Hilgartner, Genomics
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Hilgartner, Genomics

SPEAKER: This is a production
of Cornell University Library. STEPHEN HILGARTNER:
What I’m going to do today is try to
give you a little overview of this actually rather
complicated book, Reordering Life– Knowledge and Control in
the Genomics Revolution. And this– I’m going
to try to give you an overview of the approach and
the argument behind the book. And then I’d be very interested
in your questions and comments at the end. So in the mid 1980s,
a scientific vanguard of elite scientists from
the United States and Europe emerged, and they had the
explicit aim of revolutionizing the biological sciences. They sought to make genomes. That is the totality
of an organism’s DNA into a tractable unit
of scientific study– not pieces of DNA, but the
totality of them, and make genomes comparable, so you
could analyze differences across different species. You could analyze differences
among human beings and so on. And they wanted, as well,
to find all of the genes once and for all and
create a new biology for the 21st century based
on computational methods to a significant extent. And their vision of transforming
the sciences of life was most dramatically
crystallized in the proposal to do the Human Genome Project. And the Human Genome Project,
which would map and sequence the entire human DNA,
soon captured imagination, and it won a major financial
commitment from the US Congress, $3
billion, to be spent over a period of 15 years. And the official start date
of the Human Genome Project is 1990, and by the year
2000, President Clinton linked, by satellite, to
British Prime Minister Tony Blair, conducted a
celebration of the completion of the first survey
of the human genome in a joint news conference. By that time, genomic
knowledge and technology had become indispensable to
biological research in lots of fields to biotechnology and
to the pharmaceutical industry. And as the field of
genomics took place, significant change took place
in the scientific community and beyond. And that’s kind of
what the book is about. New factory-style
laboratories emerged that took shape and
differed dramatically from what was being
done in the benchtop craftwork of molecular
biology before that. And these labs
didn’t fit very well with the patterns of
careers and the ways of doing business that were
familiar to the molecular biology community. As the project began to
take shape at the outset, conflicting visions of how
you should orchestrate it, how you should
coordinate this activity, which was not going to be
conducted in one single location– it was going to
be conducted in laboratories throughout the world– how many and things like
that was up for grabs. And a lot of people wanted
pieces of this $3 billion and to be part of this
thing, as you can imagine. So you had to figure out
how to coordinate it. The public DNA
sequence databases, as the project started to spit
out huge amounts of sequence, which had been established at
the beginning of the 1980s, suddenly found themselves
with exponentially growing quantities of data. This actually had begun a little
before the Genome Project, but the quantities of
data were exploding, and this raised questions about
how the relationships that had already been established
between laboratories and scientific journals
and these databases should be reordered to make
it possible to manage this. A new wave of
biotechnology companies formed that were built
around the vision of genomic information
in the form of capital. And the prospect of a revolution
in biological sciences led to concerns and debate
about ethical, legal, and social issues. So all of this is taking place,
and the scientific vanguard kind of set forth
them very clear goals at the beginning of the
project, which included these, and there were few other ones. But they intended to
sequence the genome of the human in a number of
different model organisms. They would call them like
mice and yeast and drosophila and so forth. But at this time,
sequencing the whole genome was basically beyond the
capability of any laboratory or any assemblage
of laboratories. The first thing was to
generate some rudimentary maps of whole genomes,
which had to be done, and then to sequence it later. Their goal also was
to put all of the data into these public databases
that could be accessed by anyone anywhere, like
GenBank, and they also needed to develop the technology
for accomplishing their goals. And they promised to do this
in 15 years for $3 billion, and then, through this
process, transform the way biological research was done. Now this is a diverse audience. So let me just say that, what’s
it mean to sequence a genome? The idea– I’m going
to be very brief. This will not get
very technical. But DNA is represented
as a double helix with these pairs of what
are known as bases– the A’s, T’s, C’s, and G’s. They always pair the A
to the T, the C to the G. So the two strands of those
DNA, if they separate each one, contains all the information
needed to replicate it. And that’s how
organisms can replicate. And to sequence a genome
means to produce text like this, which has, by making
measurements of the genome, you’re able to represent
it in a text that can be stored in a database
and analyzed computationally using computers and so on. And sequences measured
in base pairs– so this is, I think,
21 base pairs long, something like that. The human genome is about
3 billion base pairs long, and so that’ll give you
a sense of the scale. This was an extremely
ambitious project. The existing technology
wasn’t up to the job. In 1988, which was the year
that this political support in the US crystallized
to do the project, sequencing DNA was
tedious, slow, craftwork. Often, there were failures. And genome mapping was
progressing very slowly. The first automated
sequencing machines were just about to come
online, and they came online a little later. But they didn’t really
automate much of the work. They automated some of it, but
there was all this front end craftwork that had to be
done, and then back end work that had to be done
after you got the data out of the machine. At this time, it was very
easy, relatively easy, to sequence a
short piece of DNA, like 500 base pairs or
something like that. But to sequence longer strips
was much, much more difficult. And the largest
continuous piece of DNA that had been sequenced in 1988
was 150,000 base pairs long, when a genome is 3 billion. It gives you a sense of
how far there was to go. OK, there were other problems. There were challenges of
coordinating that I’ve already mentioned, and there was also
controversy about the project and some opposition to it. And the most sustained
opposition, interestingly, came from biologists, who
were worried about the effects that the project would have
on the way they performed their work. It would maybe
concentrate money. It would damage
training of students. It would cause lots of trouble. OK, so– and there
were also critics who were worried about ethical,
legal, and social issues. OK, so Reordering
Life is a study of the rise of genomics during
the Human Genome Project in the period of 1988 to 2003. And the sources that I used are
ethnographic and interviewing. I observed these laboratories. I went to the world meetings on
the subject at the Cold Spring Harbor Laboratory
and in other places. I also used documents. Most of the fieldwork
was conducted while the project was going on,
but I did follow up fieldwork after 1993 as well– 2003 as well. And during this period,
scientists and others were debating the question of,
what kinds of accountability should govern the production
and use of genomic knowledge? Who should own
genomic knowledge? And what should it mean to own
a genome or a piece of a genome? These issues are
still with us today, but they were debating them,
and this was an early stage and an instructive one. How should translaboratory
collaborations be orchestrated? And how should researchers deal
with the public and the news media and things
of that sort, were all issues that were in play. So what I do in the book is I
look at genomics and the HTP, in part, to understand the Human
Genome Project in that field, but also to try to understand
how new forms of knowledge and new forms of control over
knowledge and over people and other things
take shape together during the process
of scientific change. So that’s the broader
agenda, and my argument is that some of the
approaches and things that are taken in this
book could apply to other areas like
information technology and so forth, if
genomics isn’t already an information technology. OK, so the sort of essential
questions of the study– during the rise
of genomics, what adjustments took place
in the regimes that govern biological research? And how did actors contest
and reallocate rights, duties, privileges, and powers
among the various agents and entities that were
involved in this work? And what changed, what
stayed the same, and why? And in looking at control,
there’s three kinds of control that are significant
and that I focused on. One is control over what I’ll
call knowledge objects, which are entities that contain
knowledge, anything that contains knowledge– a book,
a scientific paper, any kind of written document could
be a knowledge object. But also, not just
finished work, but maybe preliminary data– maybe information like
a genome sequence. These are all things
from which knowledge can be extracted that could
be extremely valuable, say, in a race
between scientists who were trying to compete
for, say, finding a gene or something of that sort. Biomaterials also are an
example of a knowledge object, so it could be a sample of
DNA that was very valuable for some particular reason. Skilled personnel, techniques,
even in a fast-moving field like this, rumor and
speculation and scuttlebutt might be very important
kinds of knowledge objects. They might be very important to
know what someone else is up to or what they stopped
doing and so forth. I also consider control over
jurisdictions, interpreted broadly to include a variety
of different physical and sociopolitical and
discursive spaces into which agents and
capabilities can be mapped. So just as an example,
a jurisdiction might be the
scientist’s laboratory. And the lab head who is in
charge of that laboratory has authority. They’re an agent who has
authority over that laboratory and so forth. But it could also be the pages
of the Journal Nature, which an editor in an
editorial process has authority about deciding
what enters into that space. So I’m interested in
transfers of knowledge objects across jurisdictions,
and then finally, control of relationships–
so I basically take a relational
view of control. So for example, different
systems of control structure, the relations
between the agents. So they might allocate
rights and duties. So if somebody has a
right, someone else has a duty to do or
not to do something with respect to the right. If you have a right not to be
tortured by the secret police, the secret police have a
duty not to torture you. Rights always have this
kind of relational form, and I’m interested in how those
take shape and get changed– rights, duties,
powers, privileges, immunities, and so
on in this space. OK, so that’s pretty abstract. I will give you some much more
concrete examples, but first, a couple of points. These three types of
control are actually intertwined in the
actual activity. And so the process
that I’m interested in is a dynamic process through
which specific configurations of knowledge and control get
made, reproduced, and changed, or to use the terminology of my
field, science and technology studies, the
knowledge and control get co-produced as
the action unfolds in a space like genomics. Now the central concept that
I used to organize my account is the idea of a
knowledge control regime. And these are
law-like regimes that constitute a social
order that allocates control over knowledge. And the term “regime” gets used
in the social sciences a lot. It gets used to mean
a lot of things, but what sort of unifies
the term is that it always has to do with some kind of
system that imposes order over some type of activity. And that’s the way I’m using it. So what is a knowledge
control regime? Well, the best way to
describe it initially is to just give
you some examples. So I’ve got some
examples for you. One is national
security classification. Military classification
is a regime that divides the
world into two spaces. It creates a classified
space, which you’re not supposed to know anything
about, unless you’re specially authorized to learn
things about that space. And everything else–
the unclassified space, and it constitutes
agents, authorities in the national security system,
who decide what is classified and what’s not classified. So there’s that
kind of operation. So that’s what I
mean by a regime and having specific
agents, who engage in setting up jurisdictions and
have authorities and powers. Another example– trade
secrecy in commercial contexts. Another example–
the policies that are implemented by
places like Cornell and, actually, every
university in the United States about protection
of human subjects and management of confidential
human subject information. Now these three
regimes that I’ve just described you all have
to do with preventing the flow of information
into certain spaces, but the concept’s
broader than that. Knowledge control
regimes are also about making knowledge
public, so the regime that governs publication
in scientific journals is a knowledge
control regime that divides the world into the space
of the published literature, the unpublished literature,
and the under review stuff, and it constitutes
rules for the transfer of materials across that. And it endeavors to maintain
the quality of knowledge through that process. Another one, Creative
Commons, is intended to make knowledge available. But some knowledge
control regimes look really different from
what we’ve encountered before. So for example, the business
models of the new genomics companies, like the
company, 23andMe, is a good example of a
knowledge control regime, because it specifies a
set of rules governing what the company and other
actors can do with genomic data that they extract
from their customers. The traditions
and understandings that grant the head of a
molecular biology laboratory authority over the
activities of his or her lab is also a knowledge
control regime, as are agreements, like
this material transfer agreement, which
you can’t possibly see in the back, agreements
that specify rules for transferring materials and
things around laboratories. And even the public
relations strategies of organizations, who
seek to highlight and hide certain aspects of
their activities can be understood as a
knowledge control regime. So by now, you
should be wondering, wow, what is in a knowledge
controlled regime? They have a tremendous
variety, and some of them, some of the
ones I’ve described are formal, legally
codified ones. Others are informal and ad hoc. Some of them are
well-institutionalized and familiar, and others
are novel and emerging. And they’re used for
all kinds of purposes. They allocate
scientific authority. They distribute credit. They create property. They spread knowledge. They maintain privacy. They ensure quality. They protect national security. They save face. They construct
professional jurisdictions, and they shape public beliefs. So confronted with this
variety, you may be wondering– and it’s a completely
reasonable question– what possible utility could
a concept with so much internal variety have? How could you
possibly use something that covers so many different
kinds of activity and so on? And my– of course,
I have an answer. My answer is that the
concept’s unified by the way that knowledge
control regimes play a central role in the regulation
of the production, spread, and use of knowledge. It’s further unified by their
kind of law-like structure that they operate like
a system of rules. They may be formal or informal. They may be based on all
kinds of different things, but they operate
like a set of rules that constitute specific
means of controlling knowledge objects, disciplining
actors, and bringing order to specific situations. And secondly, beyond actually
having some internal unity, the second thing is that it
provides a framework that you can use to compare regimes. You can look at the differences
in how they operate, and you can look at the way
that they change over time. So the analysis in the
book is almost always comparing regimes or comparing
how the same regime has changed as it’s adjusted,
and having something that can manage the actual
variety of the phenomenon of controlling
knowledge gives you a tool to make those
kinds of comparisons work. OK, I can give you a definition. Here is one– a
sociotechnical arrangement that constitutes categories
of agents, spaces, objects, and relationships among
them in a manner that allocates entitlements
and burdens pertaining to knowledge. But while that’s a
formal definition, the concept is actually easier
to grasp with an analogy. And the analogy I want to use
is the constitution of a state, like the written
Constitution of the US or the unwritten
constitution of Britain. A knowledge control regime
establishes a system of governance, just
like the US Constitution establishes a set of– it constitutes a set of agents– citizens, the president,
the Supreme Court, you can name them– who have certain kinds of powers
and certain kinds of rights that are specified in the
Constitution in relation to one another. There are certain
privileges and immunities and so forth that they have. And they’re given jurisdictions. Congress can write laws. The Supreme Court can
interpret them and so forth. So they allocate rights,
duties, privileges, immunities, and powers, and they
allocate authority, control, and discretion. And that’s what knowledge
control regimes do, whether we’re talking about
journals, national security classification, or the
public relations strategy of a firm or a federal agency. OK, so knowledge control regimes
specify which agents have what kinds of control over
knowledge objects, over spaces, and over other agents. And with that concept
in mind, let’s talk about how the argument
develops in the book. OK, so the book
is– first of all, it’s loosely chronological. It follows how the Genome
Project evolved over time. And it begins with
the envisioning of a revolution in science. It starts with the
scientists articulating the vision and the problems that
they would have realizing it. And then it ends with efforts
to shape the media coverage of the final endgame
of the project and to create an
exciting historical event with prime ministers and
presidents celebrating this activity. In between, the book’s
organized around the way that struggles for control
took place in different sites. So it begins with laboratories
at the very, very outset, and even a little before
the beginning of the Genome Project, and looks
at the knowledge control regimes that were
operating in laboratories as they decided,
for example, what to export from their
laboratory, what to import from their
laboratory, when to provide, to share
data with other people, when not to do so. And so this selective
revelation and concealment of data that’s
described there was something which the
early policymakers, who were trying to execute
this ambitious project, were worried about. They looked at the kind
of selective release and sharp business
practices that were going on in human
genetics laboratories in an area of
intense competition. And they said, you know, this
is not going to work so well. We need to get people
cooperating more. We need to create some kind of
new regime to run this project, or it’s not going to work. And so they came up with
different ideas about how to do this, and they came up
with different ideas about how to do this in
different countries, including the United States,
Britain, France, and so forth. And this chapter is a
comparative analysis of several of these
regimes, one of which, quite ambitious but
failed, and another of which became the central
regime that actually was used to conduct the project. The next chapter,
Chapter 5, which I’ll talk about in some
more detail in a minute, traces the history of a set of
important knowledge objects. And so it follows these objects
through different regimes, and it looks at how the
objects were incorporated into new kinds of regimes that
hadn’t been constructed before, and new objects were
created in this process. You’ll see more about
that in a minute. The next chapter looks
at regimes bumped up against each other. So just as in governments,
states, borders, where are areas of
particular tension. And in areas of
emerging technology, the border lines are indistinct. It looks more like the
Middle East in 2017 and ’18 than an orderly
border like the US and Canada or something. People are fighting
about where the border lines of jurisdictions
should be, and this chapter looks at
how the rise of genomics databases destabilized relations
between laboratories, journals, and the databases themselves
and how a series of regimes were produced and
failed, and new ones were created to manage that process. And then, finally,
we end up at the end of the Genome Project looking at
the making of history and news coverage. OK, so at this point, this all
must sound incredibly abstract. And so let me give you an
example of how the argument works in a specific case. And so what I’m going to do is
take you on a whirlwind trip through Chapter 5. And so Chapter 5 is the one
that follows a set of objects, and the objects
involved are known as partial cDNA sequences. Now that’s a mouthful, so just
hold onto that for a minute. What I want to do and
what I do in the chapter is I look at how
they’re envisioned as knowledge objects. I look at how they get
incorporated into a succession of different regimes,
and I look at how being incorporated into those
regimes changes the objects, and new regimes get produced,
and lots of kinds of control are contested,
and it all happens with lots and lots of
money being involved in a very dramatic way. OK, so what is a cDNA, and
what is a partial cDNA? Well, not to get too technical–
we can talk about this more, but you can say that a cDNA
sequence is a sequence that comes from a gene. Genes code for proteins. They’re important. They’re what shape what we
end up being as organisms. So a cDNA is a sequence
that comes from genes. cDNA stands for
complementary DNA. Let’s not go there. But so what is a
partial cDNA sequence? Well, I have one here. That’s what one looks like. A partial cDNA sequence is a
piece of an entire full cDNA sequence. The full cDNA sequence
would describe the gene in its entirety,
and the partial one is a little chunk of that. And the partial ones are all
about round figures, 500 base pairs long. This one is 308 base pairs, and
it comes from the human uterus. It’s expressed in
the human uterus. That’s where. And the key thing to know is
that the partial cDNA sequence is much, much smaller,
often, than the– it’s much, much
easier to produce, and it’s much smaller than
the full cDNA sequence, especially at this time. So genes vary by several
orders of magnitude in size, but these things
are all about 500. So you could have a gene
that’s 100,000 base pairs, and 308 doesn’t look
like a lot of that. So it’s a small thing. Now at the outset of
the Genome Project, people are worried about
how is this thing going to wreck biology? How much money is
it going to cost? Is it going to
centralize activity? Is it going to draw
work away from more fun and interesting projects? Is it going to
reduce creativity? There’s a lot of
opposition to the project, and one of the debates that
happened in the science policy community is, should we
sequence the entire genome, all 3 billion, or should
we just sequence the genes? And it turns out that the
genes themselves only represent a small percentage, just a
few percent, of the genome. And this is known at this time. And so in the US,
it’s been decided, we’re going to sequence
the whole thing. But in Britain, this
guy, Sydney Brenner, who’s a Nobel
Prize-winning biologist, and others argued that
that makes no sense. If it’s hard to sequence,
let’s sequence the genes. They’re the interesting bits. Let’s sequence the
genes, and maybe someday when sequencing
gets cheaper, maybe we’ll possibly
sequence the rest of it, if it’s interesting. But why now? And so there’s a debate
about what to do. And the sort of way that
Brenner and people like that are thinking
about it is the genome can be divided into
two categories– genes and junk. Why sequence the junk? No point. Whereas the Americans, the
ones who control the project, are saying, let’s
sequence the entire thing. We don’t know it’s junk. We want to make the totality of
the genome available to study. And maybe it’ll
turn out to be junk. Maybe it won’t. Let’s wait and see. So that debate is going on. And while that debate
is going on, remember, it’s pretty easy to make
these partial cDNA sequences. It’s harder, quite a lot harder
to do a full cDNA sequence. So how do people look? What do they think,
as a knowledge object, a partial cDNA sequence is? Well, basically, both
sides in the debate think it’s pretty uninteresting. If you want to
sequence all the genes, you don’t want to sequence
part of all the genes. You want to sequence
all of all the genes. And if you want to
sequence the entire genome, you don’t want to
sequence part of anything. You want to sequence
the whole thing. So they’re just fragments. They’re not interesting. OK, well, that changes. In 1990 and 1991, the now
very famous genome scientist, Craig Venter, pictured
here in his autobiography– he’s also a boat
racer, so that’s why he’s in a sailing outfit. He does long distance
ocean racing. He likes races a lot. OK, so Venter reimagines
what the knowledge object of the partial
cDNA sequence is. He now says, wait a second. This thing can be
thought of as what he calls an expressed sequence
tag, which means it’s a tag– It could be thought of as
a little tag that points to a gene, like an index. Kind of like in a search engine,
you put in a string of text, and bang, back comes things that
contain that string of text. It’s very, very much, actually,
like early search engines before they got
more sophisticated, which were basically based
on some kind of text matching kind of thing, initially. So Venter starts doing this. And he produces a whole bunch
of ESTs from human brain tissue, because he works for the
National Institute of Mental Health Research. And he publishes a
paper, and the reason he’s excited is he takes
these genes, and he uses them, uses these tags– they’re not genes. They’re tags. He uses these EST tags to
search through GenBank, which has lots of genes from
lots of organisms in them. And he looks for matches,
and he finds matches. Some of the matches
are from yeast, because there are
genes that are in us that are very much like
genes that are in yeast. And a lot of the molecular
biology of the human organism looks like other organisms. You know, they’re
evolutionarily conserved. It’s one of the best
kinds of evidence in favor of evolutionary theory. And some of them look like mice,
and some of them– and so on. So when he finds a
match, he’s happy, because when he
finds a match, he says, well, this human gene
that this tag points to– well, now we can
guess about what it does, because if we know
what it does in the mouse, now we know what it
does maybe in the human. It gives us information
that’s valuable. But when he doesn’t
find a match, he’s also happy, because then he
says, I have found a new gene, a new gene. He hasn’t really found a gene. He’s found a tag that points
to a gene that hasn’t yet been identified. He doesn’t know where the
gene is in the genome. He doesn’t know how large it is. He doesn’t know what it does. He doesn’t know much
about it, but it’s new. Venter encounters
a lawyer, who works for National
Institutes of Health in the Technology Transfer
Office, named Reed Adler. Adler says, you know,
maybe these are patentable, these tags. And maybe if we
patent the tag, we aren’t just patenting the tag. Maybe we can patent the
gene that the tag points to, and even the protein that
the gene codes for, and even antibodies to that protein. So all of a sudden, the
tags look like a way to find new genes, as
Venter is putting it, but not only to find
them, but to own them. So he files. NIH files for
patents on 377 genes. Now at the time, people
are searching for genes, like the gene for
Huntington’s disease. The gene for cystic fibrosis
was just found in ’89. Huntington’s disease doesn’t
get found until 1994. They began working on
it a decade before that. Labs throughout the
world working on this to find one gene and
actually find the whole gene, and cut it out and clone it
and sequence it and so on. To find one gene would
take maybe years, 10 years in the case of
Huntington’s disease, one of the first that
they started working on. So the idea that you
could patent 377 genes– this looked really
revolutionary. And not long after
the 377, the NIH files for patents on 2,375 genes. So we’re talking
about a lot of genes compared to what other
people are doing, and furthermore, if you
know the field at this time, and you pull out a
pen and an envelope, you can calculate that, wow,
if somebody starts spending a little bit of money on this– we’re talking tens of millions– they could maybe own most of
the genome in a couple of years. Well, venture capitalists are
known for their intelligence about these kinds of things,
and they smelled opportunities. And all of a sudden, genomics,
which previously looked to them like not a very productive
thing to invest in, looked like a real
business opportunity. And new business models
started taking shape, and those business models
instituted new knowledge control regimes. Now at the same time, there
are several big questions. Are these patents
going to hold up? These patent applications
are being made, but the patents haven’t issued. So are they going to hold up? And lawyers are speculating
wildly about whether or not they will. And it’s a very
controversial legal theory, but the other question
is if they do hold up, what is the NIH going
to do with the patents? And you can imagine various
things that they can do. One of them is they
could patent everything, and they could issue
low cost licenses to anyone who wants a license. That’s one knowledge control
regime they could build. And anyone, anywhere could,
for a very minimal fee, proceed to work with that gene. Or– and the British and
French were particularly incensed about
this possibility– they could license them
in ways that would benefit US companies, or
they could set up an entity kind of like
the Federal Communications Commission that would–
the Federal Communications Commission regulates
access to the airwaves. What if they said– and some people
thought they should– you can work on this gene,
and you work on that gene, and we won’t have
duplication of effort. And if you don’t do
a good enough job, we’ll pull your
license, and we’re going to give it
to somebody else. They could do that. So an international
and national debate starts happening
about these patents. Europe opposes the EST patents. The UK starts
sequencing lots of ESTs and filing
counterpatents on them, which they say they’ll
throw in a bonfire if the US withdraws its patent. The French are saying, everyone,
curse on all your houses. And this is what’s going on. Now while that’s
happening, the USPTO, the Patent and Trademark Office,
rejects the NIH’s first 377 patents. An NIH director that Bush– first George Bush–
appointee actually decides to appeal the patents,
appeal the patent decision. And then they also
file new patents, which they’ve
restructured to make them look legally stronger. So all that’s going
on, and the British are getting more and more mad. But meanwhile, a
new knowledge object arises in the venture
capital world. And this is the idea of a
proprietary EST database. This company, Human Genome
Sciences, which is founded, among others, by Craig
Venter, but the money came from a big VC. They build a large collection
of ESTs in a database, and then now they’re
going to control access to that database, kind of– they’re not going to
sell subscriptions, or it doesn’t look like
they’re going to do that. They’re going to control
access to the database and make money back
that way somehow. No one knows how they’re going
to make the money, actually, at this stage, and
there’s wild speculation among the most
informed scientists in the world about
what’s going on, who don’t actually understand
what their business model is going to be. Well, it turns out that
what they decided to do was to create a new knowledge
control regime, which I call the HGS Nexus. And the idea here is say
you’re a university biologist, and you are hunting
for a gene, and you’re in a race with
other people maybe. You care a lot about
getting results fast. Well, you could send
some of your material through there to HGS,
some of your biomaterial, and HGS would screen it
against this database and tell you everything that
they found out about it. And you’d learn a lot, and
it might be very valuable and help you advance your
research and race with whoever you’re racing with and so forth. But there’d be a little catch. You would need to give, first– a right of first refusal on
all intellectual property connected to your research
to Human Genome Sciences. And Human Genome Sciences
had a similar arrangement with the pharmaceutical
giant, SmithKline Beecham, which had a right of
first refusal on anything that HGS did. So what happened was that it
was a way for taking researchers in the universities
and translating them through this database
so that they became a source of
intellectual property for SmithKline
Beecham in particular, not for any of the other
competing pharmaceutical firms that might be interested in
buying up whatever Interesting stuff they found as they do. So the university researcher’s
identity, in a sense, is being restructured. When they enter
into this agreement, they become part of this
Nexus, and their work gets channeled to
SmithKline Beecham. Well, you can imagine Merck
didn’t think much of this. So Merck invents another
knowledge object. They see it as a threat,
and what they come up with is the idea of turning partial
cDNA sequences, or ESTs, into a public resource. They come up– they fund Human
Genome Project laboratories to produce lots of ESTs and
put them in the public domain. It’s the first privately
funded public genome database, and it’s done as a counter move
to prevent SmithKline Beecham from taking a lead in the
application of genomics to pharmaceutical development. Well, as things go,
actually, it turns out that the boosters of ESTs have
overestimated their interest in value. And there’s some technical
reasons for that. I won’t go into it, but
think of it like this. Pretty soon, there’s– you know,
there’s the best that Merck is funding. There’s two companies,
Human Genome Sciences and another one, that have
produced lots of ESTs, and now there’s hundreds
and hundreds of thousands of ESTs in these databases. And yet they’re
only expected to be like 50,000 to 100,000 human
genes, probably more like 50, they’re thinking at this point. And it’s actually fewer
than that now, they know. So that must mean that
each EST is pointing not to a unique gene, but that
there’s a whole bunch of ESTs pointing to that. The value of each database
starts to drop dramatically, because they’re
full of redundancy. Think of having an
index to a book that has 25 entries to the
same page all listed as if they were the same thing. It just stops being a very
useful index pretty fast. So it’s not that
it has no value. It can tell you some
interesting stuff, but people were
kind of disappointed who had thought that they
were going to be the solution. And so these ESTs
and the partial DNA sequences end up being viewed
as kind of an ordinary tool, just another part of the
molecular biology toolkit. So look at all those
transformations and the different
knowledge objects that were produced, the different
systems of control that took place. It started out being
only a fragment, and then it’s a tool
for indexing genes, and then it’s a potentially
patentable tool. And then it’s a tool for
not only patenting, but also for patenting most genes fast. And then it’s maybe a tool for
making the NIH into a genome Federal Communications
Commission, or it’s a part of a
proprietary database. It’s a way of sort of tying,
by a series of contracts, university researchers
to SmithKline Beecham. But then it’s a threat to
another pharmaceutical giant, and then it’s a component
in a public EST database. And then it’s a
tool that doesn’t work as well as it was
originally thought, and finally, it’s an
ordinary tool, which is still useful, but
not revolutionary and not going to change who
owns the genome very fast. That all happens in six years. This gives you a sense of
the pace of what’s going on. And during this process,
a lot of things happen. The idea of a genomics
company gets invented. The idea of raw sequence
information being a form of capital gets developed. During this process,
what if partial DNA changes dramatically? And obviously, the array
of those 308 base pairs that I showed you
at the beginning remain the same
during that process. But what that partial cDNA is,
what it means, what it can do– that changes dramatically. So in most senses, in
most important senses, they become completely
different objects as this process takes place. They can do completely
different things, or were thought to be able to
do completely different things. Now despite all
this change, it’s important also to stress
that the change was measured, that EST patents didn’t
end up allocating authority over who could– the majority of control over
the majority of human genes. The FCC didn’t become– the NIH did not become
an FCC-like entity. Human Genome Sciences didn’t
entangle large numbers of academic scientists in
its nexus, though it tried, and it did get some. SmithKline didn’t gain
long-term advantages over Merck and other competing
pharmaceutical firms. The Patent and Trademark
Office denied the patents claiming full length genes
on the basis of tags. So all of the most
radical changes that people were worrying
about at the time didn’t actually come to pass. The sort of obduracy
of the existing order is also demonstrated
by this account. So that’s a real whirlwind
tour of Chapter 5, and I hope that this
kind of rapid account of the kind of thing
that the book does gives you a sense of
how the analysis works. And now I want to end
with a few conclusions from the book as a whole. So first, I want to argue– the book concludes that
the epistemic problem of securing knowledge and
the sociopolitical problem of securing control are
deeply and even inseparably intertwined, that in
transformational scientific change, like you get
in an area like this, things become up for grabs. And it’s possible for people
to attempt to capture a lot. And the actors who are
there on the ground are positioned well
to attempt to do that. Secondly, knowledge
objects always take shape within specific
knowledge control regimes. They’re always in some
sort of jurisdiction from the beginning. But once they’re in
that jurisdiction, people can change
the– you know, they can try to stretch and
change and alter the regimes or build new ones. Further, I argue that
control relations don’t just surround knowledge
objects, but they actively get built into the objects. Some of these
objects wouldn’t even be put together if
there wasn’t control to be yielded by doing it– by doing that. Existing regimes get
adjusted, and new regimes are constituted as new forms
of knowledge take shape. But substantial change in
knowledge control regimes is most likely to occur
in particular conditions– first of all, when the changes
are consistent with prevailing cultural forms, and
the extant regimes that already are operative. Secondly, when the changes
don’t increase burdens on those who can
influence regime success, so Merck, being a wealthy
pharmaceutical company, is able to come up with a few
million, or tens of millions, to take this down. Changes, also, that don’t
require negotiations at points of regime contact also
are more likely to take place. If you can do it yourself
in your own space, it’s more likely to take place. And finally, those
who seek to understand the dynamics of power in
contemporary societies, where new knowledge and
technology is taking shape constantly in fields like
information technology and nano and genomics and
artificial intelligence and lots of other ways. People who want to
understand how power works in those societies can’t afford
to ignore knowledge control regimes and the
informal practices through which knowledge
and controls take shape. If the promoters of emerging
science and technology not only create
knowledge, but also constitute agents
and relationships. If they not only map genomes,
but also redraw jurisdictions, if they not only produce
information, but also allocate power over the direction
of sociotechnical change, then we just can’t assume
that innovation is a rising tide that raises all boats. People who are
close to the process can decide which boats
to raise, to some extent. This is not to say they have
absolute power to do this, but they can to some extent. So if knowledge and
control are co-produced, as I’m arguing that they are,
then understanding societies today requires recognizing
scientific vanguards, who champion scientific
revolutions as political actors and understanding
them in those terms. Thanks for your attention. [APPLAUSE] AUDIENCE: Steve, I wonder
how the knowledge control regime of the university,
either here or anywhere, has been changed by the
Human Genome Project. STEPHEN HILGARTNER: Yeah, I
mean that’s a really interesting question. My sense is that
there’s been a– over the past 30, maybe
almost to 40 years, there’s been a shift, a way
from viewing the university as a site that produces
knowledge and injects it into the public domain
or trains people and sends them off
into the workforce to include much more
attention as well to commercializing technology. The Genome Project
isn’t driving that. That’s already well
under way by 1988. It continues– the rules become,
broadly speaking, increasingly relaxed about commercial
involvements and things like that. So it’s not like this created
a change at that level. But what it did do, as the
example of the HGS Nexus suggests, is the university
is at a place, where, because scientists are entering
into all kinds of intellectual property arrangements and
things like that with firms, it means that if you can come
up with a clever way to entice and tie university
scientists into your system, like that was intended
to be– the HGS Nexus– then you can sort
of alter the way that university
science gets moved into the world in the
domain you’re operating in. But did the Genome Project
change science overall? Certainly, it changes
as well the ways that people do certain
kinds of research. The computer is
much more involved in genetics and genomics
and biology of all kinds than it was prior to
the time when sequences were widely available. So the way people do
their work is different. The kinds of skills that
are required are different, so there are lots of changes
of that nature as well. There also are the
formation of new entities that wouldn’t necessarily exist. It’s hard to prove
that, but that probably wouldn’t exist that
are constructed by people who were very
effective in the genome world. So for example, the
Broad Institute, which is a collaborative
effort of MIT and Harvard– that is headed by
somebody who ran an important American genome
lab, who also kind of helped orchestrate the
creation of that entity. And it’s become a very important
place for this kind of science, so it gets– these kinds of arrangements
are also being built. So in a lot of ways,
it changes things. But I think you have to look
in a little bit of detail at the specifics of the
particular university and things like that to just
say more than the sort of thing I’ve just told you. Yeah, the answer I’ve given you. Yeah. AUDIENCE: Thanks, Steve. I had two questions. First one is related
with the previous one. I was wondering how the
knowledge control regime and its sub-elements
you’re talking about here– how do knowledge control
regimes affect other ones? Are they like
historical precedents? Do they form
historical precedents? Like you mentioned
Broad Institute. I was going to say,
recently, there was another court decision
about the CRISPR patent case. For example, if 2000s
is the age of genome and the NIH versus Craig
Venter kind of rivalry, now CRISPR case
could be considered as like the 2010s story,
at least one of them. So how do these
knowledge control regimes affect other ones, if they do? And the second one– my second question is, you
put an emphasis on the word “control,” and I was
looking at the antonyms, the opposite words
for control, which is like disobedience, chaos,
mismanagement, and so on. So what are some moments
of light and these moments of resistance and
disobedience in the stories you’re talking about? And how do the actors deal with
key moments of loss of control and so on? STEPHEN HILGARTNER: Yeah, OK. Let me answer the
second question first. So I laid out the
regimes as they’re expected to operate by the
designers of the regimes and sort of that kind of a
story in what I told you today. But as you’re suggesting and
surmising, a lot of the stories have to do with people trying to
escape the control of regimes. And I have lots of examples,
so is there leakage? Do people try to stretch
the rules of the regime? Maybe you can’t break the
rule, but you can bend it. And sometimes you can break it. And those things happen. So the actors involved– the regimes just lay
out a set of rules, but we know that rules
are broken all the time, and this is very much
a part of the story. As to the first question,
how did the regimes interact, and did they have a
historical process, yeah, they definitely have
a historical process. You can think of them
having an internal dynamic, but they’re also being touched
on by the ones around them. Chapter 6 gives a specific
account of that with respect to the laboratory regime
and what scientists were doing in their laboratory
with their sequences, the journal regime,
and the databases, the public databases
like GenBank. And what you see is, in
a short period of time, the regimes get destabilized,
and a new regime has to take shape, which
requires negotiations across the regimes. And the part of the
question is, which regime is going to be changed in two or– bumping up against each other? So that kind of thing happens,
a dynamic between regimes, and I tried to give an example
of how that works in Chapter 6. And I think that happens
much more generally, but that’s the case
I have the data on. Yeah. AUDIENCE: I can remember there
was a lot of consternation about the $3 billion
investment in the project, and you spoke to that. I’d be curious if your
analysis leads you to or how your analysis
leads you to appraise the significance of that
and the rationale behind it, given some of the things you’ve
identified with boosterism, but then disappointments
that happened. How do you think about
that now, given what your investigation [INAUDIBLE]? STEPHEN HILGARTNER:
Well, I would say that people are not
fantastic at forecasting the direction of
where things are going to go with these kinds
of emerging technologies. I don’t terribly
much blame them. I think it’s really difficult.
But for example, one of the– how hard is it to
sequence the human genome? At the beginning, it
really looks hard. Some developments happen
that make it easier, but it still is a
tough task, but it’s getting progressively
easier, and it’s gotten way easier since then. And it costs $3 billion
to sequence one genome, and now you can sequence
the genome for $1,000. It’s– the decline in cost
is faster than Moore’s law in computing. So we’re talking
about a major change. People didn’t see that coming. There were people,
leading scientists central to the project, who were
saying, we can’t possibly do this project
with the technology that was in use at the
time, gel electrophoresis. But it was done with
gel electrophoresis. And that was the
majority view is we’re going to
invent something new, and it’s going to do the job. And that’s not what
happened either. Now you wouldn’t do it with
gel electrophoresis anymore. But it was after that, so
how fast some of the changes took place, people were wrong
about in both directions. You know, they thought
some things were going to take too
long, and some things were going to go too fast. They also were
surprised repeatedly by the kinds of
conflicts that broke out. No one was expecting
partial cDNA sequences, which was so
uninteresting, to produce the kind of dramatic story
that I just told you. So in that sense, people
aren’t very good at forecasting these things. Brenner doesn’t look like
his idea of sequencing just the cDNA was a good idea. If you want genome
sequence data, it turns out to be easier
to sequence the whole thing. I think that was pretty
clearly established. So if what you’re saying is– what you’re asking is,
were the biologists who opposed it right or
wrong, that kind of depends on what you think
biologists should be doing and what their careers
should look like and what the profession should
be and things like that. But if you’re talking about,
was this a cost effective way to produce this sequence, it
turns out it was pretty good. AUDIENCE: Hi, thank you. So my question is to build on
the debate between to sequence the whole genome or cDNA,
because during my fieldwork, I encountered the same
kind of debate or argument between whether to
sequence the whole genome or just the whole axome. Axome is the coding
protein of the genome. It has to contain the
protein information. So some scientists argue
that I want the whole genome, because I want all information. But some argue that
the whole genome is not that helpful to knowledge,
because you don’t know how to interpret that information. So it actually is better,
and sometimes when you don’t know how to
integrate information, it could be harmful when
you make treatment decision. So I guess my question is,
how does the knowledge control regimes, the concept
assess or evaluate the nature or the implication
of the knowledge itself? STEPHEN HILGARTNER: Yeah, so
the context you’re talking about is clinical sequencing, right? So people are sequencing the
genomes of individual patients in order to make
a better diagnosis and possibly identify
pharmaceutical approaches that would benefit that patient. We’re probably talking
about cancer patients. So that’s a very
specific context, one where the goals are centered
on helping that patient. But it may also be that
the scientists, who are saying sequence morbid, are
interested in gathering data that can be used to analyze,
not to help that patient, but for the future. And so the tension
is about that, and you can imagine one kind of
knowledge control regime that would be focused on benefiting
that patient at lowest cost, and another knowledge
control regime that would be focused on
benefiting that patient, but also producing
knowledge that would be injected into a
wider research activity, maybe at higher cost. And that kind of
attention could be analyzed as competing
regimes in the same space. I don’t know as much about
this area probably as you do, but that’s my read of it. SPEAKER: This has
been a production of Cornell University Library.

One thought on “Hilgartner, Genomics

  1. Great staging self review of "Reordering Life: Knowledge and Control in the Genomics Revolution" (MIT Press, 2017), and good questions by the audience. Tks for sharing!

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