Fukushima

ArticleValue Oscillation in Knowledge Infrastructure:
Observing its Dynamic in Japan’s Drug Discovery
Pipeline
Masato Fukushima
The School of Arts and Sciences, The University of Tokyo, Japan / maxiomjp@yahoo.co.jp

Abstract
This paper analyses the dynamics of assigned values in two cases relating to the knowledge
infrastructure of the national programme in Japan that develops drug discovery: in establishing a
database of natural product compounds and in constructing a library of virtual compounds. The
concepts of value oscillation and of the M-B (Marx-Bowker) index are proposed to designate thefluctuating appreciation of infrastructure value by its builders. These concepts combine insights fromclassical Marxist thought on the infrastructure/superstructure distinction (neglected in recent studies
on infrastructure in STS) and Bowker’s infrastructural inversion. Though value oscillation is almost
ubiquitous in the development of any infrastructure, in the cases considered here, it takes peculiar
forms because of the complex interaction of the material and knowledge infrastructures. It is widelydistributed in the sub-layers that support the autonomy of these knowledge infrastructures and is aprecondition for knowledge infrastructures to function as delineated entities.
Keywords: value, infrastructure, drug discovery, oscillation, structure

Introduction
Concepts are the ways through which we see
the world, and scholars have long realized thatapparent conceptual lucidity may hide winding
paths that can produce a variety of contradictory nuances, leading to persisting controversies.
Thus, academics from various fields have traced
the meandering former paths – including their
etymology – of such concepts as subject/object(Williams, 1976), liberalism (Hayek, 1982), society(Luhmann, 1980), and even ‘thing’ (Heidegger,
1968; Latour, 2005; cf. Fukushima, 2005).
From this perspective, the recent rise of
so-called ‘infrastructure studies’ in STS apparently

falls short of reflecting infrastructure’s conceptual
genealogy, while there are numerous concrete
examples analysing what infrastructures are andhave been. A brief review of foundational works
indicates that infrastructure is usually conceived
as a collection of such conventional prototypes as
roads, water conduits, and electricity; later, infrastructure took on new, extended meaning in termsof such phenomena as communication, information, and even knowledge (Star & Ruhleder, 1996;
Star & Bowker, 2002; Edwards et al., 2007; Bowker
et al., 2010). This approach to defining the subject,however, is plagued with historical amnesia in7

Science & Technology Studies 29(2)terms of its intellectual genealogy. The conceptof historical amnesia, which will become clearer
through the rest of this article, can briefly be
defined as blocking recourse to the proper legacyof the past for systematic reasons, often organizational, political, or even social (cf. Bowker & Star,
1999, ch. 8), in STS scholarship.
By way of demonstration, in 1978, Current
Anthropology published an article by Maurice
Godelier and colleagues titled ‘Infrastructures,
Societies, and History’ (Godelier et al., 1978).
Godelier is well known for his innovative endeavours to unify Marxist anthropology and French
structuralism (cf. Godelier, 2011). His paper was
intended to redefine the Marxist version of infrastructure to solve the evident contradiction
between societies in which such elements as
kinship or religious institutions seem to dominate
contra Marxists’ classical tenet that modes of
production determine other societal factors. At
stake here is the assumption of historical materialism – namely, that the base, or ‘infrastructure’, ofa given society is defined as the productive forces
and social relations of production that unilaterally
determine the rest of society, the superstructure(henceforth capitalized to represent their unitarycharacter; Marx, 1973). Large amounts of energy
have been expended to improve or even alter thisrather rigid framework (Lichtheim, 1971; Howard& Klare, 1972; McLellan, 1979), and Godelier andcolleagues radically expanded the Marxist understanding of Infrastructure to what he calls idéel
reality, a notion inspired both by phenomenology
and structuralism, consisting of thought andlanguage, knowledge of nature and tool usage,
and taxonomy and classification (Godelier et al.,
1978: 764).
The concept of Infrastructure has been
important since Marx (1973) formulated it in
his Grundrisse1, and it has underpinned thesocial sciences to various extents. However, thecanonical collections of infrastructure studies
noted above seem to be silent about this specificline of Marx’s intellectual legacy (cf. Carse, 2012:
542–44). The reason for this amnesia may be that,aside from the fact that the term ‘base’ is more
often used in Marxist terminology, the preferredtheoretical approaches of these authors of foundational studies, such as symbolic interactionism

8(Star & Ruhleder, 1996; Star, 1999), system theory
a la Parsons and von Bertalanffy (Hughes, 1983),
and ANT/SCOT (others), have drawn researchers
away from this tradition. In fact, the merit of theseapproaches is undeniable: by ‘clearing and erasing’
the past traces (Bowker & Star, 1999: 257), STS has
produced a fairly large number of empirical, finetuned studies of more specific technical infrastructures.
While admitting the advantages, I claim that atleast three major problems have been overlooked
by not critically conversing about and confronting
the Marxist legacy: 1) What is Infrastructure? 2)
How does it work? 3) How do we understand it?
1) In STS, the question of what infrastructure isusually relies on Star and Ruhleder’s (1996) foundational definition, which presents eight featuresthat are seemingly distinct from the Marxist
concern with the mode of material production as
the unitary basis for Infrastructure. Overlooked
here is not so much the chance of comparing
the two as a missed opportunity to refer to theextremely rich inventory of efforts to revise thelatter after its initial formulation in Grundrisse. In
addition to the ensuing attention to the pivotal
influence of the Superstructure on its counterpart(McLellan, 1979; Anderson, 1976), to be discussed
below, efforts have also made to find alternatives
to modes of production, such as consumption(Bataille, 1988; Baudrillard, 1981) or exchange(Karatani, 2014; cf. Polanyi, 1944; Sahlins, 1972).
Among these, the most striking case is the abovementioned innovation of Godelier and others in
adding structuralistic elements like taxonomy
and classification into Infrastructure, directly
leading to Bowker and Star’s (1999) similar claim
20 years later, a foundational case for the present
treatment of knowledge infrastructure. By failingto examine such preceding efforts, STS researchers
have clearly missed the chance of incorporating
certain insights into, for example, how the idéel
system works together with a more classic modeof production, which could provide clues on thelink between the knowledge infrastructure andthe wider economy in the present argument2.
2) The way Infrastructure works is tightly
related to its counterpart, Superstructure; for
Marx, his followers, and his critics, however, therelation between these two poles has been theFukushimatarget of extensive examination and polemics
because critics regard the Marxist definition ofSuperstructure as too loose, as it includes virtually
everything except modes of production. In
contrast, STS circles seem to have shown relatively
ambiguous attitudes to questions concerning theeffect of infrastructure and how it is constructed.
Again, the point here is not to adopt the unilateral determination of the earlier formulation,
but to re-examine ensuing efforts to reformulate
the very meaning of determination: for instance,some even argue that Althusser and Balibar’s(1970) concept of overdetermination is essentiallyin line with the notion of complexity (Shiozawa,
2002), possibly contributing to the present discussion of how multiple infrastructures interact witheach other (cf. Vertesi, 2014).
This determination thesis is also directly related
to the problem of power in terms of the class that
dominates Infrastructure. This element, as part ofthe legitimate vocabulary of political sociology,
seems to have some shadowy resonance incontemporary infrastructure studies; however,references to the issue are both hesitant andlacking theoretical cultivation in terms of whatkind of power is related to the issue (cf. Edwards etal., 2007: 24–31; Edwards et al., 2009: 371).
3) The question of the understanding of Infrastructure and the value directly attached to suchan act of recognition, the central theme of thisarticle, requires full exploration. In the Marxist
conceptualisation of these paired concepts, thelatter represents the surface and visible values thatapparently dominate society, whereas the former
is submerged. The Marxist strategy of historicalmaterialism, in essence, is to destroy this naive
view of the dominance of such surface values by‘turning Hegel on his head’ (Marx, 1976/81), an actof inversion in the face of the ladder of values thatexists both in Hegelian idealism and in the realworld, highlighting this apparent inferiority of thevalue of Infrastructure in order to reveal its determinant power.
Thus formulated, the following arguments
concerning infrastructure in STS have followed
a similar path without attending to its intellectual ancestry. References to the negative evaluation of infrastructure have been scattered in thepreceding body of research, in which infrastruc-ture is described as boring and unexciting (Star,
1999: 377), as maintained by undervalued andinvisible workers (Star & Bowker, 2002; Bowker
et al., 2010: 98), and as often characterized by‘tension’ with regard to its value (Edwards et al.,2013: 26)3. One description of the ambivalent
aspect of treating taxonomy as infrastructuresummarizes the issue here:“Being treated as infrastructure has hitherto
been a dubious honour. While being consideredessential gives one a certain amount of leverage, italso means one risks being taken for granted andneglected in the face of other, more prominent
topics.” (Hine, 2008)

When this idea is extended to metadata, things do
not seem to be radically different:“All recognize metadata’s potential value, but when
the rubber meets the road, an unfunded mandate
to be altruistic [...] does not prove highly attractive.”(Edwards et al., 2012)

Thus, although the conflict of values regarding
infrastructure has been a matter of constant, if
sporadic, concern even within the study of infrastructure in STS, its formulation lacks the consistency of the Marxist tradition in dealing with theirown version of Infrastructure.

Value Oscillation and theM-B (Marx-Bowker) Index
In this article, drawing upon the last point above,
I focus on this discrepancy: whereas the power of
infrastructure is recognized, the practices related
to it, such as service to others and its maintenanceand repair, are not highly ranked in the existingvalue system, being often regarded as invisible
and even ‘boring’. Because of this particular natureof infrastructure, Bowker (1994) proposed ‘infrastructural inversion’, a strategic analytical operation to bring hidden infrastructure to the surface
and expose its importance. My claim in this paper
is that this particular operation is, in essence,
structurally isomorphic with the Marxist operation of ‘turning Hegel on his head’, different onlyin terms of its focus and scope of theorization.

9

Science & Technology Studies 29(2)These operations have thus far been confined
to analysts’ strategies, whereas my examination
relates to how practitioners in situ regard the valueof infrastructure and its related practices. Conflict,contradiction, and oscillation (as is evident tosome extent in the preceding monographs on theissue) are expected from this approach, precisely
because practitioners are the ones who develop
and maintain infrastructures. Hence, I will adopt
the term ‘value oscillation’ for describing this
aspect of fluctuating attitudes, between these twopoles of the recognition of its supportive value
and avoidance of its shadowy character.
This observation relates intrinsically to thevery concept of infrastructure itself, which isalmost oxymoronic: though infrastructure exerts
immense power as it structures other things, it
is inferior (inferus, inferior from infra, in Latin)
because it lacks surface value. By way of analogy,
Weick and Westley (1999) have claimed that‘organisational learning’ is an oxymoron because
organizing is a process of reducing complexity,
whereas learning increases it; hence, organisational learning is rare. Infrastructure as oxymoron
exists rather steadily but generates a double-bind(Bateson, 1972) for its concerned practitioners,
owing to its intrinsically opposing vectors ofvalue. Because its value oscillates between thesetwo poles, like other double-bind situations, it ishung in indeterminacy. Hence, my term, ‘value
oscillation’, is more adequate than conventional
expressions like ‘value conflict’ or ‘contradiction’:these are both too macroscopic, and they alsoeasily connote a ‘dialectical’ solution of cancellingthe contradiction out (aufheben!), which, I believe,
rarely happens in a double-bind.
To describe this zig-zag movement of indeterminacy, I introduce a second term, the ‘M-B
(Marx–Bowker) index’, to show the degree ofappreciation for the invisible infrastructure values.
Here, ‘infrastructure’ is defined not only as thematerial entity designated by the term, but also
the wider assemblage of activities involving quasipublic services to others, works of a sub-contractive nature, and backstage efforts including thoseindirectly related to infrastructure building. The
juxtaposition of these two names signifies theintrinsic continuity of the two approaches in termsof inverting the underlying value, while simulta-

10

neously emphasising the practitioners’ view andaction; in addition, the index becomes an easily
visualized means for observing the oscillating
attitude of the concerned practitioners.
Presupposing practitioners’ recognition of thevalidity of any given infrastructure, the M-B index
is defined as concerned practitioners’ observed
degree of commitment to developing and maintaining a given infrastructure: hence, a high index
means a high degree of commitment to it, and a
low index implies avoidance of such commitment.
In this paper, this index is used as a figurative toolfor visualising the observed oscillation of practitioners’ attitudes as demonstrated by both theirdiscourse and their actions vis-à-vis the issue ofbuilding and maintaining infrastructure. One may
argue that such values behind our actions are toocomplex to be adequately identified with thisindex. This argument admittedly has some truth;
however, I claim that when we focus sharply anddirectly on the issue of building and maintaining
infrastructure, we may reduce it to a simple
question of whether one promotes it or avoids it,
though there may be intermediate choices withaccompanying complementary reasons. Such
focused action and discourse, along with any oscillation, are in fact observable, reaffirming Geertz’s(1973) classical formulation of cultural practice asa public vehicle of meaning.
My own research is based upon ethnographic
observation, and I use such relative expressions
as ‘high’ and ‘low’ with regard to the M-B index.
However, my approach does not preclude thepossibility of substantiating the claims by using
questionnaires, though I did not attempt such inthis project. In such a case, the M-B index could
be tentatively quantified, with zero meaning thepractitioners’ avoidance of any commitment to
infrastructure building, and 5 or 10 showing fullcommitment to its construction, thus expressing
a continuum.
Some laboratory studies seemingly present
cases of apparent value oscillation concerning
the ambivalent role of research tools and related
work practices (Clark & Fujimura, 1996; Gaudilliere & Löwy, 1998; Joerges & Shinn, 2001; Mody,
2011), the interchangeability of epistemic things
and research technology (Rheinberger, 1997;
Joerges & Shinn, 2001), and the problem of theFukushima
fluctuating status of such tools in the hierarchyof credibility in laboratory settings (Clark &Fujimura 1992: 16)4. However, infrastructure goes
far beyond the limited scopes of laboratory anddisciplinary boundaries, and its multi-layered
character increases the complexity of analysing
value oscillation, as it is distributed across diverse
spaces and various layers. It is further complicatedwhen extended to its knowledge aspect, wherein‘infrastructure’ generally signifies the whole set ofheterogeneous elements of databases, computerization, grid systems, e-science, and so on without(thus far) a proper definition (Edwards et al., 2009;
Edwards et al., 2013).
In fact, the question of how value oscillation takes shape arises in light of the ongoing
momentum and extensive influence of computers,
information, and even data science as ‘science’(Hine, 2006b, 2008; Edwards et al., 2007; Edwards
et al., 2009; Bowker et al., 2010; Edwards et al.,2013). We can presume that these factors push
the M-B index both upwards – because the halo of
new science attracts devotion – and downwards
because ‘infrastructuralization’ is avoided in suchareas compared to established engineering efforts
to maintain roads, water, and electricity. Thus,
possible strategies that concern the dynamics ofknowledge infrastructure become a question ofconcern.

The Research Subject
The remainder of this article will discuss ‘value
oscillation’ as it relates to the knowledge infrastructure in two distinct case studies, both related
to attempts to build a sort of database as part ofthe larger scheme of Japan’s national policy ofdeveloping an infrastructure for drug discovery(sôyaku-kiban) that is academia-based. First, welook at a faltering endeavour to establish a database of natural product compounds to make thesearch for drug seeds more effective and to facilitate basic research for ligand–protein interaction.
Second, we will examine the ongoing construction of a large-scale virtual library of chemicalcompounds, using a world-class supercomputer.
The analytical focus in these case studies is
twofold. The first is on how value oscillation isobservable in the multi-layered infrastructures

wherein these databases are embedded. The
schemes for building such drug discovery infrastructure require coordinating and constructing
various layers of sub-infrastructures simultaneously, providing intriguing examples of how the
problem of value oscillation is approached in the
different layers beyond the confines of the specificdatabases that are the main focus.
The second focus is how this issue is related tothe context of knowledge and material interaction. As drugs are material entities that require avast amount of heterogeneous knowledge, thedevelopment of the knowledge infrastructurein this context is closely related with its material
counterpart in producing drugs. By highlighting
these two aspects, this paper examines the various
appearances of value oscillations throughout
the complex, multi-layered character of theknowledge infrastructure and how the practitioners deal with the situation in each contextual
effort, along with the consequences5.

Background: Drug Discovery
Infrastructure as Knowledge
Infrastructure
Drug discovery is a hugely complex process thatdemands a vast amount of heterogeneous knowledge and related infrastructure, beginning withfinding the proper target proteins and drug seeds
and progressing to a range of steps from animal
testing to clinical trials, which include Phases I to
III (Epstein, 2007; Petryna, 2009; Keating & Cambrosio, 2003, 2012). Behind the policy idea ofdeveloping a national drug discovery infrastructure lies the fact that the productivity of drug discovery has decreased sharply despite the growing
knowledge and technology related to the process,and controversies have occurred about its possible causes (Epstein, 2006; Ryzewski, 2008; Bartafai& Lees, 2006; Kubinyi, 2003). Drug companies havethus urged governments to promote the idea ofoutsourcing such development to academia,
which is supposed to be able to bear greater risks.
This idea gained momentum when the National
Institutes of Health (NIH) in the United States published their Roadmap Initiative for Biomedical
Research in 2003 to promote constructing an academic drug discovery infrastructure in the form

11

Science & Technology Studies 29(2)of public chemical libraries and screening centres
for academic use (Wikstrom, 2007; cf. Mazzucato,2013). In response, the Japanese government
launched their version of the policy (Fukushima,2015).
Here, I comment briefly on the peculiarities ofconsidering the drug discovery infrastructure asa particular type of (knowledge) infrastructure.
Despite the general usage of the term kiban inpolicy discourse, ‘infrastructure’ here means thevery specific purpose of producing drugs, as
opposed to more general infrastructures like roads
and the Internet. The alternative term, the drug
discovery ‘pipeline’, also connotes the horizontal
integration of the temporal procedures frombench to bedside. Thus, some researchers preferusing terms like ‘platform’ (Keating & Cambrosio,
2003) to highlight the horizontal assemblageof knowledge and material rather than the term
‘infrastructure’.
Nevertheless, the term ‘drug discovery infrastructure’ has its own legitimacy. First, this process
consists of multi-layered entities, ranging fromthe national plan to specific institutions to thelaboratory level, where various aspects of infrastructure-like characteristics can be spotted,
exhibiting similarity with other types of moreconventional infrastructures, such as databases
open to academic purposes (Star & Ruhleder,
1996; Edwards et al., 2007; Edwards et al., 2013).
Second, the process includes a unique entanglement of materiality and knowledge. Althoughdrugs are an industrial material, they are also what
Barry (2005, borrowing from Bensaude-Vencent& Stengers, 1996) calls ‘informed material’, which
requires a huge complex of knowledge fromprotein science, chemistry, and medicine, wherein
the elements of the knowledge infrastructure play
pivotal roles.
In the following sections, the sub-institution
levels are given priority for the detailed analysis,
but higher levels are by no means unrelated.
The focal institution is RIKEN, a public research
institute representative of basic science in Japan.
RIKEN’s involvement in the infrastructure plan is
the main background. RIKEN has experienced aseries of ups and downs, from its pre-war status
as the pivotal nexus of research and industry to
post-war decline and revival in recent years in

12

the form of national genomic and postgenomic
research projects given by its supervising ministry(RIKEN, 2005). After a series of major science
projects, such as Protein 3000 Project (Fukushima,2016), RIKEN launched a plan to establish a more
tightly woven infrastructure for drug discovery
with a more effective organization of its branches,
which had not previously been tightly combinedwith one another. The following cases both fallwithin the larger scheme of RIKEN’s policy6.

The Chemical Biology Centre as
Future Quasi-Infrastructure
The first major topic of this paper is the faltering effort to establish NPEdia (whose name isabridged from ‘Natural Products Encyclopaedia’),a database for public use. This project was implemented along with the development of NPDepo,a public library of natural product compounds.
These plans were launched in parallel with agovernment plan to establish a national libraryof chemical compounds open to academic use.
Natural products are the chemical entities produced by living things, such as microbes, plants,
and marine organisms. These entities have bioactivity—namely, the effects exerted on other living
things. This genre of research has had extensive
relations with drug discovery, most notably in thecase of antibiotics extracted from fungus, such as
penicillin, or recent searches for plants and marineorganisms to provide new seeds for drugs (Fukushima, 2015).
This specific idea was promoted by a number
of RIKEN’s leading laboratories; among them, theantibiotic laboratory (of more than 60 members),
which boasts a long genealogy of precedinglaboratories in the same genre of research (Ueno,2008), has taken the pivotal role. This infrastructural innovation was accompanied by an organizational plan to establish a new centre for an
emerging hybrid science called ‘chemical biology’,
wherein chemical compounds are used to regulate
and probe the activity of life phenomena. In the
United States, the above-mentioned Roadmap
Initiative policy to promote chemical biology
includes a public chemical library and screening
centre (Wikstrom, 2007); however, controversies
have developed between the NIH and leading

Fukushimascientists there over how best to orient chemical
biology for drug discovery (Fukushima, 2013).
Like Matryoshka (Russian nesting) dolls, theproblem of value oscillation vis-à-vis the development of various layers of infrastructure can beobserved on various levels, from RIKEN itself down
to the laboratory practices. One of the focuseshere is the centre level, within which the NPEdia/
NPDepo complex is situated. The chemical biology
centre plan was once intended to establish oneof the hubs for the coming drug discovery infrastructure, both within and without RIKEN, symbolising RIKEN’s commitment to connect academia
and industry by implementing governmental
biomedical policy more directly. For that purpose,
RIKEN has increased the number of time-limit
centres to improve the infrastructural functioningof various kinds of large facilities, libraries, anddatabases (RIKEN, 2005). The chemical biology
centre, under the leadership of the antibiotic laboratory mentioned above, was once part of thislong-term plan. The centre was intended to beequipped with not only the database and library,
but also with various assay systems as well as
high-throughput machinery to enable the rapid
examination of ligand–protein interactions, forpublic service as well as for their own research.
However, a contradiction has thus been revealed
about what the centre was meant to be from the
beginning.
First, despite the official emphasis on the infrastructural objective, the promoters of this planalso intended to use the centre to pursue theirown research innovation. I observed this divided
intention during my visit to the laboratory, wherea large part of the researchers’ energy was spent
preparing for the coming centre. In fact, the main
members of the laboratory were subdivided into a
number of teams, each consisting of a team leader
and several members and technicians, each tasked
with various infrastructural obligations, such as
improving the high-throughput machinery, establishing new assay systems, and collecting and classifying materials for NPEdia/NPDepo.
Here, the division of labour is not confined
to scientist/technician distinctions; almost all
the scientists were also assigned to one or more
infrastructure-related tasks for the coming centre.
However, the distribution of such workloads was

uneven, with some teams doing basic work likecollecting materials and organizing information, while others were only doing their own
homework7.
Many examples of value oscillation occurredin this complex distributions of workloads. For
instance, the 2008 intra-RIKEN official report,
which is issued every seven years about theactivity of the laboratory, was concerned mainlywith the activities directly related to the infrastructural aspects of the future centre, while theoutcomes of the individual research activities
were given only passing references. Thus, its M-B
index for emphasis on infrastructure elements
was high. However, these individual papers
were published in major journals and reported
in a separate annual record about the laboratory’s academic activity (interview, 24/6/2008). In
addition, the uneven distribution of the infrastructural workloads led to some rather cutting remarks
by some of the staff about their colleagues’ work.
For instance, after the official interview, one of theteam leaders suggested to me that there would
be no need to interview some of them, as theirwork was nothing but technicians’ work, not thatdone by scientists. Such remarks clearly demonstrate a contrastively low M-B index score despite
the laboratory’s general policy (field note, 20/11/
2007).
The changing discourse of the laboratory head
was a living example of such value oscillation. In ameeting with the whole laboratory, for instance,
he rather openly warned those who were then
too keen to do service work for outsiders about
their infrastructural duties, such as examining thebioactivity of the compounds entrusted to them.
Even though these are the sort of preliminary
duties that the future centre would be expected
to carry out, he underlined the possible danger ofdoing too much service work for outsiders, which
could decrease the quality of their own scientific activity (field note, 15/4/2008). On anotheroccasion, he suggested that the staff collaborateon their entrusted work if they found the job
interesting enough to do as part of their own
research (field note, 13/11/ 2007). This impressive
degree of ambivalence was observed throughout
the laboratory, even, as demonstrated, with the13

Science & Technology Studies 29(2)same leader, with the M-B index score seeming tochange daily, like stock prices.

NPEdia and NPDepo:
Between Material andKnowledge Infrastructure
The above example, wherein the whole laboratory
was related to the centre plan, rather simply demonstrates the problem of value oscillation. NPEdia and NPDepo, however, present a more subtle
picture of how value oscillation is embedded in
the more complex layers of multiple infrastructures. To understand this, we must take a closer
look at the very concept of both databases as the
public library of ‘natural product’ compounds. As
I have shown, this project is officially in line withthe wider national science policy programme toestablish a public library of chemical compounds,
but the uniqueness of this plan lies in its adherence to collecting natural products as opposed
to collecting ordinary chemical compounds, as
directed in the competing scheme of RIKEN’s rival,
the University of Tokyo (Fukushima, 2013)8.
The idea behind this project derives first fromthe historical fact that natural products havebeen powerful sources of drug seeds, especially in microbial cases, which have included avariety of powerful discoveries, from penicillin
to statins (Newman & Cragg, 2007; Endo, 2006).
Second, the relatively strong tradition of Japanese
research in this area, to which the antibiotic laboratory belongs, led the promoters to maximize
their traditional advantages. Third, the uniquechemical structures of these natural products
were expected not only to promote the search for
drug seeds, but also to lead to the developmentof unique bio-probes for basic biological research(Fukushima, 2015).
NPEdia was designed to supplement thecollection of materials, to serve as a legitimate
knowledge infrastructure in the wider context
of the drug discovery infrastructure, and tofunction as an encyclopaedia for natural productcompounds with annotated meta-information,such as bioactivity and the details of related assay
methods. It was also meant to serve as a catalogue
for NPDepo to give details on the further uses of
the actual compounds that NPDepo provides

14

Thus, the NPDepo/NPEdia complex was considered pivotal for the coming chemical biology
centre, and a specific team was assigned responsibility for them. This team included a leader – who
also acted as office manager of the laboratory
administration and coordinator of the other teams
that collaborated to develop the various elementsof the library/database – as well as a couple ofinformaticians. This meticulous organizationsuggests that the managers of this facility require
full commitment to its development and administration – demanding high M-B index scores –unlike other team leaders whose attitudes were
often lukewarm vis-à-vis such infrastructural obligations.
This initial scheme, however, did not develop
as planned, owing perhaps to entangling factors.
First, collecting natural products from individuallaboratories presented a hindrance because thesematerials take years or even decades to extract,unlike commercially synthesized materials; being
thus the object of researchers’ attachment, it isdifficult to get them released for public use (interviews, 25/5/2008, 30/6/2011). This aspect can beinterpreted as a certain version of value oscillation:
researchers officially understand the value of sucha library, but they do not want to commit to it. A
similar situation was found in the mouse genome
database, where young researchers hesitated tosubmit their research outcomes to the database(Hine, 2006a).
Obtaining materials from retiring researchers
before they closed their laboratories was slightly
easier, but changing property rights trends, inwhich universities started to strengthen theircontrol over the products of individual laboratories, are now a problem (interview, 29/5/2014).
Thus, a sort of vicious cycle occurred: the failure tocollect material enough to demonstrate the meritof such a library with its capacity for processing
information in a high-throughput manner further
diminished incentives for researchers to submit
theirs.
The NPDepo’s delay fostered stagnation in
NPEdia’s development. One of the expectedfunctions of NPEdia, to serve as a full databasefor natural products, proved too feeble to work
autonomously because of competition from rival
databases for chemical compounds. Generally, inFukushimachemistry, SciFinder, by the American Chemical
Society, has been one of the world’s most comprehensive and authoritative sources of chemicalcompounds9. The chemists in the laboratory
affirmed that SciFinder is sufficient for all partsof their work (interview, 14/8/2014). However,
PubChem, released in 2004 by the National Center
for Biotechnology Information (NCBI) as an open
source database, focuses on the biological activities of small molecules and has recently gained
popularity10. The informatician in charge of NPEdia
explained that in NPEdia’s earlier days, the ideaof an open database specific to natural productcompounds worthy of trial, such as PubChem,
was still underdeveloped. However, the speed ofdata enrichment at PubChem surpassed that of
NPEdia, making it extremely difficult for NPEdia
to compete with its global rival (interview,
29/5/2014).
However, NPEdia could have retained its
advantage if its catalogue function had beendeveloped further. Natural products often occur
in minuscule quantities in laboratory settings
and are usually very hard to synthesize, whichmakes their production challenging for synthetic
chemists – in some cases, global competition has
developed among leading chemists to synthesize
natural products first11.
This situation differs generally from that ofchemical or genomic databases. For instance, thechemical databases mentioned above provide
ample data related to methods of synthesis orabout the vendors that sell such compounds. In
the genomic database case, the recent development of commercial service companies has
made it possible to quickly produce the necessary
vectors from the genetic sequence information insuch databases when a researcher asks for them.
In other words, there are large networks of articles,
laboratories, and vendors between the data in the
database and the corresponding materials, whichconstitute a sub-layer of infrastructure, enabling
the users of such databases to adapt the information to develop the materials they need (interviews, 22/5/2014; 6/6/2014; 22/8/2014)12.
In the case of natural products, this sub-layer
has not developed fully, because of limited quantities and difficulty in reproduction. Thus, even
if data about a particular compound are gained

through the database, the only way to obtain thecompound is to ask the laboratory to share thesubstance. According to a veteran natural productchemist, this is a complicated process because thelaboratory may not exist any longer or because
the laboratory has such a limited amount of thetarget compound that it cannot be shared. Even if,
in rare cases, the compound might be synthesized
and sold by vendors, its purity may be questionable, and further effort to refine the compound byreanalysing its real components may be required(interview, 22/8/2014).
NPDepo would thus be tremendously beneficial for users of such compounds because it would
increase the ease of finding the target compoundin the library, and the open protocol would
simplify the procedure for obtaining material,
eliminating negotiations with individual laboratories. NPEdia’s full potential would be realized
in this way as users could refer to the annotated
information within the database and use it as acatalogue, as well.
However, this potential has not been realized
thus far. Without NPDepo, NPEdia cannot
compete with the existing databases, because its
merit is sufficiently strong only with the supportof NPDepo. Thus, the apparent powerlessness of
NPEdia as a small, emerging database should not
be understood solely as the problem of ‘gateways’
in terms of connecting isolated systems to larger
ones (Edwards et al., 2007; Edwards et al., 2009),
but also in the context of the data-material
complex, where the material scarcity of natural
products may have produced a unique set of datamaterial relations not seen in the wider genres ofchemical or genetic databases.
This situation also relates to the relative invisibility of the problem of value oscillation here, incontrast with the preceding case of the chemical
biology centre. Needless to say, as part of thecentre, NPDepo/NPEdia would inevitably invite
value oscillation for those who were obliged
to commit themselves to infrastructural work.
However, the more visible aspect of value oscillation lies at the sub-layer of the infrastructure – inthe supporting network that enables the processof converting data in the database to its corresponding materials, under the guise of individuallaboratories’ reluctance to submit their materials

15

Science & Technology Studies 29(2)to support the coming library as infrastructure.
This contrasts with the case of the chemical
biology centre, where the sway of the M-B index
is much more easily observable as the centre
scheme itself has more manifestly advanced.

The Virtual Library as the Coming
Knowledge Infrastructure?
To observe the knowledge aspect of value oscillation more clearly in the emerging knowledge
infrastructure of NPEdia—which has been realized only to a limited degree—let us briefly examine a supplementary case: the emerging virtual
library of chemical compounds within the related
scheme of RIKEN’s drug discovery pipeline. Thisidea has been promoted by a research grouprelated to the so-called K supercomputer in Kobe,
West Japan, as part of a scheme to redevelop thecity within a large biomedical complex after the
1995 earthquake (KBIC, 2012). K, from kei, meaning‘quadrillion’ in Japanese, symbolizes the computation of 10 petaflops per second; this computer isintended to have the fastest computing speed in
the world13. A number of projects related to thissupercomputer are specifically concerned withcomputational drug discovery. There are at least
two major plans: The first is to build a huge libraryof virtual chemical compounds, and the other toanalyse ligand protein interaction using big data14.
The first plan relies on use of Archem – existing
software originally designed for rapid analysisof the optimal paths for synthesizing the target
compound – so as to produce large amountsof virtual compounds by reversing the process.
The research group succeeded in producing
five billion virtual chemical compounds (Ashida,2010), an astronomical number compared to
NPDepo’s tens of thousands of compounds or tothose of the drug companies, perhaps 10 million
at best (interview, 12/5/2012). However, this does
not include some of the complex cases of natural
products that may often exhibit 3D structuralcomplexity, such as chirality (interview, 2/9/2014).
The purpose of this library is to examine thepossible interaction between virtual compounds
and the target proteins to predict the best-fit
cases. The rising expectation that the supercomputer would handle huge computational loads

16

made computer companies like Fujitsu eager toparticipate in this newly emerging field15.
However, these methods are not without
problems. First, the issue of computational
explosion remains in terms of how to balance
between calculations based upon either
Newtonian or quantum dynamics, and how (not)to calculate the influence of the molecules in
the mediating substances, such as solutions or
intracellular environments, existing between
proteins and their ligands (interview, 12/8/2014).
Most problematic, however, is the huge amountof noise. Just as in the past case of combinatorial chemistry, where the once-popular highthroughput production of new compounds has
lost its glamour because of the huge nonsensical structures it produces (Barry, 2005; Borman,
2004), the virtual library must also sort significant
structures from the huge amount of meaninglessones (interview, 2/9/2014). In fact, past reports
indicate that existing calculations not done by K
computers have produced a prediction successrate of less than 10% for proper protein-ligand
binding (Kanai, 2012).
Thus, the second programme is designed toraise this success rate by enabling the computer
to learn the pattern of such bindings using theexisting databases on protein–ligand relations.
In principle, this is performed similarly to the way
a neural computer learns fingerprints or humanfaces. The success rate for prediction is expected
to double from the traditional way of computing
the molecular dynamics of these interactions(Kanai, 2012; Okuno, 2012).
In terms of value oscillation, these new
radical features reveal intriguing problems notclearly seen in the NPEdia case. Although these
programmes are still largely in development andare not ready for public use, their main researchers
have enumerated hindrances to plan development, some of which I interpret as indicating
value oscillation. For instance, they are uncertain
whether they should continue maintenance work
to promote the public use of this library as aresource centre after the present phase of systemdevelopment. The laboratory head responsible forthis scheme seems to have high M-B index scores,
as he fully recognizes the importance of theinfrastructural aspect of his role. His somewhat

Fukushima
subtle value oscillation, then, may derive fromhis peculiar background, a hybrid of computing
sciences and other disciplines, such as biology
and astronomy. This background often makes him
unsure of his position in each of these communities. At informational science meetings, heoften finds his colleagues too excited over trivial
software innovations; in our terms, he regards
them having insufficient M-B index scores for what
they should do. However, in life science meetings,
he feels that his computational approach is often
merely subcontract work for the mainstream wet
approaches, meaning that, paradoxically, he isdissatisfied with the way the biology community
regards his work as infrastructural.
This means that his generally high M-B index
is not sustainable all the time; occasionally, hefeels that the essential work that he assigns to
his staff – related to deleting all possible noiseor the nonsensical chemical structures producedin the library – can be problematic when considering career development possibilities after suchtedium (cf. Hine, 2008 for similar uncertainty)(interview, 2/8/2014).
The researcher in the second programme forthe machine learning of ligand–protein binding,
who seemed to have a very low M-B index score,shrewdly evaded the service aspect of work byentrusting it to a venture company that he established, exempting his laboratory from any further
infrastructural work so that he could concentrate on the development of the new method(interview, 8/8/2014). Nevertheless, the value
problem is unavoidable when his new method
is applied in the real drug discovery context. The
problem is how to gain support from chemists
for synthesizing their computational predictions
into the embodied compounds. The researcher
admits that to synthesize the outcome of hisvery pragmatic machine learning with a processthat is theoretically blackboxed would be considered by the synthetic chemists’ community to beservice work without scientific value – the M-B
index score is close to zero here; thus, he asks forhelp with synthesis only from an old friend fromhigh school. In the case of the virtual library, theresearcher from the beginning plans to entrust
the job to companies to avoid possible conflicts

with scientists who do not want such subcontractlike duties (interviews, 8/8/2014; 2/9/2014).
These programmes are in a development stage
wherein their innovative characteristics are spotlighted in public, but eventually, they will moveinto a maintenance – that is, infrastructural –phase. The knowledge aspects of such infrastructural efforts entail the problem of data-material
relations similar to the case of natural products
above – namely, the problem of collaboratingwith synthetic chemists whose M-B index scores
are often close to zero in terms of doing service
work purely for such a virtual method. Likewise, innatural products, the chemists tend to be hesitant
to become involved with the library plan, whichis also interpretable as showing a low M-B index
score.
This rather unstable relation between theknowledge and related material aspects in theform of non-collaboration by synthetic chemists
shows the inherent instability of the in-process
knowledge infrastructure. On this point, anotherspecialist in the simulation of protein structures
who participates in the K computer programme
pointed out the inevitable duality of the infrastructural and innovative aspects of computer
technology and the difficulty of balancing them.
He noted that computer technology is now
widely distributed even in the basic tools of thestructural analysis of proteins – namely, in X-rays
and NMR spectroscopy – where complex signals
are analysed with the help of computerized dataprocessing. Thus, he underscored that even in
his laboratory, the aspects of the cutting edgeof innovation and infrastructural work exist side
by side. He emphasized that the latter should betreated carefully in such ways as developing andfine-tuning the software needed for such simulations, which is ordinarily seen as infrastructural work that does not produce appreciable
credentials; thus, careful persuasion is needed toenlist members of the laboratory for such work
(interview, 12/8/2014).

Discussion
The main claim of this research is that value oscillation is intrinsic to consolidating and maintaining
infrastructures of any type. The problem, then,17Science & Technology Studies 29(2)is how it takes shape and is dealt with according to different degrees of infrastructure consolidation within multiple layers. In fact, a seriesof preceding discussions have taken place on
the perpetual tension or contradiction between
the approaches of biology and computer science, often dubbed ‘wet’ and ‘dry’ approaches in
the research on genomic sciences. For instance,
García-Sancho (2012) traces the relation between
genetic/protein science and sequence technique
as a constant swinging between antagonism
and accommodation. In more detailed microscopic studies, Lewis and Bartlett (2013) discuss
the problem of bio-informatician identity, whichsways between scientists pursuing new knowledge and technicians supporting the jobs of wet
biologists. In preceding studies more on knowledge-infrastructural aspects of laboratory work,
Star and Ruhleder (1996: 126) refer to a primordialcase of value oscillation that they call ‘tool building and the reward structure’ in their case studyof the gene-sequencing network. In relation to
the mouse-based genetic database, Hine (2006a)
details various potential conflicts and their avoidance in cases similar to the institutional separation
between biologists and the resource centre, while
her analysis of systematics (Hine, 2008) delineates
the value oscillative aspects in more detail.
Compared to these preceding examples, thetwo cases in this paper appear to be situatedon a more complex institutional ladder – one in
the chemical biology centre, the other in the K
computer project, and both within RIKEN’s wider
programme. These two are also situated in thewider context of a more established knowledge
infrastructure: the databases of chemical andgenomic information.
Hence, the phenomenon of value oscillation ismost visible at the rather established level of thecentre, whose purpose from the beginning has
been torn between the goals of an infrastructural
service centre and those of a centre for innovating
research; the M-B index appeared to be literally
fluctuating, as observed in both the leader’s andresearchers’ discourses and action in various
contexts. In contrast, the subsequent cases of
NPEdia and the virtual library demonstrate a more
complex picture owing to their being embedded
in multiple layers of both organizations and other18

databases while falling short of establishing aproper level of autonomy. Hence in the case of
NPEdia, the value oscillation is spotted in the
sub-layer that supports this database, while in thecase of the virtual library, diverse strategies wereobserved for avoiding a double-bind situation:that is, the shrewd avoidance of a further commitment to maintenance and dissatisfaction with
the indeterminate character of the concerned
researcher’s role in terms of infrastructure development. Thus, compared to the precedingarguments that emphasized a rather black-andwhite image of contradictory values, these cases
exhibit a more subtle and layered embodiment ofvalue oscillation, as well as diverse ways the practitioners deal with it.

Conclusion
I have argued here that current studies of infrastructure have suffered from historical amnesia
lacking critical dialogue with the preceding Marxist discussions on Infrastructure in terms of thegenealogy of concepts. I have pointed out thatan opportunity has been missed for theoretical
dialogue in relation to at least three major questions, the last of which is highlighted in this paper:
the pivotal importance of the Marxist operation of inverting the unseen value of Infrastructure, which has been occulted by the shadow ofSuperstructure – the operation represented by
the phrase ‘turning Hegel on his head’. I claimed
that this operation is intrinsically isomorphic with
Bowker’s ‘infrastructural inversion’, now regarded
as pivotal in contemporary research on this topic.
Behind the need for this operation lies the recognition that the concept of infrastructure is an oxymoron, imbued with contradictory meaning – thatis, infrastructure is endowed with power while it issimultaneously inferior to the surface value.
‘Value oscillation’ is the term used to describe
this double-bind situation whereby practitioners hang in indeterminacy between opposite
vectors, and the M-B index is the tool used tovisualize these oscillating values. Two cases ofsuch value oscillation were taken from the drug
discovery infrastructure building in Japan where
the knowledge of drugs is uniquely entangledwith the physical material in a complex, layered
manner.

Fukushima
To elucidate further implications of myapproach here, we must note that the recent
rise in concern with the infrastructure in the
STS community derives not only from growing
academic interest in such individual cases as thecomputer network or energy infrastructure but
also from increasing attention within the researchcommunity to the more structuralized, longue
duree elements of socio-technical development
rather than to the early and rapidly changing
aspects of technoscientic transformation. These
renewed intellectual concerns can be observedin such diverse expressions as a reference to the‘cold’ situation (Rip, 2010), the ‘obduracy’ of urban
technology (Hommels, 2005) or even ‘the shock of
the old’ (with regard to technologies) (Edgerton,
2006; cf. Fukushima, 2015).
Two points can be drawn from this observation. The first is the merit of talking about infrastructure vis-à-vis the related concepts cited
above. Conceptually, infrastructure leads us tofocus on the dual aspects of a) its power to exert
influence upon that which hinges upon it and b)
its invisibility. In this article, I have pointed out thediverse strategies produced by value oscillation,
ranging from devotion to shadow work – that is,efforts to raise the status of what is invisible – tothe minimal commitment devoted to maintaining
and repairing infrastructure. Beyond the microsociological examples presented in this paper,larger-scale and more historical consequences ofsuch value oscillation will be similarly importantin further examining the longitudinal dynamics
of the infrastructure at large – represented, say,by such instances as the recent issue of roads andbridges in decay, owing to politicians’ general lackof interest in their proper maintenance (Nemoto,2011).
The second point is the yet unexamined relationship, in this era wherein STS scholarship highlights the rapidly changing, unstable networkof humans and nonhumans, between current
concepts of infrastructure and the century-old
use of ‘structure’ in the social sciences. Though
not detailed above, the concept of infra-‘structure’
is not only comparable to its Marxist counterpart
but it also partakes of the larger genealogy thathas taken its intellectual inspiration from theconcept of structure found in structural functionalsociology or even structuralism.
Take, for instance, my formulation of value
oscillation vis-à-vis the dual aspects of infrastructure – namely, its power to exert influence andits invisibility. If we slightly modify this idea toconsider the contrast between the various formsof our ‘existence’ in terms of l’engagement, andthe power of invisible structure – whatever thatmeans – to exert an implicit influence upon thisexistence, this contrast dimly echoes a well-known
historical controversy: Sartre’s revised concept ofexistentialism, somewhat modified by his conversion to historical materialism (Sartre, 1976), versus
Levi-Strauss’s (1966) fatal criticism in which hehighlighted the determining power of classification and taxonomy as the invisible structure that
regulates our very understanding of history. In
fact, the missing link with Marxism which I havehighlighted in this paper is only the tip of theiceberg in terms of possible linkages. Godelier(2011; Godelier et al. 1978), for instance, can also
be regarded as a concrete embodiment of theconfluence of both Marxism and structuralism,
which later leads to the thesis of ‘classification as
infrastructure’ that is foundational for the current
discussion of knowledge infrastructure.
My emphasis on the resurgence of the intellectual concern with the concept of structure lurking
in infrastructural studies, however, does not deny
the novelty of the later approach vis-a-vis theearlier. Compared to the more traditional waysof dealing with structure – either as an invisible
mental structure or as the social structure, mostly
as it relates to humans – current infrastructurestudies provide a series of fresh perspectives on
the socio-technical complex. Its workings canbe more closely observed through modern than
through more traditional ways, such as with casestudies on roads and databases, which were unanticipated in the past. This is why infrastructurestudies, even if they share a concern with past
problematics in the social sciences, do not merely
repeat the past (cf. Marx, 1994) but may be considered as more finely-tuned re-examinations of
persisting controversies from the past, generated
by the historical genealogy of concepts throughwhich we see the world.

19

Science & Technology Studies 29(2)

Notes
1
2

3
4

5

6
7

8

9
10

11

12

13
14

20

The earliest version of this formula appeared in the posthumous publication of a draft called Grundrisse
der Kritik der politischen Ökonomi (Outline of the Critique of Political Economy), written around 1857–58.
The labour process theory is a possible candidate for bridging these two research traditions (Braverman, 1974; Nakaoka, 1971; Knights & Willmott, 1990; Sturdy et al., 1992). Vann and Bowker (2006),
somewhat exceptionally, reflect this line of concern by focusing on the production side of e-science,emphasising the role of funding agencies, thus recalling classical arguments on the role of the capitalist class.
This nuance can be contrasted with technological regimes, highlighting the total visibility of the phenomena (Rip, 1995; Rip & Kemp, 1998, followed by many).
Included in this category are requests for more attention to neglected aspects of laboratory technicians and technical workers at large (Barley & Bechki, 1994; Barley & Orr, 1997) as well as the Burri’s(2008) analysis of radiologists’ strategy in terms of cultural capital and boundary work.
This paper draws on data from various phases of my ethnographic research in the antibiotic laboratory
and chemical biology (2007–2010), the Protein 3000 Project (2010–2013), and drug discovery infrastructure (2013–), which are all related to RIKEN. Interviews were conducted with researchers in various genres on the topic of this theme, both inside and outside the institute.
RIKEN’s recent programme can be seen at http://www.riken.jp/dmp/english/index_en.html (accessed
22/8/2014)
In 2007, ten teams covered the following themes:
1. Streptomyces
2. Genetic analysis of secondary metabolites
3. Fractions
4. Chemical library
5. Compound array
6. Protein analysis
7. Cancer related issues
8. Cell cycles
9. Transcription
10. Chemical compounds at large 2–5 and 10 are largely for infrastructural works
See Parry (2004) for the history of the U.S. Natural Products Repository of the National Cancer Institute. Compared to this global scheme, NPDepo is straightforwardly intended for public use for bothresearch and drug discovery.
SciFinder’s official home page is http://www.cas.org/products/scifinder (accessed 22/8/2014). For its
history since 1995, see Chemical Abstract Service (2007).
PubChem, launched in 2004 by the National Center for Biotechnology Information (NCBI), is a freeaccess database focusing on the biological activities of small molecules. It has experienced serious friction with SciFinder (Marris, 2005). https://pubchem.ncbi.nlm.nih.gov/about.html (accessed 22/8/2014).
The global competition of synthesizing taxol, an anti-cancer material extracted from Pacific yew, issuch a case. In 1993, R. Holton succeeded in its total synthesis. Despite millions of dollars spent, theresulting method, which has more than 40 steps, has not been used for actual drug production (Sato,2007: 78-86).
The case presented here highlights the limit of Parry’s (2004) claim concerning the growing use of what
she calls the dominance of ex-situ data mining. In addition, this line partially refutes Elvebakk’s (2006)
claim that chemistry has largely become a matter of examining information.
http://www.aics.riken.jp/en/k-computer/about/ (accessed 20/8/ 2014).
http://www.mext.go.jp/b_menu/houdou/24/09/__icsFiles/afieldfile/2012/09/04/1325265_1_1.pdf(accessed 20/8/2014).

Fukushima
15

Thus, the University of Tokyo, Fujitsu, and the Kowa Company announced the discovery of an anti-cancer drug candidate through computer-based virtual design. http://www.rcast.u-tokyo.ac.jp/research/
report/2014/140807PR.pdf (accessed 18/8/2014).

Acknowledgements
I thank all the interviewees in RIKEN and elsewhere for kindly answering my questions. I thank Geof Bowker
for introducing me to the world of knowledge infrastructure studies; Antti Silvast and two anonymous
reviewers for their constructing comments. This research was conducted with the support of grantsin-aid for scientific research from the Japan Society for the Promotion of Science, under Grant 26350359(2014–2016).

References
Althusser L & Balibar É (1970) Reading Capital. London: New Left Review.
Anderson P (1976) Considerations on Western Marxism. London: New Left Books.
Ashida J (2010) Sharing Information Using ARChem. CICSJ Bulletin 28(1): 15-16. (in Japanese)
Barley S & Bechky B (1994) In the Backrooms of Science: The Work of Technicians in Science Labs. Work andOccupations 21(1): 85-126.
Barley S & Orr J (eds) (1997) Between Craft and Science: Technical Work in U.S. Settings. Ithaca, NY: ILR Press.
Barry A (2005) Pharmaceutical Matters: The Invention of Informed Materials. Theory, Culture & Society
22(1):51-69.
Bartfai T & Lees G (2006) Drug Discovery: From Bedside to Wall Street. Amsterdam: Elsevier Academic Press.
Bataille G (1988) The Accursed Share: An Essay on General Economy. Cambridge, Mass.: Zone Books.
Bateson G (1972) Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology. Chicago: University of Chicago Press.
Baudrillard J (1981) For a Critique of the Political Economy of the Sign. Candor, NY: Telos Press.
Bensaude-Vincent B & Stengers I (1996) A History of Chemistry. Cambridge Mass.: Harvard University Press.
Borman S (2004) Rescuing Combichem: Diversity-oriented Synthesis Aims to Pick Up Where Traditional
Combinatorial Chemistry Left Off. C&EN: Science & Technology 82(40): 32-40
Bowker G (1994) Information Mythology. The World of/as Information. In: Bud-Frierman L (ed) Information
Acumen: The Understanding and Use of Knowledge in Modern Business. London: Routledge, 231-247.
Bowker G & Star S (1999) Sorting Things Out: Classification and Its Consequences. Cambridge, Mass.: MIT Press.
Bowker G, Baker K, Millerand F & Ribes D (2010) Towards Information Infrastructure Studies: Ways of Knowing
in a Networked Environment. In: J Hunsinger, J Allen & L Klastrup (eds) International Handbook of Internet
Research. Drotrecht: Springer, 97-117.
Braverman H (1974) Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century. New York:Monthly Review Press.
Burri R (2008) Doing Distinctions: Boundary Work and Symbolic Capital in Radiology. Social Studies of Science38(1): 35-62.
Carse A (2012) Nature as Infrastructure: Making and Managing the Panama Canal Watershed. Social Studiesof Science 42(2): 539-563.

21

Science & Technology Studies 29(2)Chemical Abstract Service (2007) A National Historic Chemical Landmark. Available at: http://www.acs.org/
content/dam/acsorg/education/whatischemistry/landmarks/cas/chemical-abstracts-service-commemorative-booklet.pdf (accessed 28.8.2014).
Clarke A & Fujimura J (eds) (1992) The Right Tools for the Job: At Work in Twentieth-Century Life Sciences.
Princeton: Princeton University Press.
Edgerton D (2006) The Shock of the Old: Technology and Global History Since 1900. Oxford: Oxford University
Press
Edwards P, Jackson S, Bowker G & Knobel C (2007) Understanding Infrastructure: Dynamics Tensions andDesign. NSF Office of Cyberinfrastructure. Available at: http://cohesion.rice.edu/Conferences/Hewlett/emplibrary/UI_Final_Report.pdf (accessed 10.6.2014).
Edwards P, Bowker G, Jackson S &Williams R (2009) Introduction: An Agenda for Infrastructure Studies.
Journal of the Association for Information Systems 10(5): 364-374.
Edwards P, Mayernik M, Batcheller A, Bowker G & Borgman C (2011) Science Friction: Data, Metadata, andCollaboration. Social Studies of Science 41(5): 667-690.
Edwards P, Jackson S, Chalmers M, Bowker G, Borgman C, Ribes D, Burton M & Calvert S (2013) Knowledge
Infrastructures: Intellectual Frameworks and Research Challenges. Available at: http://knowledgeinfrastructures.org/ (accessed 20. 4.2014).
Elvebakk B (2006) Network of Objects: Practical Preconditions for Electric Communication. In: Hine C (ed)New Infrastructures for Knowledge Production: Understanding E-Science. Hershey: Information Science
Pub.,120-142.
Endo A (2006) The Discovery of a New Drug Statin: Challenging Cholesterol. Tokyo: Iwanami-shoten. (inJapanese)
Epstein R (2006) Overdose: How Excessive Government Regulation Stifles Pharmaceutical Innovation. New
Haven: Yale University Press.
Epstein S (2007) Inclusion: The Politics of Difference in Medical Research. Chicago: University of Chicago Press.
Fujimura J (1996) Crafting Science: A Sociohistory of the Quest for the Genetics of Cancer. Cambridge Mass.:
Harvard University Press.
Fukushima M (2005) On Small Devices of Thought: Concepts, Etymology and the Problem of Translation. In:
Latour B & Weibel P (eds) Making Things Public: Atmosphere of Democracy. Cambridge Mass.: The MIT Press,
58-63.
Fukushima M (2013) Between the Laboratory and the Policy Process: Research Scientific Community and
Administration in Japans Chemical Biology. East Asian Science Technology and Society 7: 7-33.
Fukushima M (2015) Resilience in Scientific Research: Understanding How Natural Product Research
Rebounded in an Adverse Situation. Science as Culture. (First on line).
Fukushima M (2016) Constructing Failure in Big Biology: The Socio-technical Anatomy of Japan’s Protein
3000 Project. Social Studies of Science 46(1): 7-33.
García-Sancho M (2012) Biology Computing and the History of Molecular Sequencing: From Proteins to DNA
1945-2000. Basingstoke: Palgrave Macmillan.
Gaudillière JP & Löwy I (eds) (1998) The Invisible Industrialist: Manufactures and the Production of Scientific
Knowledge. London: Palgrave Macmillan.
Geertz C (1973) The Interpretation of Cultures. New York: Basic Books.
Godelier M, Bloch M, Claessen H, Gilmore D, Pi-Sunyer O & Tagányi Z (1978) Infrastructures, Societies andHistory [and Comments]. Current Anthropology 19(4): 763-771.

22

Fukushima
Godelier M (2011) The Mental and the Material: Thought Economy and Society. London: Verso.
Hayek F (1978) New Studies in Philosophy, Politics, Economics and the History of Ideas. London: Routledge &Kegan Paul.
Heidegger M (1968) What is a Thing? Chicago: H. Regnery.
Hine C (2006a) Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work. Social
Studies of Science 36 (2): 269-298.
Hine C (ed) (2006b) New Infrastructures for Knowledge Production: Understanding E-Science. Hershey: Information Science Pub.
Hine C (2008) Systematics as Cyberscience: Computers Change and Continuity in Science. Cambridge Mass.: theMIT Press.
Hommels A (2005) Unbuilding Cities: Obduracy in Urban Sociotechnical Change. Cambridge Mass.: the MIT
Press.
Howard D & Klare K (eds) (1972) The Unknown Dimension: European Marxism since Lenin. London: Basic Books.
Hughes T (1983) Networks of Power: Electrification in Western Society 1880-1930. Baltimore: Johns Hopkins
University Press.
Joerges B & Shinn T (2001) A Fresh Look at Instrumentation: An Introduction. In: Joerges B & Shinn T (eds)
Instrumentation between Science, State and Industry. Dortrecht: Kluwer Academic Publishers, 1-13.
Kanai C (2012) In Silico Screening Using Chemical Genomic Information. CICSJ Bulletin 30(4): 69-72. (inJapanese)
Karatani K (2014) The Structure of World History: From Modes of Production to Modes of Exchange. DurhamNorth, Carolina: Duke University Press.
KBIC (2012) Kobe Biomedical Innovation Cluster. Available at: http://www.city.kobe.lg.jp/information/
project/iryo/img/iryou.pdf (accessed 20.8.2014).
Keating P & Cambrosio A (2003) Biomedical Platforms: Realigning the Normal and the Pathological in LateTwentieth-Century Medicine. Cambridge Mass.: The MIT Press.
Keating P & Cambrosio A (2012) Cancer on Trial: Oncology as a New Style of Practice. Chicago: The Universityof Chicago Press.
Knights D &Willmott H (eds) (1990) Labour Process Theory. London: Macmillan.
Kubinyi H (2003) Drug Research: Myths Hype and Reality. Nature Reviews Drug Discovery 2: 665-668.
Latour B (2005) From Realpolitik to Dingpolitik or How to Make Things Public. In: Latour B & Weibel P (eds)
Making Things Public: Atmosphere of Democracy. Cambridge Mass.: the MIT Press, 14-41.
Lévi-Strauss C (1966) The Savage Mind. Chicago: Chicago University Press.
Lewis J & Bartlett A (2013) Inscribing a Discipline: Tensions in the Field of Bioinformatics. New Genetics andSociety 32(3): 243-263.
Lichtheim G (1971) From Marx to Hegel. New York: Herder and Herder.
Luhmann N (1980) Gesellschaftsstruktur und Semantik : Studien zur Wissenssoziologie der modernen Gesellschaft. Bd.1. Frankfurt a. M.: Suhrkamp.
Marris E (2005) Chemical Reaction. Nature 437(October): 807-809.
Marx K (1973) Grundrisse: Foundations of the Critique of Political Economy (rough draft). Harmondsworth:Penguin Books.
Marx K (1976-1981) Capital: A Critique of Political Economy. London: Penguin Books in association with New
Left Review.

23Science & Technology Studies 29(2)Marx K (1994) The Eighteenth Brumaire of Louis Bonaparte. New York: International Publishers.
Mazzucato M (2013) The Entrepreneurial State: Debunking Public vs. Private Sector Myths. New York: Anthem
Press.
McLellan D (1979) Marxism after Marx: An Introduction. London: Macmillan.
Mody C (2011) Instrumental Community: Probe Microscopy and the Path to Nanotechnology. Cambridge Mass.:
the MIT Press.
Nakaoka T (1971) The Philosophy of Factory. Tokyo: Heibonsha. (in Japanese).
Nemoto Y (2011) Decaying Infrastructure: Another Crisis that Creeps Up to Us. Tokyo: Nihonkeizai Shinbunsha.
(in Japanese)
Newman D & Cragg G (2007) Natural Products as Sources of New Drugs over the Last 25 Years. Journal of
Natural Products 70(3): 461–477.
Okuno K (2012) Chemo-genomics: Form Genome to Chemical Space. CICSJ Bulletin 30(4): 60. (in Japanese).
Parry B (2004) Trading the Genome: Investigating the Commodification of Bio-Information. Princeton: Princeton
University Press.
Petryna A (2009) When Experiments Travel: Clinical Trials and the Global Search for Human Subjects. Princeton:
Princeton University Press.
Polanyi K (1944) The Great Transformation. New York: Farrar & Rinehart.
RIKEN (2005). 88 Years of RIKEN’s History (in Japanese). Available at: http://www.riken.go.jp/r-world/info/
release/riken88/book/ (accessed 22.1.2007).
Rip A (1995) Introduction of New Technology: Making Use of Recent Insights from Sociology and Economicsof Technology. Technology Analysis and Strategic Management 7: 417-31.
Rip A (2010) Processes of Entanglement. In: Akrich M, Barthe Y, Muniesa F & Mustar P (eds) Débordements:
Mélanges offerts à Michel Callon. Paris: Presses des Mines, 381-392.
Rip A & Kemp R (1998) Technological Change. In: Rayner S & Malone E (eds) Human Choice and Climate
Change. Columbus, Ohio: Battelle Press, 327-399.
Rheinberger HJ (1997) Toward a History of Epistemic Things: Synthesizing Proteins in the Test Tube. Stanford,
CA: Stanford University Press.
Rydzewski R (2008) Real World Drug Discovery: A Chemists Guide to Biotech and Pharmaceutical Research.
Amsterdam: Elsevier.
Sahlins M (1972) Stone Age Economics. Chicago: Aldine.
Sartre JP (1976) Critique of Dialectical Reason. London: New Left Books.
Sato K (2007) Welcome to a Museum of Organic Chemistry. Tokyo: Gijutsu-hyoronsha. (in Japanese).
Shiozawa Y (2002) The Legacy of Marx: From Althusser to Complexity. Tokyo: Fujiwara-Shoten. (in Japanese)Star S (1999) The Ethnography of Infrastructure. American Behavioral Scientist 43(3): 377-391.
Star S & Bowker G (2002) How to Infrastructure? In: Lievrouw L & Livingstone S (eds) The Handbook of New
Media: Social Shaping and Consequences of ICTs. London: Sage Publications, 151-162.
Star S & Ruhleder K (1996) Steps toward an Ecology of Infrastructure: Borderlands of Design and Access forLarge Information Spaces. Information Systems Research 7(1): 111-134.
Sturdy A, Knights D & Willmott H (eds) (1992) Skill and Consent: Contemporary Studies in the Labour Process.
London: Routledge.
Ueno A (2008) Study on the Organizational Knowledge of Research Laboratories with Long Histories. Discussion Paper No.50, National Institute of Science and Technology Policy (NISTEP): 1-94. (in Japanese)24

Fukushima
Vann K & Bowker G (2006) Interest in Production: On the Configuration of Technology-Bearing Labors for
Epistemic IT. In: Hine C (ed) (2006) New Infrastructures for Knowledge Production: Understanding E-Science.
Hershey: Information Science Pub., 71-97.
Vertesi J (2014) Seamful Spaces: Heterogeneous Infrastructures in Interaction. Science Technology & Human
Values 39(2): 264-284.
Weick K & Westley F (1999) Organizational Learning: Affirming an Oxymoron. In: Clegg S, Hardy C & Nord W(eds) Managing Organizations: Current Issues. London: Sage Publications, 440-458.
Wikstrom M (2007) Chemical Biology in the USA. Swedish Institute for Growth Policy Studies. Available at:
http://www.vinnova.se/upload/dokument/Verksamhet/Bioteknik/Rapporter_Life_Science/Chemical%20
Biology.pdf (accessed 21.1.2012).
Williams R (1976) Keywords: A Vocabulary of Culture and Society. London: Fontana/Croom Helm.

25