10.6092/issn.2038-3460/17171
This paper examines translational or “bench to bedside” research – which is promoted as the application of biomedical knowledge to medical practice – at the interface between the laboratory and the clinic. Referring to the field of “metabolomics”, the post-genomic study of metabolism, it argues that efforts to make and make sense of data emerge as one of the key challenges in translational research. Focusing on case studies of translational molecular imaging, clinical databases, and surgery, I explore how metabolomics researchers and clinicians have fundamentally different notions of what data entail. I then argue that metabolomics researchers experience great difficulty not in generating but in interpreting statistical and metabolic data. Finally, I examine the future visions of translational metabolomics research to suggest that data and automation cannot replace judgment and interpretation in clinical practice. Ultimately, the paper problematizes the changing form, role, and value of “data” in post-genomic efforts to carry out translational research.
application/pdf
2038-3460
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en
Nadine Levin
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
statistics
What’s Being Translated in Translational Research? Making and Making Sense of Data between the Laboratory and the Clinic
Text.Serial.Journal
https://tecnoscienza.unibo.it/
5
Essays
2014-07-14
2023-06-12
2014-07-14
2023-07-24
91-113
TS
Nadine Levin
2014/07/14
10.6092/issn.2038-3460/17171
91
University of Exeter
2038-3460
1
statistics
en
113
What’s Being Translated in Translational Research? Making and Making Sense of Data between the Laboratory and the Clinic
https://tecnoscienza.unibo.it/article/download/17171/16072
5
                                                                              Essays

What’s Being Translated in
Translational Research?
Making and Making Sense of Data between the
Laboratory and the Clinic
!
Nadine Levin
University of Exeter
!
    Abstract: This paper examines translational or “bench to bedside” research
    – which is promoted as the application of biomedical knowledge to medical
    practice – at the interface between the laboratory and the clinic. Referring to
    the field of “metabolomics”, the post-genomic study of metabolism, it argues
    that efforts to make and make sense of data emerge as one of the key chal-
    lenges in translational research. Focusing on case studies of translational mo-
    lecular imaging, clinical databases, and surgery, I explore how metabolomics
    researchers and clinicians have fundamentally different notions of what data
    entail. I then argue that metabolomics researchers experience great difficulty
    not in generating but in interpreting statistical and metabolic data. Finally, I
    examine the future visions of translational metabolomics research to suggest
    that data and automation cannot replace judgment and interpretation in clini-
    cal practice. Ultimately, the paper problematizes the changing form, role, and
    value of “data” in post-genomic efforts to carry out translational research.

    Keywords: data; metabolomics; translational research; objectivity; statistics.

    Corresponding author: Nadine Levin, Department of Sociology, Philoso-
    phy and Anthropology, University of Exeter, Byrne House, St. German's
    Road Exeter EX4 4PJ, UK – Email: nadine.sarah.levin@gmail.com




1. Introduction

    On a warm summer morning, I am standing in a laboratory watching a
post-doctoral researcher named Sarah interact with a surgeon-in-training
named Joseph. Wearing pristine white laboratory coats that reach down
to their knees, they are working on a project to develop molecular mark-
ers of cancer within the field of metabolomics, the post-genomic study of
metabolism. After completing the clinical component of his surgical train-

!
TECNOSCIENZA
Italian Journal of Science & Technology Studies
5 (1) pp. 91-113 - ISSN 2038-3460
www.tecnoscienza.net                              2014
92                                                             Tecnoscienza - 5 (1)

ing, Joseph has elected to carry out a PhD on the metabolic properties of
cancer, in an attempt to gain insight into the integration of laboratory and
clinical approaches to disease diagnosis and treatment. Despite being
through more than a decade of surgical training, however, Joseph’s labor-
atory experience is minimal. Having spent the few months of his PhD col-
lecting samples from the surgical operating theatre, Joseph has spent little
time in the metabolomics laboratory, and is a self-proclaimed “complete
beginner”. He is adept with surgical tools, but has none of the skills re-
quired to carry out metabolomics laboratory experiments or analyze
metabolomics data.
    Throughout the morning, Sarah has been reprimanding Joseph for his
improper handling of tissues and samples, and her frustration is obvious.
Joseph has just exited and re-entered the laboratory while wearing used
gloves, and Sarah is upset that this has potentially contaminated the la-
boratory environment. Joseph, Sarah exclaims, has spread bits of tissue
across the computer, freezer, door handle, and anything else he has
touched while wearing gloves. Sarah feels that this is a reflection of Jo-
seph’s lack of care and concern for the rigor of metabolomics experi-
ments. Joseph’s response is to try to defend himself – by explaining that
in his clinical work he is not used to changing gloves with such frequency
– but Sarah is too flustered to listen to his reasoning.
    Several days later, when I speak to Sarah about this incident, she ex-
plains her frustration with Joseph’s supposed lack of interest and effort in
experimental laboratory work. Because Joseph is busy doing surgical
training and collecting clinical samples, says Sarah, he is not able to fulfill
his duties as a doctoral student-in-training. Problematically, he prioritizes
his patients over his experiments, and does not spend enough time learn-
ing how to do experiments from Sarah. As a result, Joseph has made criti-
cal mistakes handling the tissue samples and machines in the tenth floor
laboratory. “He doesn’t even know how to pipette” – Sarah says angrily –
“and he doesn’t actually know what research is”. She emphasizes, in other
words, that laboratory and clinical researchers have divergent notions of
how and why to go about biomedical research.
    Sarah’s comments speak to the fact that clinical researchers and scien-
tists are different not only in their cultures of professional training (Löwy
1996; Knorr-Cetina 1999), but also in their very notions of what consti-
tutes biomedical practice and its objects of investigation. Sarah and Jo-
seph’s conflicts over sample handling embodied what other researchers
described as a “gulf of understanding” between clinical researchers and
scientists. Joseph’s struggles with the practicalities of laboratory research
– of learning how to use particular pieces of equipment, of attempting to
balance the time demands of clinical work and laboratory research – were
struggles on a more fundamental level to understand the ideologies and
values of molecular, post-genomic research. As one clinical researcher
commented, clinicians like Joseph were “not versed in the language of
basic science, much less biochemistry and [data analysis]”. Joseph was
Levin                                                                    93
not expected to have the same skills and knowledge about metabolomics
experiments as Sarah, because his everyday clinical work entailed atten-
tion to patient needs and disease treatment. Similarly, laboratory re-
searchers like Sarah were not accustomed to “looking beyond [patterns]
into the clinical data, and trying to understand what’s actually happened
to the patient while they were in hospital”. Ultimately, and as I discuss
throughout this paper, such conflicts and tensions are reflective of the
ways in which clinical and metabolomics researchers have different prac-
tices and ways of thinking about biology. Issues of communication and
collaboration arise from different and overlapping sets of skills and
knowledge about experiments, disease, patients and data.


2. Translational Research and Data

    This paper begins with a vignette of Sarah and Joseph, because their
interaction highlights some of the fundamental and recurring challenges
faced within “translational research”. Commonly referred to as “bench to
bedside” research, translational research has become an increasingly im-
portant concept in the biomedical sciences over the past decade (Kohli-
Laven et al. 2011; Davies 2012; Davies 2013). Often portrayed as the line-
ar movement of knowledge from the laboratory to the clinic, translational
research attempts to mobilize biomedical research towards the advance-
ment of human health (O’Connell and Roblin 2006; Wainwright et al.
2006; Martin et al. 2008; Morgan et al. 2011). In such an account of trans-
lation, the laboratory and the clinical not only entail different technolo-
gies, practices, ideology, cultures, and norms, but are also brought to-
gether in unclear and contested ways (Rajan and Leonelli 2013). As this
occurs, notions of disease are developed, reinforced, and negotiated at
multiple points – and to varying degrees of success – throughout the pro-
cess of translation (Friese 2013).
    Amidst the complexity of these processes and relationships, this paper
examines translational research in the context of the post-genomic sci-
ences that seek to examine the combined effects of genes and the envi-
ronment (Davies 2013). In these fields, research is characterized by the
generation and management of data, such that statistical analyses and
computation are increasingly central to the production of knowledge
(Mackenzie 2003; Stevens 2011; Räsänen and Nyce 2013). Though there
are many ways of examining translational research, this paper asks what
we might learn from seeing translational research as an informational
process: as an often problematic attempt to create, shape, and move data
between the realms – conceptual and physical – of laboratory research
and clinical practice. It focuses on the practices and negotiations that oc-
cur at the laboratory-clinic interface, examining how disease objects are
enacted and problematized by researchers in everyday practice. At the in-
94                                                          Tecnoscienza - 5 (1)

terface between the laboratory and the clinic (Fleck [1927] 1986; Löwy
1996; Keating and Cambrosio 2003), how do efforts to make and make
sense of data emerge as one of the key challenges in translational re-
search?
    This paper examines translational research from the perspective of
metabolomics, the post-genomic study of the molecules and processes
that make up metabolism. Metabolomics is one of the fastest growing
fields of post-genomic research (Dutton 2013), which includes high-
throughput genomics, epigenetics, transcriptomics, and proteomics (Blow
2008). It involves efforts to create and analyze metabolic data with bio-
chemistry and statistics, and ultimately to interpret such data in relation
to states of health and disease (Nicholson et al. 1999; Nicholson and Lin-
don 2008). To discuss the challenges inherent in translational laboratory
research, I draw from ethnographic fieldwork in the Computational and
Systems Medicine (CSM) Laboratory at Imperial College London, one of
the leading global metabolomics research centers, as well as interviews
with members of the broader metabolomics community within the Unit-
ed Kingdom.
    Methodologically, tracking translational metabolomics research in the
CSM entailed observations of laboratory-based research on clinical sam-
ples, observations of interactions between laboratory researchers and clin-
ical practitioners at meetings, and observations of and interviews with
clinical practitioners who had been trained in laboratory methods and
were carrying out metabolomics experiments. Because of a variety of ef-
forts to implement molecular metabolic technologies in clinical settings,
the CSM represents an ideal site to examine the complexities at the la-
boratory-clinic interface of translational research. Despite published ac-
counts that allude to “clinical metabolomics” and the use of technologies
for disease diagnosis and treatment (Collino et al. 2013; Xia et al. 2013),
translational research and technologies in the CSM are not yet established
within clinical settings, and do not yet involve interactions with patients.
The research I describe in this paper involves preliminary findings to veri-
fy whether and how translational metabolomics technologies might be a
possibility. Consequently, my account of translational metabolomics re-
search itself demonstrates the non-linear, hybrid, and complex spaces and
temporalities in which translational research occurs more broadly.
    Overall, this paper argues that translational research is characterized
by different – and at times opposing – articulations of what constitutes
data, and of what value data has for biomedicine. Acknowledging that
such definitions and values are highly dependent on the context in which
data is developed and used, this paper explores how data in translational
metabolomics research is something inherently statistical, molecular,
moveable, and relational. Here, I define “data” as a series of techniques
(Hadolt et al. 2012) and practices, which exist in various material and
immaterial forms, and also entail constellations of people, technologies,
objects, ideas, and values. Thus, the paper argues that translational re-
Levin                                                                       95
search is an inherently problematic process because the laboratory and
the clinic entail different realms of practice, and thus enact different dis-
ease objects (Mol 2002). Metabolomics researchers and clinical practi-
tioners have different notions not only of how disease should be re-
searched and treated, but also of what constitutes disease and the data
that relates to it. This creates challenges in assessing what definitions,
roles, and values “data” should have in clinical practice, particularly as
diseases and individuals are articulated in informational ways (Caduff
2012).
    As metabolomics disease objects are translated into clinical practices –
or as, in reverse, clinical objects are translated into metabolomics practic-
es – the question becomes: what is and isn’t being translated, and why?
How might the case of metabolomics allow us to better understand the
challenges faced by the implementation of data-intensive approaches in
clinical settings? Or, how might the translational efforts of metabolomics
help to re-conceptualize translational research, with its emphasis on data
rather than clinical technologies and practices, in the first place?
    To begin, I argue that translational research involves negotiations
about the form and value of “data” at the interface between the laborato-
ry and the clinic. I then argue that, despite invocations to the central role
and value of data, metabolomics researchers experience great difficulty
not in generating, but in making sense of statistical and molecular data.
Finally, I argue that although laboratory researchers pose “data” as the
solution to the challenges of translational research, human interpretation
and judgment remain indispensable for the alignment of the laboratory
and the clinic, signaling the practical limitations inherent in using statisti-
cal and molecular data to make sense of disease.
    As a final note, in contrasting the laboratory with the clinic, my aim in
this paper is not to essentialize different realms of practice, by claiming
that there are fundamental differences between laboratory research and
clinical work. Nor is my aim to portray translational research as the linear
movement of laboratory technologies into clinical settings. My aim, ra-
ther, is to examine how the objects of biomedical research are articulated
at the interface between the clinic and the laboratory, and how this pro-
vides a window onto the changing visions, forms of knowledge, and val-
ues inherent in 21st century biomedicine (Rajan and Leonelli 2013). It is,
in other words, to examine how the increasing contact and hybridization
of the laboratory and clinical sciences is resulting in changing technolo-
gies, practices, and approaches to the understanding and treatment of
disease.
96                                                            Tecnoscienza - 5 (1)

3. Metabolomics Meets Clinical Practice

     In practice, metabolomics consists of a wide variety of techniques and
practices for producing, manipulating, and making sense of data. By
studying the “raw materials and products of the body’s biochemical reac-
tions, molecules that are smaller than most proteins, DNA and other
macromolecules” (Pearson 2007), metabolomics provides a snapshot of
an organism’s “metabolome”, the sum of its biochemical compounds and
reactions (Hunter 2009). In experiments, metabolomics researchers ana-
lyze the composition of urine, blood, and tissue samples with biochemis-
try technologies like nuclear magnetic resonance and mass spectrometry.
They then analyze this biochemical data with a variety of computational
techniques, many of which involve multivariate statistics, a domain of sta-
tistics involving the observation and analysis of many variables simultane-
ously, often in large data sets. Such multivariate statistics include forms of
analysis such as principal components analysis, cluster analysis, and neu-
ral networks, but more generally represent the underlying practices that
allow researchers to grapple with large volumes of complex data (Levin
2014).
     Throughout my fieldwork, researchers claimed that because metabo-
lomics provided a real-time understanding of the dynamic outcome of the
interaction between genes, metabolic pathways, and then environment, it
was ideally suited for use in clinical settings (see Bhattacharya 14 Decem-
ber 2009). Researchers worked to develop the technologies in which
complex metabolic data could be analyzed to produce molecular ways of
diagnosing disease. They envisioned that nuclear magnetic resonance and
mass spectrometry machines would exist in surgical operating rooms, al-
lowing clinical practitioners to carry out clinical trials on breast and colon
cancer, to generate biomarkers of disease, or to assess –or even predict –
adverse reactions to pharmaceutical or surgical interventions (Kinross et
al. 2011; Nicholson et al. 2012).
     Such visions of the future of medical treatment and care speak to the
kinds of science – and with this the kinds of technologies, ideologies, and
values – being created, legitimated, and used during the development of
translational research. To this end, many translational metabolomics
technologies involved attempts to find a more “objective” alternative or
complement to histopathology, a clinical technique involving the visual
analysis of stained cells under a microscope. Histopathology plays a cen-
tral role in the diagnosis of diseases like cancer, and has been the gold-
standard of tissue analysis since the early 20th century (Löwy 2009). It is
carried out by highly specialized professionals who examine stained cells
under a microscope, and who look for morphological differences between
normal and abnormal tissues. Through training and individual experi-
ence, histopathologists learn recognize abnormal tissues via morphologi-
cal characteristics like shape, size, and position of cells. Researchers em-
phasize that such objective practices to can ameliorate or circumvent the
Levin                                                                    97
subjective influence of histopathologists and clinical practitioners. They
make claims to “digital objectivity” (Beaulieu 2001; Beaulieu 2004), as
they attempt to eclipse the manual possibilities of data analysis or reveal
the “hidden meanings” of data. Metabolomics and histopathology, there-
fore, entail different “epistemic virtues” (Daston and Galison 2007: 40)
about how knowledge should be produced and how objectivity should be
achieved, as metabolomics places value on statistical measurements rather
than morphological assessments.
    One translational metabolomics project that I observed attempted to
develop a molecular technique called matrix-assisted laser desorp-
tion/ionization mass spectroscopy imaging (MALDI-MSI), which uses
mass spectrometry to develop “molecular maps” of tissues (Moody 2004).
It enables researchers to make sense of the quantitative and spatial distri-
bution of hundreds of molecules within a tissue sample, and therefore
provides a molecular complement to imaging techniques like histopathol-
ogy, immunochemistry, and fluorescence microscopy (Stoeckli et al. 2001:
493). Alaina, a post-doctoral researcher in the CSM with a background in
statistical data analysis techniques, was developing MALDI-MSI as a
“clinical platform”, as a metabolomics technology that would be used in
clinical settings to molecularly measure and diagnosis disease. MALDI-
MSI, like many of the other technologies with which metabolomics re-
searchers were working, was a relatively undeveloped and non-
standardized technology. Thus, Alaina hoped to carry out a “proof of
concept” experiment to determine whether MALDI-MSI data could be
correlated with – or could perhaps improve upon – histopathology.
    Much of Alaina’s work involved efforts to understand the data gener-
ated by MALDI-MSI, by implementing and experimenting with a variety
of statistical data analysis techniques. I watched her use such techniques
to process large data files, and also to make sense of data that was too
complex – that held too many data points and patterns – to be interpret-
ed by eye. An analysis of MALDI-MSI data was impossible to do by
hand, because each tissue slice contained twenty thousand pixels and tens
of thousands of chemical peaks. Alaina used statistical techniques to find
patterns and meanings that were “hidden” within biochemical data, and
which would otherwise be inaccessible through visual analysis. She as-
serted that they provided an “objective” and “unbiased” means for re-
searchers to explore those relationships within the data that were not
readily apparent. But as researchers like Alaina make choices about sam-
ple collection, experimental methods, or data analysis techniques, exper-
iments can never be without the influence of values, world views, or the
bias of researchers (Räsänen and Nyce 2013). Data and the techniques
through which it is produced are “always structured according to some-
body’s predispositions…and value choices all the way through” (Brooks
18 February 2013).
    By using, experimenting, and playing with statistical data analysis
techniques, Alaina produced particular understandings of biology and da-
98                                                          Tecnoscienza - 5 (1)

ta (Levin 2014). Using the computing environment and programming
language “MATLAB”, she tested how various algorithms and sequences
of code could find different patterns and points of comparison in her da-
ta. In working with such techniques, Alaina envisioned biological pro-
cesses and anatomical structures as biochemical similarities and differ-
ences, mathematical patterns, and statistical clusters. In using data-
intensive approaches to the study of biology, Alaina’s concern was not
with identifying the biological composition of the tissues, but rather with
showing their statistical relationships and meanings.
    One day, Alaina presented her MALDI-MSI research to a varied
group of clinicians based in St. Mary’s Hospital, one of the six research
hospitals run by Imperial College London. This meeting of researchers
and clinicians occurred under the banner of the National Institute for
Health Biomedical Research Council (NIHR-BRC), which – in addition
to several industrial partners and other public funders – funded several
tens of millions of pounds of translational research activities in the CSM.
The Imperial College NIHR-BRC was one of many groups established
throughout the UK within outstanding NHS and University partnerships,
with the goal of driving innovation and translational research into NHS
practice (National Institute for Health Research 2012; Imperial College
London 2014). Within the CSM, translational activities funded by the
NIHR-BRC involved both the participation of clinically-trained research-
ers in metabolomics laboratory experiments, and also the application of
metabolomics technologies and approaches to clinical issues.
    Encouraged to present her work as a tool that could be used by clini-
cians in everyday research, Alaina contrasted the benefits of “modern”
MALDI-MSI technology with “dated” histopathological approaches. She
asserted that metabolomics could provide a more “objective” view of bi-
ology, because it relied on molecular and statistical technologies rather
than the “subjective” decisions of histopathologists. MALDI-MSI would
use large quantities of molecular data, which could quantitatively measure
the extent and nature of disease, eliminating the reliance on the qualita-
tive judgments of histopathologists. Comparing MALDI-MSI and histo-
pathology, however, was not without its difficulties. At a basic level, re-
searchers struggled to compare the format and resolution of MALDI-MSI
data to those of histopathological images. While histopathological slides
were analyzed by eye and were therefore not commonly digitized,
MALDI-MSI data could only be generated, processed, and analyzed with
the aid of computers, due to its size and complexity. The two modes of
analyzing tissue, moreover, entailed fundamental issues of scale. While
histopathology resolved images of individual cells, MALDI-MSI resolved
images with “chunks of cells in each pixel”. This presented key problems
to the comparative analysis of the two techniques.
    Despite these challenges, embedded within Alaina’s presentation was
the suggestion that MALDI-MSI could one day provide a superior alter-
native to histopathology. Though most researchers working on transla-
Levin                                                                         99
tional projects acknowledged that their work would likely operate in par-
allel to rather than replace existing clinical practices, Alaina insisted:
“You would want to show that you can do more than histopathology”.
Her comments hinted at the notions of “digital objectivity” (Beaulieu
2001) embedded within metabolomics, as researchers made claims to
knowledge through statistical data and automation, rather than the
“manual possibilities” of clinical judgment and interpretation. In re-
sponse, the clinicians to whom Alaina was presenting began a heated dis-
cussion. They wondered: how could a data-driven approach to biology
replace a time-honored practice like histopathology? Would MALDI-
MSI be able to inform disease diagnosis and treatment with the same suc-
cess as histopathology, or would it fall prey to the false promises of other
post-genomic technologies?
    In their discussion, the clinicians raised concerns that while histo-
pathology visualized biological markers within and between cells,
MALDI-MSI visualized tissue as a “molecular signature of anatomy”, as a
set of statistical signals and patterns. MALDI-MSI, the clinicians
acknowledged, could provide a new perspective on the biochemical com-
position of tissue, but its use in reasoning through the diagnosis and
treatment of disease was less clear. As one clinician proclaimed:

            Of course you’re going to add a whole lot of information that
        we simply don’t have. But the real thing is to take the information
        and go back to the tissue, and say: ‘What is this telling us about
        the pathogenesis that we would not know in any other way’?

    At stake in this discussion was a challenge to the long-standing, and
therefore institutionalized, practice of histopathology. However, also at
stake were the different understandings of disease – and of the form and
role of data – that metabolomics and histopathological practices es-
poused. For the clinicians, histopathology was valuable not because it
shed light on tissue structures, but rather because it provided morpholog-
ical markers of vascular invasion or tumor grade and stage, which though
visual and qualitative, could be directly linked to disease diagnosis and
treatment. Consequently, such an encounter between metabolomics re-
searchers and clinicians hinted at the different notions of “data” and
“disease” that existed at the laboratory-clinic interface.
    In a similar contrast between metabolomic and clinical data, I spoke
to several researchers involved in efforts to apply the data analysis tech-
niques commonly used in metabolomics to clinical databases. These clini-
cal databases consisted of routine physiological measurements, tests, and
observations – such as blood glucose, blood oxygen levels, heart rate –
with which researchers attempted to. This was an effort to visualize the
complexity of clinical data, and to uncover previously hidden patterns or
relationships between markers and outcomes of disease. Overall, this
work embodied metabolomics’ idea that the best way to learn about dis-
100                                                          Tecnoscienza - 5 (1)

ease was to collect as much data as possible, in a process one of the re-
searchers described as “data mining for improved information recovery”.
Creating more powerful tools to aggregate and look for statistical rela-
tionships within large volumes of data, researchers believed, would even-
tually translate into the improved diagnosis and treatment of disease.
     Noah, a research fellow in the CSM who like Alaina had a back-
ground in statistical data analysis techniques, commented on the chal-
lenges inherent in carrying out such data analysis on a clinical dataset col-
lected from the Intensive Care Unit (ITU) of St. Mary’s Hospital. This
was part of a translational research initiative within the CSM to integrate
existing clinical data with “omics” data derived from metabolomics ex-
periments into a broader database, which would contain a heterogeneous
collection of data that could later be correlated with samples stored in bio
banks (Mitchell and Waldby 2010). In building such a database, metabo-
lomics researchers attempted to maximize the amount of data – in the sta-
tistical sense of the word – that could be made available and used to make
diagnoses and predictions about patients. But first, for metabolomics to
work in clinical settings, researchers emphasized that statistical and mo-
lecular data had to interface with – rather than replace – existing clinical
data. Thus, the goal of the research was not only to establish the use of
new metabolomics technologies within clinical settings, but also to find
new and statistical ways of interpreting existing clinical data.
     For Noah working with clinical data would be no different from
working with the types of data metabolomics researchers routinely used.
Though the type of data contained within the ITU dataset was certainly
different, by performing certain steps and methods, it could be analyzed
in the same informational way as metabolomics data. This involved build-
ing a “data matrix” – a two-dimensional table composed of rows and col-
umns filled with numbers – and looking for patterns with complex statis-
tical methods. “You build a table in a consistent way” – Noah said –
“And after that, all of your data is always the same”. For Noah, data ex-
isted in a specific, multivariate statistical form.
     As Noah discussed his attempts to analyze clinical data, he not only
revealed the value placed on the collection and analysis of large volumes
of data, but also indicated that what counted as “data” was highly specific
to metabolomics practices. For Noah, like Alaina, data consisted of statis-
tical patterns and relationships. It relied on computerized algorithms, and
ultimately commented on statistical features – referred to with the lan-
guage of “parameters” or “signals” – rather than disease processes. Thus,
as Noah emphasized that the study of disease could be optimized with
particular techniques for manipulating data, he highlighted how the
translational practice of metabolomics was enabled through large and
specially-formatted datasets, and required the practice of particular tech-
niques for generating and manipulating data.
     Ultimately, in the examples of metabolomics research on tissues and
clinical data, researchers place value on the collection and analysis of
Levin                                                                    101
complex statistical data, which they claim has the potential to transform
disease diagnosis and treatment. However, what counts as “data” in such
cases is highly contextual, and metabolomics researchers only attribute
meaning to data once it takes on a particular – quantitative and statistical
– form. While histopathological and physiological observations engender
certain practices and meanings for medical practitioners, they do not on
their own count as “data” within metabolomics research. In this case,
translational research at the interface between the laboratory and the clin-
ic entails not only particular types and forms of data, but also different
notions of the role and value that data hold within medical practice. As
metabolomics researchers attempt to generate and use statistical data,
they also attempt to imbue such data with new and “better” meanings.


4. Making Sense of Metabolomic Data

    While the previous section explored the form and value engendered
by “data” in translational metabolomics research, this section explores
the challenges faced by researchers in the interpretation of such data.
Throughout my fieldwork, metabolomics researchers emphasized the re-
curring challenges of making sense of statistical and molecular data in re-
lation to disease processes and outcomes. Despite the overt value they
placed on the production and use of multivariate forms of data, they still
acknowledged that the interpretation of such data posed a serious chal-
lenge to the application of metabolomics technologies to clinical issues.
This section explores, therefore, how metabolomics researchers struggle
to translate their findings into clinical practice, and to make their results
meaningful in relation to clinical epistemologies or understandings of the
body, which are oriented around patient care and disease outcomes.
    I spoke with a former researcher in the CSM, who after moving to a
different research group to work on the statistical analysis of large ge-
nomic datasets, had a unique perspective on the strengths and challenges
that faced the field of metabolomics. Metabolomics, he emphasized, was
very successful at the “analytical side” of experiments, at identifying and
quantifying the biochemical components within biological fluids and tis-
sues. The field had discovered a large number of biomarkers, the quanti-
fiable end-products of metabolism that could be correlated with health
and disease, and had generated a large number of medium- and high-
impact papers. He emphasized, however, that in spite of its research
productivity metabolomics struggled to relate statistical data to specific
genes, metabolic pathways, or bodily systems. Statistical patterns, like
those generated in MALDI-MSI experiments, had no inherent or pre-
existing connections to clinical outcomes.
    Similarly, another researcher in the CSM suggested that the main chal-
lenge faced by metabolomics was not in generating but in interpreting
102                                                                 Tecnoscienza - 5 (1)

statistical data. She said:

           It’s not necessarily that it’s too much information. It’s just that
      it’s complicated to put it all together in a meaningful fashion...
      We’re still at a stage where, okay, x metabolite goes up and y me-
      tabolite goes down. And we don’t really know what that means.

     She emphasized that metabolomics was successful at establishing sta-
tistical relationships, or at correlating changes in metabolite levels to dis-
ease states. It struggled, in contrast, to relate such results to meaningful
biological pathways or disease symptoms. She questioned whether the bi-
ochemical and statistical methods of metabolomics experiments could be
translated, applied to, and used in clinical settings. It was all too easy to
“hide behind the numbers” in metabolomics experiments, especially
when working with statistical relationships and outputs that were abstract
and easy to manipulate.
     In general, the interpretation of metabolomics data was made difficult
for several reasons. Firstly, the same biochemicals tended to recur across
multiple experiments and analyses, making their biological relevance un-
clear. As a doctoral student commented to me, metabolomics experi-
ments tended to highlight the biological role of the same “common” bio-
chemicals. For example, lactate and hippurate, which play a role in cellu-
lar respiration and microbial metabolism respectively, were features of
almost every experiment. The recurrence of common biochemicals was
due to the fact that many compounds were involved in multiple metabolic
pathways, which one researcher described as “metabolic hubs” of activi-
ty. The biochemicals commonly detected in experiments were the end re-
sult of multiple biological processes occurring simultaneously within an
organism. Researchers questioned the spatial, temporal, and environmen-
tal relevance of their data (see Rajan and Leonelli 2013, 471-72). They
sought to determine if common metabolites were detected because of dis-
ruptions of cellular respiration, the use of particular medications, or the
ingestion of certain foods. In this way, the complex nature of metabolom-
ics data – the fact that it was the end product of many biological process-
es – made its interpretation challenging.
     The interpretation of metabolomics data was made difficult, secondly,
because the biological origins of the biochemicals that metabolomics
technologies detected were not always clear. I spoke with a researcher
named Thomas about the challenges involved in making sense of the data
generated by a technology called the “intelligent knife” (Balog et al.
2013). This was a surgical device that used mass spectrometry to analyze
the molecular composition of tissues cut during electrocautery, in which
the standard surgical blade was replaced by a device that cauterized and
cut tissue with an electric current. According to Thomas, one of the main
issues with making sense of the data generated by the intelligent knife was
in figuring out what exactly the machine was measuring. The intelligent
Levin                                                                      103
knife was an incredibly complicated device that attempted to make real-
time measurements and statistical analyses about the spatial composition
and nature of tissues. Researchers using the technology therefore had dif-
ficulty understanding whether the machine detected biochemicals from
tissues at the surface of or from deep within the surgical incision. Know-
ing the origin of the biochemicals was fundamental, because it had impli-
cations for the types of molecules, cells, or biological pathways involved
in surgical treatment.
    The interpretation of metabolomics data was made further difficult by
the uncertainty surrounding the range of biochemicals that devices like
the intelligent knife were able to detect. The intelligent knife, like other
analytical instruments, had inherent capabilities and limitations that made
it suitable for the detection of a certain range of biochemicals. This, as
Thomas said, raised questions about whether the machine would be able
to detect those biochemicals that were implicated in health and disease.
Thomas emphasized that the intelligent knife could only detect fat-
containing molecules that occurred at the surface of cells, whose im-
portance in surgery and disease diagnosis was unknown. Metabolomics
researchers were, as Thomas described, “at the mercy” of the machine’s
technical capabilities. He said: “There’s so much of a metabolome out
there, and we’re just able to tell tissues apart by lipids because that’s what
we see”. Though they were able to build customized statistical algorithms
to analyze the machine’s data, they had to operate within the parameters
of the machine’s commercially-determined settings. Thus, metabolomics
researchers struggled to interpret the biological meaning of the intelligent
knife data, primarily because they could not always say whether the bio-
chemicals it detected played a key biological role.
    In conclusion, this section suggests that the broad challenge facing
metabolomics researchers is that of the interpretation – rather than the
generation – of data. Researchers continually question how their statisti-
cal and biochemical data can be made meaningful or “translated” into
metabolic pathways or bodily functions. The links between data and
states of health and disease are not pre-given or objective, but rather are
enacted through the everyday work of metabolomics research. As such,
metabolomics researchers struggle not only to produce situated forms
and values of data, but also, and perhaps even more importantly, to make
such data meaningful in relation to clinically-relevant understandings of
the human body. Such an emphasis and value on the generation and anal-
ysis of statistical data therefore side-steps a critical bottleneck in the pro-
cess of translational research: it is not an easy or trivial question of how
metabolomics data can or should be made meaningful in relation to dis-
ease treatments and outcomes.
104                                                            Tecnoscienza - 5 (1)

5. Translation and Interpretation

     In the previous sections, I explored the processes and challenges asso-
ciated with the movement of knowledge between the metabolomics la-
boratory and the clinic. This section steps back slightly from the realm of
everyday metabolomics practices, in order to examine how researchers
envision the future “translation” of metabolomics technologies into clini-
cal practice. Such future visions tell us about the different forms, uses,
and values of data that exist at the laboratory-clinic interface. They por-
tray translational research as an inevitable result of the development of
sophisticated technologies and the collection of large volumes of data.
But they also implicate, as mentioned in the previous section, fundamen-
tal issues of data interpretation. Consequently, this section asks: what role
does interpretation and judgment have in translational research, and how
does this contrast with metabolomics’ emphasis on the value of particular
kinds of data?
     I spoke at length with William – a surgeon in the NHS who had com-
pleted his doctoral training in the CSM – about the future visions and
possibilities of metabolomics technologies in clinical settings. William was
the clinical coordinator of many of the CSM’s translational research pro-
jects funded by the NIHR-BRC, and as one of the first clinician-
researchers to spend an extended amount of time doing metabolomics re-
search in the CSM, he had developed a concrete vision of the translation
of metabolomics technologies to clinical settings. His work was therefore
part of the growing impetus to bring academic medicine into contact with
laboratory research through the figure of the “clinician-scientist”, who
would provide input on the development of laboratory technologies
which were being translated into clinical practice (Wilson-Kovacs and
Hauskeller 2011). It articulated the growing expectation within the UK
that research occurs concurrently with clinical practice, and that clinician-
researchers are the “essential conduit” for the translation of laboratory
research from “bench to bedside and back” (Nature Publishing Group
2004).
     For William, metabolomics would form a key platform for developing
“surgical metabolomics” technologies, and would give researchers the
unique ability to measure, model, and provide data about surgical inter-
ventions. William emphasized that surgeons had little knowledge of the
metabolic pathways underpinning surgical treatments, or of how patients
responded to things like anesthesia, drug treatments, or nutritional inter-
ventions. “It’s a dense, complex system…and in surgery we have no
measure of this system at all, it’s totally primitive”. William, like other re-
searchers, turned to metabolomics for a way to make surgery more “sci-
entific” and to provide quantitative data about patients before, during,
and after surgical interventions. Researchers hoped that metabolomics
would transform surgery, like histopathology and other clinical endeav-
ors, from a profession based on subjective human experience to a techno-
Levin                                                                             105
logical intervention based on objective data. In asserting that surgical
knowledge was subjective, researchers placed value on the data practices
and techniques of metabolomics, and in particular on statistical and mo-
lecular techniques for diagnosing and treating disease.
    As I spoke to William about the development of surgical metabolom-
ics, he painted a vision of the future in which metabolomics technologies
would be neatly packaged into self-contained boxes, and would involve
easy-to-use, push-button interfaces. Such visions of the future, while they
are clearly hypothetical, provide insight into the ideas and values that re-
searchers have about the present and expect for the future (Brown and
Michael 2003; Wainwright et al. 2006; Martin et al. 2008). William valued
the use of post-genomic data in clinical practice, and, like other metabo-
lomics researchers, emphasized the importance of generating and using
large volumes of statistical data. He said:

            It may take my whole career, so that I can walk into an operating
        theatre, and there can be a machine there that will be a shoebox
        sized mass spectrometer. And I’ll drop the sample in, and the data
        will come out [as a] lovely, clear data visualization. And it will tell
        me the information that I need.

    Before this could happen, researchers emphasized that metabolomics
data would need to be transformed into a format that made it amenable
to clinical use by surgeons. Like Noah’s work in clinical database, much
of the CSM’s translational research involved not only the reformatting of
clinical data, but also the development of interfaces that would enable
surgeons to combine metabolomics data with existing surgical techniques
and procedures. As one clinician-researcher commented:

             With a lot of these, you need an actual surgeon to be able to run
        it. You’re not going to take one of our massive mass spec[trometer]s
        and shove it in, and expect someone to know how to use it. So you
        hope eventually it will be…more of a ‘yes no’ answer to things.
        Something that’s easier to interpret.

    There were considerable practical limitations inherent in engaging
with metabolomics data – both in its form and visualization – during the
diagnosis and treatment of disease. Translational technologies would rely
not only on surgeons’ ability to use them, but also on surgeons’ ability to
interpret them, particular in relation to existing clinical data. As another
clinician-researcher emphasized:

             Clinicians want simplicity, they crave it in their decision mak-
         ing…They all want a simple test, a simple score, that gets them a
         yes-no answer…And what’s the balance…at what point does
         complexity become too difficult as a bedside test?
106                                                          Tecnoscienza - 5 (1)


    Such comments not only signal the practical limits to engaging with
statistical data that does not have an established or obvious meaning, but
also signal the challenges inherent in aligning laboratory and clinical prac-
tices. Despite assertions that surgery should move away from subjective
judgments and towards technological innovations, several clinician-
researchers remarked how in their everyday experiences with patients and
bodies, they used a combination of medical instruments and bodily know-
how (Prentice 2005; Carmel 2012) to “sense” patients’ states of health
and disease. Clinician-researchers, like histopathologists, relied on trained
judgment and interpretation, as well as understandings of disease as
something dynamic and normative (Canguilhem 1989), to assess patients
and decide a course of treatment. Thus, for clinician-researchers working
to apply metabolomics technologies to surgery, the alignment of laborato-
ry and clinical practices did not happen automatically, but instead re-
quired active clinical decision making and judgment. Seen in this way,
conflicts in the realm of translational research arose not only because of
conflicts in the practices used to generate and move data, but also be-
cause of the different values and forms placed on data at the laboratory-
clinic interface.
    In articulating the differences between laboratory and clinical practic-
es, my aim is not to elevate qualitative interpretation and judgment over
the quantitative measurements and inferences that characterize metabo-
lomics research. Clinical practitioners themselves rely on quantitative da-
ta, and reduce patients to objective and docile bodies (Hirschauer 1991;
Foucault 2003). Moreover, as clinicians place value on human intuition
and leverage their working knowledge of patients in hospital settings,
they attempt to assert their authority and control over certain aspects of
medical practice. Clinicians see the influx of medical technologies –
which have the potential to “deskill physicians” (Reardon 2011, 104) – as
a threat to medical institutions and realms of power. However, amidst
such generalizations about the capacities of clinical practitioners to carry
out and understand certain types of research, what emerges is the central
role that the “human” capacities of interpretation and judgment play in
medical practice. Despite technological advances and data-intensive prac-
tices, clinical decision-making remains central to patient care, such that
medical practitioners are constantly combining technological information
with human intuition. Translational research, it becomes clear, relies on
the interpretive abilities of medical practitioners just as much as data.
    Throughout my fieldwork, it was not only clinicians but also metabo-
lomics researchers themselves who articulated a reliance on human inter-
pretation and judgment, and – to a point – a distrust of statistical automa-
tion. As I have discussed throughout this paper, researchers emphasized
that multivariate statistics revealed otherwise hidden aspects of biochemi-
cal data and allowed them to surpass the limitations of visual analysis.
However, researchers also conceded that handling and inspecting their
Levin                                                                   107
data manually was critical for assuring the quality of their experimental
methods and conclusions. One researcher emphasized that it was im-
portant not to completely rely on computers to carry out data analysis, as
she said: “I’m not sure how much I really trust the data”. Many research-
ers, she asserted, used statistical analysis as an initial means to explore
their data, and then used manual inspection to look for interesting differ-
ences. Likewise, another researcher emphasized that it was important not
to “let yourself be fooled by the data”. For him, statistics were merely a
tool, rather than an end-all-be-all for determining if experimental conclu-
sions were obtained by chance. It was necessary to, as a leading metabo-
lomics researcher with a background in engineering described, “keep the
human in the loop”.
     Thus, clinical researchers and metabolomics researchers alike
acknowledge the central role that human interpretation and judgment
play in the development, interpretation, and implementation of metabo-
lomics technologies within the clinic. Emerging technologies and human
capacities are interdependent, such that technologies can serve to re-
arrange – but never truly replace – human judgment. As Keating and
Cambrosio (2003, 59) argue, though technologies attempt to automate
biology and transform it into an information science, human judgment is
still required to turn “quantitative differences […] into qualitative dis-
tinctions”.. Thus, this section explores how visions of the technological
and data-driven future of translational metabolomics research conflict
with the inherent appreciation – among both medical practitioners and
metabolomics researchers themselves – of the interpretive practices of
clinical medicine. Though technological innovation, through the creation
and value of particular types of “data” is posed as a solution to the prob-
lem of translation, human interpretation emerges as a fundamental neces-
sity for the alignment of the laboratory and the clinic. Data cannot exist
independently of human practices, such that the negotiation of the form
and value of data remains one of the main challenges facing translational
research.


6. Conclusion

    This paper considers how translational research, in attempting to
bring metabolomics technologies to the clinic, involves tensions between
research practices, disease objects, and data. Processes of translation be-
tween laboratories and clinics are fundamentally problematic, because the
laboratory and the clinic entail different realms of practice and enact dif-
ferent biological and disease objects. Thus, metabolomics researchers and
clinical researchers have fundamentally different notions not only of how
disease should be researched and treated, but also of the form and value
data about disease should have.
108                                                           Tecnoscienza - 5 (1)

    Though translational research is a complex and dynamic process, this
paper examines it as an informational practice for generating and making
sense of data at the interface between the laboratory and the clinic.
Through metabolomics technologies and practices, tissues and diseases
come to be understood as statistical patterns and numerical relationships,
and value is placed on the production and analysis of particular kinds –
large volumes and multivariate statistical forms – of data for the ad-
vancement of human health. Despite the fact that data is posed as increas-
ingly central to medical practice, metabolomics researchers struggle to in-
terpret biochemical and statistical data in relation to patient outcomes,
presenting fundamental challenges to the “translation” of data into un-
derstandings of and treatments for disease. Thus, as metabolomics por-
trays translation as a technological feat, it raises key questions about the
ability of data alone to align the practices and values of the laboratory and
clinic. Data and automation cannot triumph or replace trained judgment
and interpretation. Such human capacities are still central to the applica-
tion of metabolomics research to clinical issues, and cannot – at least at
this point in time – be overcome with complex types or large volumes of
data.
    In the end, translation is clearly much more than an informational
practice, as it involves a diverse range of actors, materials, locales, disci-
plines, funding strategies, and ideologies. By showing the practices, val-
ues, and ideas at stake in thinking through “data” as something central to
translational research, this paper invites us to question the dominant cat-
egories, timescales, and dynamics involved in translational research.
Though the “translation” of biomedical research to clinical practice is of-
ten portrayed as linear and unproblematic, translation is much more
messy and complicated in practice. Ultimately, by questioning the chal-
lenges involved in alignment of the laboratory and the clinic, this paper
addresses the ways in which the very notion of “bench to bedside” be-
comes a possibility for contemporary biomedicine.
    In conclusion, this paper is concerned with how we might think about
the act and effect of “translation” in metabolomics research, and even
more broadly in the range of post-genomic fields that are attempting to
generate knowledge about life with large volumes of data. Of central con-
cern is not the existence of data-intensive sciences per se, but rather the
types of knowledge they are able to capture, as well as the values they
place on particular ways of understanding and intervening into human
health and disease. Data on their own are not neutral or self-evident: they
are able to capture and measure some things but not others. At stake in
my discussion of translation, therefore, is the question: how do competing
practices affect how biomedical research gets done? How does an insist-
ence on the value and use of data promote certain types of medical
knowledge and care over others? Returning to the central premise of this
paper, how might we use the case of metabolomics to better understand
what kinds of translation are occurring, or to think through what is and
Levin                                                                        109
isn’t being translated and why? How might we use the notion of “transla-
tion” to interrogate the challenges and limits faced by the use of data to
understand biology and disease?
    In the end – or at least at this point in the evolution of the field of
metabolomics – not much is being translated between the metabolomics
laboratory and the clinic. This lack of translation emerges because of di-
verging understandings of what constitutes data, and also because of a
failure to relate statistical findings to existing clinical methods for diag-
nosing and treating disease. While metabolomics researchers think that
more data will enhance translational research, clinicians are less optimis-
tic. They overtly recognize, like many metabolomics researchers as well,
that the human body is difficult to understand and predict. Based on
first-hand experience, clinicians acknowledge that biology is utterly com-
plex, dynamic, and unpredictable: patients respond to pharmaceutical
and surgical interventions in different ways, and conditions like obesity
and cancer have variable symptoms and etiologies.
    Here, what I want to suggest is that as metabolomics ideas and tech-
nologies are translated into clinical practices, statistical notions of “data”
struggle to capture dynamic and vitalistic (Canguilhem 1989) notions of
disease. The utter complexity of biology presents very real challenges to
translation in relation to processes of information, quantification, statis-
tics, and biochemistry. Translation entails the movement of some types of
knowledge over others, as those carrying out the translation select the
meanings and values they wish to convey. Thus, amidst the rhetoric of
technological progress, are there aspects of biology, bodies, and health
that cannot be captured through statistics? With this in mind, the ques-
tion becomes not whether statistical and biochemical measures of disease
can replace human interpretation and judgment, nor whether the labora-
tory and the clinic entail different practices and disease objects. The ques-
tion becomes, rather, if post-genomic ways of engaging with disease can
capture the utter complexity of the human body (Levin 2014).



References

Balog, J., Sasi-Szabó, L., Kinross J., Lewis, M.R., Muirhead, L.J., Veselkov, K.,
   Mirnezami, R., Dezső, B., Damjanovich, L., Darzi, A., Nicholson, J.K. and
   Takáts, Z. (2013) Intraoperative Tissue Identification Using Rapid Evaporative
   Ionization Mass Spectrometry, in “Science Translational Medicine”, 5 (194),
   pp. 1-11.
Beaulieu, A. (2001) Voxels in the Brain: Neuroscience, Informatics and Changing
   Notions of Objectivity, in “Social Studies of Science”, 31 (5), pp. 635-80.
Beaulieu, A. (2004) From Brainbank to Database: The Informational Turn in the
110                                                               Tecnoscienza - 5 (1)

      Study of the Brain, in “Studies in History and Philosophy of Science Part C”,
      35 (2), pp. 367-90.
Bhattacharya, A. (2009) Surgeons Get Real-Time Tissue Profiling, http://www.
   nature.com/news/2009/091214/full/news.2009.1128.html.
Blow, N. (2008) Metabolomics: Biochemistry's New Look, in “Nature”, 455 (7213),
   pp. 697-700.
Brooks, D. (2013) What Data Can't Do, http://www.nytimes.com/2013/02/19/
   opinion/brooks-what-data-cant-do.html.
Brown, N. and Michael, M. (2003) A Sociology of Expectations: Retrospecting Pro-
   spects and Prospecting Retrospects, in “Technology Analysis & Strategic Man-
   agement”, pp. 15 (1), pp. 3-18.
Caduff, C. (2012) The Semiotics of Security: Infectious Disease Research and the
   Biopolitics of Informational Bodies in the United States, in “Cultural anthro-
   pology”, 27 (2), pp. 333-57.
Canguilhem, G. (1989) The Normal and the Pathological, Cambridge, MA, MIT
   Press.
Carmel, S. (2012) The Craft of Intensive Care Medicine, in “Sociology of Health &
   Illness”, 35 (5), pp. 731-45.
Collino, S., Martin, F.P.J. and Rezzi, S. (2013) Clinical Metabolomics Paves the
   Way Towards Future Healthcare Strategies, in “British Journal of Clinical
   Pharmacology”, 75 (3), pp. 619-29.
Daston, L. and Galison, P. (2007) Objectivity, New York, Zone Books.
Davies, G. (2012) What is a Humanized Mouse? Remaking the Species and Spaces
   of Translational Medicine, in “Body & Society”, 18 (3-4), pp. 126-55.
Davies, G. (2013) Arguably Big Biology: Sociology, Spatiality and the Knockout
   Mouse Project, in “BioSocieties”, 8, pp. 417-31.
Dutton, G. (2013) Emerging Market Predicted to Double in the Next Five Years to
   Well over One Billion Dollars, in “Genetic Engineering & Biotechnology
   News”, 33 (13).
Fleck, L. ([1927] 1986) Some Specific Features of the Medical Way of Thinking, in
    R.S. Cohen and T. Schnelle (eds.), Cognition and Fact – Materials on Ludwik
    Fleck, Dordrecht, D. Reidel Publishing Company.
Foucault, M. (2003) The Birth of the Clinic, London, Routledge.
Friese, C. (2013) Realizing Potential in Translational Medicine: The Uncanny
    Emergence of Care as Science, in “Current Anthropology”, 54 (S7), S129-S138.
Hadolt, B., Hörbst, V. and Müller-Rockstroh, B. (2012) Biomedical Techniques in
   Context: On the Appropriation of Biomedical Procedures and Artifacts, in
Levin                                                                          111
    “Medical anthropology”, 31 (3), pp. 179-95.
Hirschauer, S. (1991) The Manufacture of Bodies in Surgery, in “Social Studies of
   Science”, 21 (2), pp. 279-319.
Hunter, P. (2009) Reading the Metabolic Fine Print, in “EMBO Reports”, 10 (1),
  pp. 20-23.
Imperial College London (2014) NIHR Imperial Biomedical Research Centre,
   http://imperialbrc.org/.
Keating, P. and Cambrosio, A. (2003) Biomedical Platforms: Realigning the Nor-
   mal and the Pathological in Late-Twentieth-Century Medicine, Cambridge,
   MIT Press.
Kinross, J. M., Holmes, E. A., Darzi, W. and Nicholson, J. K. (2011) Metabolic
   Phenotyping for Monitoring Surgical Patients, in “Lancet”, 377 (9780), pp.
   1817-1819.
Knorr-Cetina, K. (1999) Epistemic Cultures: How the Sciences Make Knowledge,
   Cambridge, MA, Harvard University Press.
Kohli-Laven, N., Bourret, P., Keating, P. and Cambrosio, A. (2011) Cancer Clini-
   cal Trials in the Era of Genomic Signatures: Biomedical Innovation, Clinical
   Utility, and Regulatory-Scientific Hybrids, in “Social Studies of Science”, 41
   (4), pp. 487-513.
Levin, N. (2014) Multivariate Statistics and the Enactment of Biological Complexity
   in Metabolic Science, in “Social Studies of Science”, http://thesp.
   leeds.ac.uk/files/2014/04/MV_Stats_Complexity_STS_Levin_20141.pdf.
Löwy, I. (1996) Between Bench and Bedside: Science, Healing, and Interleukin-2 in
   a Cancer Ward, Cambridge, Harvard University Press.
Löwy, I. (2009) Preventive Strikes: Women, Precancer, and Prophylactic Surgery,
   Baltimore, Johns Hopkins University Press.
Mackenzie, A. (2003) Bringing Sequences to Life: How Bioinformatics Corporealiz-
   es Sequence Data, “New Genetics and Society”, 22, pp. 315-32.
Martin, P., Brown, N. and Kraft, A. (2008) From Bedside to Bench? Communities
   of Promise, Translational Research and the Making of Blood Stem Cells, in
   “Science as Culture”, 17 (1), pp. 29-41.
Mitchell, R. and Waldby, C. (2010) National Biobanks: Clinical Labor, Risk Pro-
   duction, and the Creation of Biovalue, in “Science, Technology & Human Val-
   ues”, 35 (3), pp. 330-355.
Mol, A. (2002) The Body Multiple: Artherosclerosis in Practice, Durham, NC,
   Duke University Press.
Moody, G. (2004) Digital Code of Life: How Bioinformatics Is Revolutionizing Sci-
  ence, Medicine, and Business, Hoboken, NY, John Wiley & Sons.
112                                                              Tecnoscienza - 5 (1)

Morgan, M., Barry, C.A., Donovan, J.L., Sandall, J., Wolfe, C.D.A. and Boaz, A.
  (2011) Implementing ‘Translational’ Biomedical Research: Convergence and
  Divergence among Clinical and Basic Scientists, in “Social Science & Medi-
  cine”, 73 (7), pp. 945-952.
National Institute for Health Research (2012) Biomedical Research Centres,
   http://www.nihr.ac.uk/infrastructure/Pages/infrastructure_biomedical_resear
   ch_centres.aspx.
Nature Publishing Group (2004) Lost in Clinical Translation, in “Nature Medi-
   cine”, 10 (9), pp. 879.
Nicholson, J.K., Holmes, E., Kinross, J.M., Darzi, A.W., Takats, Z. and Lindon,
   J.C. (2012) Metabolic Phenotyping in Clinical and Surgical Environments, in
   “Nature”, 491 (7424), pp. 384-392.
Nicholson, J.K. and Lindon, J.C. (2008) Systems Biology: Metabonomics, in “Na-
   ture”, 455 (7216), pp. 1054-1056.
Nicholson, J.K., Lindon, J.C. and Holmes, E. (1999) Metabonomics: Understand-
   ing the Metabolic Responses of Living Systems to Pathophysiological Stimuli Via
   Multivariate Statistical Analysis of Biological NMR Spectroscopic Data, in “Xe-
   nobiotica”, 29 (11), pp. 1181-1189.
O’Connell, D. and Roblin, D. (2006) Translational Research in the Pharmaceutical
   Industry: From Bench to Bedside, in “Drug Discovery Today”, 11 (17-18), pp.
   833-838.
Pearson, H. (2007) Meet the Human Metabolome, in “Nature”, 446 (7131), pp. 8.
Prentice, R. (2005) The Anatomy of a Surgical Simulation: The Mutual Articulation
   of Bodies in and through the Machine, in “Social Studies of Science”, 35 (6),
   pp. 837-866.
Rajan, K.S. and Leonelli, S. (2013) Biomedical Trans-actions, in “Public Culture”,
    25 (3), pp. 463-75.
Räsänen, M. and Nyce, J. M. (2013) The Raw Is Cooked: Data in Intelligence Prac-
   tice, in “Science, Technology & Human Values”, 38 (5), pp. 655-677.
Reardon, J. (2011) The ‘Persons’ and ‘Genomics’ of Personal Genomics, in “Per-
   sonalized Medicine”, 8 (1), pp. 95-107.
Stevens, H. (2011) On the Means of Bio-Production: Bioinformatics and How to
    Make Knowledge in a High-Throughput Genomics Laboratory, in “BioSocie-
    ties”, 6 (2), pp. 217-242.
Stoeckli, M., Chaurand, P., Hallahan, D.E. and Caprioli, R.M. (2001) Imaging
    Mass Spectrometry: A New Technology for the Analysis of Protein Expression in
    Mammalian Tissues, in “Nature Medicine”, 7 (4), pp. 493-496.
Wainwright, S.P., Williams, C., Michael, M., Farsides, B. and Cribb, A. (2006)
Levin                                                                         113
    From Bench to Bedside? Biomedical Scientists’ Expectations of Stem Cell Sci-
    ence as a Future Therapy for Diabetes, in “Social Science & Medicine”, 63 (8),
    pp. 2052-2064.
Wilson-Kovacs, D.M. and Hauskeller, C. (2011) The Clinician-Scientist: Profes-
   sional Dynamics in Clinical Stem Cell Research, in “Sociology of Health & Ill-
   ness”, 34 (4), pp. 497-512.
Xia, J., Broadhurst, D.I., Wilson, M. and Wishart, D.S. (2013) Translational Bi-
    omarker Discovery in Clinical Metabolomics: An Introductory Tutorial, in
    “Metabolomics”, 9 (2), pp. 280-299.
