Called the Panton Principles, they were negotiated over the summer between myself, Rufus Pollock, Cameron Neylon, and Peter Murray-Rust. If you’re too busy to read them directly, here’s the gist: publicly funded science data should be in the public domain, full stop.


and this

BBC News – Science damaged by climate row says NAS chief Cicerone
[Via BBC News | Science/Nature]

Leading scientists say that the recent controversies surrounding climate research have damaged the image of science as a whole.

President of the US National Academy of Sciences, Ralph Cicerone, said scandals including the “climategate” e-mail row had eroded public trust in scientists.


He said that this crisis of public confidence should be a wake-up call for researchers, and that the world had now “entered an era in which people expected more transparency”.

“People expect us to do things more in the public light and we just have to get used to that,” he said. “Just as science itself improves and self-corrects, I think our processes have to improve and self-correct.”


It is important for Federally funded research to be in the public domain. But, Universities, who hope to license the results of this research, and corporations, who will not as likely commercialize a product if they can not lock up the IP, Both of these considerations must be accounted for if we want to translate basic research into therapies or products for people.

So, as the Principles seem to indicate, most of this open data should happen AFTER publication, so this would give the proper organizations to make sure they have any IP issues dealt with.

But what about unpublished data? What about old lab notebooks? The problem supposedly seen now has nothing to do with data that was published. It has to do with emails between scientists. Is this relevant data that should be made public for any government funded research?

Who determines which data are relevant or not?

And what about a researcher’s time? More time in front of the public, more time filling out FOIs, more time not doing research in the first place.

The scientific world is headed this way but how will researcher’s adjust? There will have to be much better training of effectively communicating science to a much wider audience than most scientists are now comfortable with.

More collaboration in biology

[Crossposted at A Man with a PhD]

analysis by Cushing Memorial Library and Archives, Texas A&M
I’ve collected my data, now what do I do with it?:
[Via Bench Marks]

4-dimensional live cell imaging has gone from being a rare technique used only by cutting-edge laboratories to a mainstream method in use everywhere. While more and more labs are becoming comfortable with the equipment and protocols needed to collect imaging data, performing detailed analyses is often problematic. The application of computational image processing is still far from routine. Researchers need to determine which measurements are necessary and sufficient to characterize a system and they need to find the appropriate tools to extract these data. In Computational Image Analysis of Cellular Dynamics: A Case Study Based on Particle Tracking, Gaudenz Danuser and Khuloud Jaqaman introduce the basic concepts that make the application of computational image processing to live cell imaging data successful. As one of the featured articles in December’s issue of Cold Spring Harbor Protocols, it is freely accessible for subscribers and non-subscribers alike.

The article is adapted from the new edition of Live Cell Imaging: A Laboratory Manual, now available from CSHL Press.


My first year as a biochemistry graduate student, one of the classes simply dealt with the analytical technologies we would be using. Things like NMR, UV spectroscopy, circular dichroism, fluorescence and X-ray crystallography. They would help us understand the properties of isolated biological molecules

This paper gives a great view of some of the new analytical approaches that examine entire living cells, not just isolated molecules. Now it looks like students will also have to get some firm understanding of image analysis. There will be some really interesting results from these sorts of technologies. The conclusions provide insights into the promise and the problems:

Computational image analysis is a complex yet increasingly central component of live cell imaging experiments. Much has to be done to make these techniques useful for cell biological investigation. First, algorithms must be transparent, not necessarily at the level of the code but in terms of their sensitivity to changing image quality and the effect that control parameters have on the output. Second, the design of imaging experiments must be tightly coupled to the design of the analysis software. All too often, images are taken without careful consideration of the subsequent analysis and are forwarded to the computer scientist to retrieve information from the images. To avoid these problems, communication must be initiated early on, and experiments must be designed with the appreciation that data acquisition and analysis are equivalent components. Third, software development and application require careful controls, as is customary for molecular cell biology experiments. This article provides a brief introduction to the ideas useful for implementing such controls. Hopefully, the cell biological literature will include a more extensive discussion of the measures taken to substantiate the validity of results from image analysis. On the other hand, manual image analysis should no longer be an option. As discussed in this article, manual analyses fall short in consistency and completeness, two essential criteria underlying the validity of a scientific model derived from image data.

While the results can be amazing, there needs to be close collaboration between the different researchers involved. Because very few people will have all the expertise necessary for success. This tight coupling of researchers with vastly different backgrounds and focus (i.e. cell biology and bioinformatics) is a relative new aspect of modern biological research.

There may be slowing of this coupling in some labs but the successful results by those that can accomplish this type of collaboration will rapidly overtake those who take a slower course. As I mentioned below, large collaborations may be a big part of the published record as we move forward.

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Stop them from choking

golf by chispita_666
The Tiger Woods Effect:
[Via The Frontal Cortex]

Success is intimidating. When we compete against someone who’s supposed to be better than us, we start to get nervous, and then we start to worry, and then we start to make stupid mistakes. That, at least, is the lesson of a new working paper by Jennifer Brown, a professor at the Kellogg school.

Brown demonstrated this psychological flaw by analyzing data from every player in every PGA tournament from 1999 to 2006. The reason she chose golf is that Tiger Woods is an undisputed superstar, the most intimidating competitor in modern sports. (In 2007, Golf Digest noted that Woods finished with 19.62 points in the World Golf Ranking, more than twice as many as his closest rival. This meant that “he had enough points to be both No. 1 and No. 2.”) Brown also notes that “golf is an excellent setting in which to examine tournament theory and superstars in rank-order events, since effort relates relatively directly to scores and performance measures are not confounded by team dynamics.” In other words, every golfer golfs alone.

Despite the individualistic nature of the sport, the presence of Woods in the tournament had a powerful effect. Interestingly, Brown found that playing against Woods resulted in significantly decreased performance. When the superstar entered a tournament, every other golfer took, on average, 0.8 more strokes. This effect was even more pronounced when Woods was playing well. Based on this data, Brown calculated that the superstar effect boosted Woods’ PGA earnings by nearly five million dollars.


One of the things I have seen in great athletes I have known is, for want of a better term, a lack of self-awareness. They just do, They don’t think about it too much.

For example, they did not worry as much about striking out as I did. I had a talented bat, which allowed me to get a bat on almost anything. But I was not disciplined enough. If I had two strikes I would go after anything, anywhere because I did not want to strike out. I’d rather ground out by hitting a bad pitch than allow a called third strike. I was more worried about the humiliation of that one event than the larger strategic aspects.

I hated losing and would replay all the parts where if only I had done something different, then the result would have been a win. This was not something I really saw with the really great players. They just moved on, seemingly riding the vagaries of the sport with a wonderful adeptness I envied.

So it is nice to see that at the highest levels, when they really are competing with physical peers, the numbers indicate that they feel the same way. They think too much.

Now, another part of this is that once in a group of peers, such as the PGA, most people eventually find a relative plateau of effort and worry. That is, the pressure of the tour selects for golfers that can at least deal with the pressure of the Tour itself. And many golfers, week to week, do not have to really directly compete with Woods. They are in the middle, competing with the other golfers that they are used to seeing in the middle also. Familiarity means not too many worries, So they are not too worried and are not thinking too much to hurt their chances.

They find their own level and can be successful there.

It is when they have an extraordinary week, where they now move up into the elite group where overthinking can cause a problem. And, in some ways, being able to move away from the overthinking might allow them to stay in that elite group.

This sort of worry happens in many facets of life. The worry about our position, whether we are really good enough. It happens almost anytime we enter a truly novel situation.

I saw this first hand when I entered CalTech. The entire Freshman year was entirely pass/fail. Every class. Not only did this allow people to experiment and try a lot of different classes but it also provided a modicum of time to find your level without having to directly compete with others for GPA.

It removed a lot of pressure and worry. Most students had never had to think about studying in High School. They just did it. Like great athletes.

Now they were competing with other peers in ways that were completely novel and worrisome. By removing the pressure of grades, CalTech sought to ameliorate these worries. Not all the way but it was one less thing. We were less likely to choke and more likely to calm down as the novelty wore off.

So, in that first year I found a balance. I saw that there were guys that never seemed to do any homework, yet got better scores than me (Yes, they still had grades on tests, essays and such. It just did not matter for the GPA). I found that no matter how hard I studied, I just was not going to pass them. And that was fine. I saw where I fell by doing the work I was capable of.

I recognized that I was not going to be one of the elites at CalTech. And I could be okay with that. Giving us that first year to find our place in the crowd was one of the most significant things CalTech did.

And then, being a smart guy, I figured out ways to take classes that played to my strengths, used the knowledge I gained to raise my GPA every year, so that I was able to graduate with honors.

But having that break the first year permitted me to gather myself in ways that being dumped directly into competition with other might have broken me. Like a golfer who finds that there is a particular course that plays to their strengths.

It is a lesson I have held my whole life. So many organizations are designed to break people, taking only those who survive and making them the leaders, champions, etc. But that is so wasteful because there are so many others who, if given a break, a chance to find their own level, could perform quite well.

One of my advisors once said he purposefully created an environment of competition between those who work in his lab ‘The cream will rise to the top.’ Well, the cream will always rise but the process makes it curdled, And you waste so much that could have been so useful. Too many people dropped out of graduate programs, ones who could have been very good scientists, simply because the system worked by breaking its members.

It was designed to cast out those who ‘choked’. CalTech’s approach was to support everyone until they could figure out where they needed to be. Just like the middling golfers. They might not win very often but they provide some really exciting golf. Because they really are very, very good when compared to the rest of us.

We need better processes in scientific education so that more than only the elite make it through. Just as not every lawyer needs to plead in front of a jury, not every graduate student needs to get a job in academia. There are so many places where a well-trained scientist is needed.

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Make it a pub

[Crossposted at A Man with a PhD]

pub by gailf548
Participation Value and Shelf-Life for Journal Articles:
[Via The Scholarly Kitchen]

Discussion forums built around academic journal articles haven’t seen much usage from readers. Lessons learned from the behavior of sports fans may provide some insight into the reasons why.


The scientific discussions that many researchers have found the most productive are often those sitting around a table in a informal setting, like a pub. These discussions are often wide-ranging and very open. They often produce really innovative ideas, which get replicated on cocktail napkins.

Some of the best ideas in scientific history can be found on such paper napkins. Simply allowing comments on a paper does not in any way replicate this sort of social interaction. But there already online approaches that do. We call them blogs.

Check out the scientific discussions at RealClimate, ResearchBlogging or even Pharyngula. Often the scientific discussions replicate what is seen in real life, with lots of open discussion about relevant scientific information.

If journals want to create participatory regions in their sites, they might do well to mimic these sorts of approaches. David Croty at Cold Spring Harbor has such a site. Although it has not reached the popularity of RealClimate, it is a nice beginning.

I would think that research associations, with an already large audience of members, would have an easier time creating such a blog, one that starts by discussing specific papers but is open to a wide ranging, semi-directed conversation.

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Innovation on the cheap

innovate by jordigraells>

Why Great Innovators Spend Less Than Good Ones


A story last week about the Obama administration committing more than $3 billion to smart grid initiatives caught my eye. It wasn’t really an unusual story. It seems like every day features a slew of stories where leaders commit billions to new geographies, technologies, or acquisitions to demonstrate how serious they are about innovation and growth.

Here’s the thing — these kinds of commitments paradoxically can make it harder for organizations to achieve their aim. In other words, the very act of making a serious financial commitment to solve a problem can make it harder to solve the problem.

Why can large commitments hamstring innovation?

First, they lead people to chase the known rather than the unknown. After all, if you are going to spend a large chunk of change, you better be sure it is going to be going after a large market. Otherwise it is next to impossible to justify the investment. But most growth comes from creating what doesn’t exist, not getting a piece of what already does. It’s no better to rely on projections for tomorrow’s growth markets, because they are notoriously flawed.

Big commitments also lead people to frame problems in technological terms. Innovators spend resources on path-breaking technologies that hold the tantalizing promise of transformation. But as my colleagues Mark Johnson and Josh Suskewicz have shown, the true path to transformation almost always comes from developing a distinct business model.

Finally, large investments lead innovators to shut off “emergent signals.” When you spend a lot, you lock in fixed assets that make it hard to dramatically shift strategy. What, for example, could Motorola do after it invested billions to launch dozens of satellites to support its Iridium service only to learn there just wasn’t a market for it? Painfully little. Early commitments predetermined the venture’s path, and when it turned out the first strategy was wrong — as it almost always is — the big commitment acted as an anchor that inhibited iteration.


One problem of too much money is that bad ideas get funding also. In fact, there are often many more incremental plans than revolutionary ones. They soak up a lot of time and money.

Plus they create the “We have to spend this money” rather than “Where are we going to get the money to spend?”

Innovations often result in things that save money. But they are often riskier to start with. So how to recognize them and get them the money they need, but not too much?

Encouraging people to work on ‘back burner’ projects in order to demonstrate the usefulness of the approach is one way. Careful vetting can help determine whether it can be moved to the front burner or not.

Part of any innovator’s dilemma is balancing the innovative spirit with sufficient funding to nurture that spirit, without overwhelming the innovator with the debit of too much cash.

Updated: Short answers to simple questions

fail by Nima Badiey

NIH Funds a Social Network for Scientists — Is It Likely to Succeed?

[Via The Scholarly Kitchen]

The NIH spends $12.2 million funding a social network for scientists. Is this any more likely to succeed than all the other recent failures?


Fuller discussion:

In order to find an approach that works, researchers often have to fail a lot. That is a good thing. The faster we fail, the faster we find what works. So I am glad the NIH is funding this. While it may have little to be excited about right now, it may get us to a tool that will be useful.

As David mentions, the people quoted in the article seem to have an unusual idea of how researchers find collaborators.

A careful review of the literature to find a collaborator who has a history of publishing quality results in a field is “haphazard”, whereas placing a want-ad, or collaborating with one’s online chat buddies, is systematic? Yikes.

We have PubMed, which allows us to rapidly identify others working on research areas important to us. In many cases, we can go to RePORT to find out what government grants they are receiving.

The NIH site, as described, also fails to recognize that researchers will only do this if it helps their workflow or provides them a tool that they have no other way to use. Facebook is really a place for people to make online connections with others, people one would have no other way to actually find.

But we can already find many of the people we would need to connect to. What will a scientific Facebook have that would make it worthwhile?

Most social networking tools initially provide something of great usefulness to the individual. Bookmarking services, like CiteULike, allow you to access/sync your references from any computer. Once someone begins using it for this purpose, the added uses from social networking (such as finding other sites using the bookmarks of others) becomes apparent.

For researchers to use such an online resource, it has to provide them new tools. Approaches, like the ones being used by Mendeley or Connotea, make managing references and papers easier. Dealing with papers and references can be a little tricky, making a good reference manager very useful.

Now, I use a specific application to accomplish this, which allows me to also insert references into papers, as well as keep track of new papers that are published. Having something similar online, allowing me access from any computer, might be useful, especially if it allowed access from anywhere, such as my iPhone while at a conference.

If enough people were using such an online application then there could be added Web 2.0 approaches that could then be used to enhance the tools. Perhaps this would supercharge the careful reviews that David mentions, allowing us to find things or people that we could not do otherwise.

There are still a lot of caveats in there, because I am not really convinced yet that having all my references online really helps me. So the Web 2.0 aspects do not really matter much.

People may have altruistic urges, the need to help the group. But researchers do not take up these tools because they want to help the scientific community. They take them up because they help the researcher get work done.

Nothing mentioned about the NIH site indicates that it has anything that I currently lack.

Show me how an online social networking tool will get my work done faster/better, in ways that I can not accomplish now. Those will be the sites that succeed.

[UPDATE: Here is post with more detail on the possibilities.]

A very big challenge for biopharma

Loose coupling and biopharma:
[Via business|bytes|genes|molecules]

A few days ago, via the typical following of links that is typical of a good search and browse section on the interwebs, I chanced upon a discussion about a presentation given by Justin Gehtland at RailsConf. The talk was entitled Small Things, Loosely Joined, Written Fast and that title has been stuck in my head ever since. Funnily enough, what was in my head was not software, and web architectures, cause today, I consider that particular approach almost essential to building good applications and scalable infrastructures, and most people in the community seem to understand that (not sure about scientific programmers though). What I started thinking about was if that particular philosophy could be extended to the biopharma industry.

Without making direct analogies, but without suspending too much disbelief, one can imagine a world where drug development is not done in today’s model, but via a system consisting of a number of loosely coupled components that come together to combine cutting edge research and products (drugs) in a model that scales better and does a better, more efficient job of building and sustaining those products. One of the tenets of the loose coupling approach to scalable software and hardware is minimizing the risk of failure that is often a problem with more tightly coupled systems and in many ways the current blockbuster model is very much one where risk is not minimized and one failure along the path can result in the loss of millions of dollars. I have said in the past that by placing multiple smart bets, distributed collaborations and novel mechanisms (like a knowledge and technology exchange), we can reboot the biopharma industry, reducing costs and developer better drugs more efficiently. I don’t want to trivialize the challenge, the numerous ways in which the process can go wrong, and the vagaries of biology, but resiliency is a key design goal of high scale systems, and is one we need to build into the drug development process, one where the system chooses new paths when the original ones are blocked.

How could we build such a network model? I know folks like Stephen Friend have their ideas. Mine are ill formed, but data commons, distributed collaborations, and IP exchanges are a key component especially in an age where developing a drug is going to be a complex mix of disciplines, complex data sets and continuous pharmacavigilance. I can’t help but point to Matt Wood’s Into the Wonderful which does point to some of those concepts albeit from a computational perspective


Designing great and awesome tools for researchers to use will be critical for successful drug development. But there also has to be a cultural change in the researchers themselves and the organizations they inhabit.

One is that the tools have to work the way scientists need them to, not what works well for developers. This is actually pretty easy now and many tools are really starting to reflect the world views of researchers in biotech, who, more times that expected, are somewhat technophobic.

This leads to the second area- researchers often need active facilitation in order to take up these sorts of tools. They need someone they trust to actually help convince them why they should change their workflows. Most will not just try something new unless they can see clear benefits.

Finally, the last thing is better training for collaborative projects. Most of our higher education efforts for training researchers makes them less collaborative. They are taught to get publications for themselves in order to gain tenure. Plus, with the competition seen in science, letting others know about your work before publication can often be harmful Large labs with many people often can quickly catch up to a smaller lab and its work.

Like in the business world, being first to accomplish something can be overtaken by a larger organization. So, many researchers are trained to keep things close to the vest until they have drained as much reputation as possible form the work.

But many of the difficult problems today can not be solved by even a large lab. It can require a huge effort by multiple collaborators. Thus, there is a movement towards figuring out how to deal with this and assign credit.

Nature just published a paper by the Polymath Project, an open science approach to the discovery of an important math problem. They addressed the problem of authorship and reputation:

The process raises questions about authorship: it is difficult to set a hard-and-fast bar for authorship without causing contention or discouraging participation. What credit should be given to contributors with just a single insightful contribution, or to a contributor who is prolific but not insightful? As a provisional solution, the project is signing papers with a group pseudonym, ‘DHJ Polymath’, and a link to the full working record. One advantage of Polymath-style collaborations is that because all contributions are out in the open, it is transparent what any given person contributed. If it is necessary to assess the achievements of a Polymath contributor, then this may be done primarily through letters of recommendation, as is done already in particle physics, where papers can have hundreds of authors.

We need to come up with better ways to design useful metrics for those that contribute to such large projects. Researchers need to know they will get credit for their work. As we do this, we need to also help train them for better collaborative work, because that is probably what most of them will be doing.

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