10.6092/issn.2038-3460/17173
Significant challenges exist around the translation of the enormous amounts of data generated from large-scale gene and genome sequencing that has been facilitated by the Human Genome Project into tangible medical. Widespread acceptance exists within the biomedical research community about the role that bioinformatics will play in that translational process. While the goal of moving research from “the bench” into socially beneficially applications “at the patient’s bedside” has long driven science and technology policy, the picture is now more likely to resemble interactions between very powerful computers, and lab benches. Given the importance of bioinformatics, work presented here reports on a case study of a large Canadian scientific network that has developed a bioinformatics tool designed to facilitate investigations into gene-gene and gene-protein interactions and pathogenomics pathways. By focusing on this kind of bioinformatics system that facilitates a project’s own internal biomedical research and simultaneously serves as a free and open resource for a wider group of academic nonpeers, we advocated for a broadening of what translational science and medicine can and should entail. Furthermore, by highlighting the importance of movements between developers and a host of prospective users (and back again) we show how translational bioinformatics systems can be more effectively advanced.
application/pdf
2038-3460
17173
1
en
Conor M.W. Douglas
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
systems biology
The Role of Bioinformatics in Facilitating Translational Science and Medicine
Text.Serial.Journal
https://tecnoscienza.unibo.it/
5
Essays
2014-07-14
2023-06-12
2014-07-14
2023-07-24
141-163
TS
Conor M.W. Douglas
2014/07/14
10.6092/issn.2038-3460/17173
141
University of British Columbia, Vancouver
2038-3460
1
systems biology
en
163
The Role of Bioinformatics in Facilitating Translational Science and Medicine
https://tecnoscienza.unibo.it/article/download/17173/16074
5
                                                                             Essay

The Role of Bioinformatics in
Facilitating Translational Science
and Medicine
!
Conor M.W. Douglas
University of British Columbia, Vancouver
!
    Abstract: Significant challenges exist around the translation of the enor-
    mous amounts of data generated from large-scale gene and genome sequenc-
    ing that has been facilitated by the Human Genome Project into tangible
    medical. Widespread acceptance exists within the biomedical research com-
    munity about the role that bioinformatics will play in that translational pro-
    cess. While the goal of moving research from “the bench” into socially bene-
    ficially applications “at the patient’s bedside” has long driven science and
    technology policy, the picture is now more likely to resemble interactions
    between very powerful computers, and lab benches. Given the importance of
    bioinformatics, work presented here reports on a case study of a large Ca-
    nadian scientific network that has developed a bioinformatics tool designed
    to facilitate investigations into gene-gene and gene-protein interactions and
    pathogenomics pathways. By focusing on this kind of bioinformatics system
    that facilitates a project’s own internal biomedical research and simultane-
    ously serves as a free and open resource for a wider group of academic non-
    peers, we advocated for a broadening of what translational science and med-
    icine can and should entail. Furthermore, by highlighting the importance of
    movements between developers and a host of prospective users (and back
    again) we show how translational bioinformatics systems can be more effec-
    tively advanced.

    Key words: bioinformatics; translational science; translational medicine; us-
    er-configuration; infovis; systems biology.

    Corresponding author: Conor M.W. Douglas, 2405 Wesbrook Mall, Fac-
    ulty of Pharmaceutical Sciences, University of British Columbia, Vancouver,
    Canada – Email: conor.douglas@ubc.ca




!
TECNOSCIENZA
Italian Journal of Science & Technology Studies
5 (1) pp. 141-163 - ISSN 2038-3460
www.tecnoscienza.net                              2014
142 X                                                       Tecnoscienza - 5 (1)

1. Introduction

     Biomedical research and development (R&D) is undergoing major
transformations as it attempts to achieve translational goals of moving re-
search into the clinic, and deliver on earlier health-related promises is-
sued alongside the Human Genome Project (HGP). One of the compo-
nents of that transformation has been the development of bioinformatics
systems and tools necessary to make sense of enormous amounts of data
generated from large-scale gene and genome sequencing that has been fa-
cilitated by the HGP and the subsequent (next generation) sequencing
activities. There is widespread acceptance within biomedicine that the
development of medical interventions derived from that data will only be
possible with such bioinformatics systems and tools (Zerhouni 2005;
Yang et al. 2008; Ostrowski and Wyrwicz 2009; Szalma et al. 2010),
which has even culminated in an emergent subfield in-and-of itself: trans-
lational bioinformatics (Butte 2008; Altman 2012). While biomedical
R&D may have once been understood as processes that involve move-
ments between the lab bench and the clinical bed, the picture is now
more likely to resemble complex interactions between very powerful
computers, lab benches, and maybe some place down the road a clinical
bed. That said, bioinformatics systems and tools on their own are not suf-
ficient to facilitate developments in biomedicine. In the interest of under-
standing the role that bioinformatics systems play in the process of trans-
lation, social science research has been conducted on a database and suite
of analytical tools called InnateDB (Lynn et al. 2008; Breuer et al. 2012),
which has been developed for the systems-level analysis of the innate im-
mune system as a part of the Pathogenomics of Innate Immunity project
(PI2)1. This bioinformatics case study was a component of a broader so-
cial science endeavor located within the PI2 project that asked which cul-
tural and socio-technical factors constrained and/or enabled the transla-
tion of pathogenomics research into medical applications. The argument
forwarded here is that bioinformatics systems and tools must be designed
with keeping the larger (biological) research community in mind so that
biomedical advances can be made more broadly. On top of the need for
this particular design mindset, it is argued here that particular design
processes and features can also facilitate the development of bioinformat-
ics systems and tools that can be of tangible and far-reaching use in trans-
lational science and medicine.
     Work in Science and Technology Studies (STS) and beyond has ex-
plored the history of bioinformatics (Suárez-Díaz 2010) as well as defini-
tional issues important to understanding these novel systems (Leonelli
2010). Still other work has outlined some of the socio-culture aspects af-

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1
  Pathogenomics of Innate Immunity (PI2) project website, About the project,
http://www.pathogenomics.ca/, accessed 17 December 2009.
Douglas                                                                143
fecting the usability of bioinformatics systems (Douglas et al. 2011), and
the corporeal implications for data that bioinformatics facilitates (Mac-
kenzie 2003). Despite this recent interest, the production processes of bi-
oinformatics systems – such as it is taken up here- has received relatively
little attention from a social science perspective.
     To position our case study some of the literature and current models
of translational science and medicine are first overviewed, along with the
acknowledgment of the importance of users in the translation of success-
ful innovation. After detailing the methods through which we have col-
lected and analysed our social science data on this bioinformatics system,
we will then outline the functions of InnateDB and the Pathogenomics of
Innate Immunity project (PI2) project in more detail. In the body of the
text we use the classical sociological concept of verstehen to describe the
particular mindset that bioinformaticians within the PI2 project adopted
when designing a system for translational biomedical work. Further, we
will show how specific design processes such as limited release strategy
and a particular peer-review system facilitated the development of this
translational bioinformatics tool. We will also describe the particular in-
formation visualization design features that were integrated into Innat-
eDB so that it would be of use to researchers beyond those with computa-
tional backgrounds.
     It is our position that resources and systems that are being designed
both for internal project-specific use and as platforms that the broader
biomedical community of academic non-peers can use for biotechnologi-
cal development might be conceptualized as form of translational science
(TS) that is distinct from other forms of commercial and/or clinical TS.
While the iterative movements between bedside and bench (and back
again) can be shown in cases of clinical translation, which are mirrored by
bench-to-bedside (and back again) movements in the technology transfer
and cases of commercial translation, this case of the development of bio-
informatics tools suggests that TS needs to be more broadly understood.
By including activities that involve movements between developers of re-
search and analysis resources and a host of prospective users (and back
again) we not only account for diverse forms of TS, but in doing so we al-
so contribute to the larger goal of translating the masses of genomic data
into usable information for health improvements.


2. Translational Science/Medicine and the Role of Users in
Innovation

    There has long been policy pressure to translate investments in a
variety of research into socially beneficial applications (Bush 1945), and
more recent demands for medical genetics research activities to deliver
health benefits is no exception. The novel journal Translational Medicine
144 X                                                               Tecnoscienza - 5 (1)

– published by American Association for the Advancement of Science
(AAAS), who produces Science among other journals – outlines the need
for a specific sub-field to facilitate this process:

              A profound transition is required for the science of transla-
          tional medicine. Despite 50 years of advances in our fundamental
          understanding of human biology and the emergence of powerful
          new technologies, the rapid transformation of this knowledge into
          effective health measures continues to elude biomedical scientists.
          This paradox illustrates the daunting complexity of the challenges
          faced by translational researchers as they apply the basic discover-
          ies and experimental approaches of modern science to the allevia-
          tion of human disease. Studies in humans often highlight deep
          gaps in our fundamental understanding of biology, but the linkag-
          es back to basic research to fill these gaps have not been as effec-
          tive as they could be. Clearly, creative experimental approaches,
          novel technologies and new ways of conducting scientific explora-
          tions at the interface of established and emerging disciplines are
          now required to an unprecedented degree if real progress is to be
          made. Nothing short of a true reinvention of the science of transla-
          tional medicine is likely to suffice.
                          (Science Translational Medicine Mission Statement)2

    Alongside academic journals, models of translational medicine have
also been developed to try and steer translational work. For instance,
common models describe the movement of biomedical research into di-
agnosis or treatment (i.e. phase 1 translation, or T1), which then moves to
subsequent development into evidence-based protocols (T2) (Kerner
2006, 73), and their deployment into clinical practice (T3) (Westfall,
Mold and Fagnan 2007), and ultimately the verification and evaluation
for ‘real world’ impacts on health (T4) (Khoury et al. 2007). Specific areas
of research (e.g. autoimmunity) have adapted their work and concurrent
challenges to such models for translational medical research (Blumberg et
al. 2012).
    Work in the area of technology transfer and cooperative research cen-
ters (CRCs) suggests that advances in medicine need not be restricted to
the kind clinical translation described above. A considerable amount of
scholarship exists in the area of management sciences and science policy
that have sought to facilitate the flow of knowledge and technology be-
tween universities and industry (Bozeman 2000). In this way we can come
to think of commercial translation in medicine when health technologies
or research on medicinal products are transferred to private companies or
spun-off into their own market venture.
    While it may be the case that these areas of scholarship have some
traction with forms of clinical and commercial translation, they are argu-

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
2
    http://stm.sciencemag.org/site/about/mission.xhtml, accessed March 20, 2014.
Douglas                                                                 145
ably less well equipped to handle dynamics related to the production of
open access bioinformatics research and analysis infrastructures that are
being discussed here. What the case presented here shows is that a par-
ticular design mindset and specific design processes and design features
stand to play a significant role in the production of bioinformatics sys-
tems that are critical for the translation of gene and protein data into ac-
tionable medical information. As the body of the text shows, what these
design mindset, processes, and features share is their attention to –if not
direction integration of- system users in the development and production
process. To be sure our work is not the first to acknowledge that a reli-
ance on users is beneficial for the innovation processes with considerable
attention being given to “user-driven research” (De More et al. 2010),
customer-active innovation (von Hippel 1978), or “user-producer interac-
tions” (Laursen 2011). Our case marks a slight departure from this per-
spective and instead suggests a bi-directional flow of innovation between
users and creators of technology. Attention to such dynamics has been
made in innovation studies (von Hippel 2005), science and technology
studies (Oudshoorn and Pinch 2003), and e-commerce and computer
programming (Klein and Totz 2004); however, it has yet to be applied in
the area of translational science and medicine as is the case here.


3. Methods

    The examination of InnateDB was a part of a broader social science
project that sought to understand the social, political, economic, cultural,
and technological factors that constrain and enable translational biomedi-
cal science. As such our team was an integrated component of the PI2
network from 2006 through 2009, and we conducted three translation
cases studies within the PI2 network related to the clinical translation in
the university hospital (Lander and Atkinson-Grosjean 2011), commercial
translation associated with a pharmaceutical spin-off company, and the
bioinformatics case presented here. While these three cases do not form
an exhaustive list of translational pathways, we selected them because of
their respective success in the translational process, their heterogeneity,
and because of their connections with the PI2 network.
    Given our integration within the PI2 network we knew that the bioin-
formatics database (InnateDB) and suite of analytical visualizations tools
(Cerebral) would play a central role in the development and success of
the PI2 project. Not only was clear that InnateDB and Cerebral were crit-
ical to the PI2 project, but as it is described in more detail below, these
systems and tools were also being developed as a platform technology for
those within and outside of the PI2 project to build knowledge, facilitate
future discoveries, and assist in the early development of future medical
prophylactics and/or therapeutics. Given our research goal of describing
146 X                                                        Tecnoscienza - 5 (1)

the constraining and enabling factors in translational science, and in light
of our recognition of the role that the bioinformatics system and tools
were playing in the translational process of PI2 and beyond, we chose to
conduct in-depth social science research on the production, maintenance,
and use of InnateDB and its associated tools. As a result, from July 2007
to December 2007 we conducted ethnographic participant research and
qualitative semi-structured interviews with one part of the bioinformatics
collaboration responsible for the design and construction of InnateDB,
and in November and December of 2008 we conducted a series of follow-
up interviews across the two institutions involved in InnateDB (total
n=25). Our interviews included the heads of the bioinformatics lab, the
leaders of the PI2 network, the bioinformaticians designing the front-end
and logic of the system, the computer scientist writing the programming
code, and the curators who were manually inputting and managing the
data submitted to the system. Given the relatively small number of re-
searchers involved with InnateDB we choose to interview practically eve-
rybody who was significantly involved in the design, production, and
maintenance of the system. Our integration within the PI2 network
meant we were able to contact and arrange interviews directly with partic-
ipants who were ready and willing to contribute to the social science
component of the project.
    Interviews were audio recorded, and transcribed by members of the
research team and private transcriptionists. Interviews were then analyzed
using a grounded theory approach to guide our exploration of the mate-
rial (Charmaz 2006). This approach does not assume a theoretical posi-
tion a priori to analysis, but instead allows a theory to grow out of the da-
ta in a developmental movement from code to concept to category to the-
ory. In our case this was accomplished by the team constructing a coding
matrix containing terms that highlighted important aspects related to the
social, political, economic, cultural, and technological factors that con-
strained and enabled translational biomedical science. Codes were then
attached to segments of interviews using qualitative software ATLAS.ti.
Some codes were applied across the three cases (e.g. ‘role of teaching and
learning’, ‘impact of disciplinary background’, or ‘patents and intellectual
property’), and some specific to the bioinformatics case (e.g. ‘limited re-
lease strategy’, ‘manual database curation’, or ‘problems with database
maintenance’). To improve the reliability in applying the coding matrix
between team members, several interviews were coded by multiple mem-
bers. Variations in coding application were discussed and consistent defi-
nitions agreed upon. A lead researcher for each case study then coded all
remaining transcripts. The software was then used to produce reports on
specific codes, which is similar to the ‘concept’ development phase within
the grounded theory method. These reports were then examined for the
most salient factors involved in the diverse forms of translation within the
PI2 network, which were worked into concepts. It is here that we identi-
fied the importance of users, and consequently developed categories that
Douglas                                                                         147
described the difference facets through which users were included in the
translational process. These categories consisted of the importance of the
end-user in the design process, the integration of users in developmental
processes, and the creation of a system that includes features to enhance
the user experience and enlarge the user-community. These categories
have formed the core sections in the body of the text presented here.
Within qualitative methodologies interview excerpts have been used to il-
lustrate the above mentioned categories. In doing so the code reports that
were used to develop our concepts were re-examined, and the most clear
and succinct interview responses have been used as quotations in the
body of the text to illustrate the specific category.
    The final step in the grounded theory approach is to use identified
categories, and the associated quotations, as the basis for a theory of the
phenomenon in question. In our case that theoretical supposition is that if
bioinformatics systems are going be of use to those beyond the develop-
ment team for the translation data into useable health information, then
they need to be constructed with a particular a mindset (i.e. verstehen)
that take users into account, and they need to integrate users in the design
process (i.e. through the peer review and a limited release strategy), and
design the system with tools that facilitate systems-level analysis for those
without a computational background.


4. InnateDB Case Study and the PI2 Project

    The PI2 project/network was funded largely by Genome Canada to im-
prove the systems-level understanding of the innate immune system. The
human immune system has two general components: the adaptive immune
system that response defensively against microbial infection and is stimulat-
ed by medical interventions like vaccination, and the innate immune system
which acts as the first line defense against all foreign pathogens. According
to the project’s webpage, innate immunity can be understood as a:

              …part of our natural biological makeup – [and because of it]
          we are able to withstand a daily onslaught of tens of thousands of
          potentially pathogenic microbes in air, food and water, and in
          our interactions with other people and animals. But our innate
          immunity can sometimes get over-stimulated, leading to inflam-
          mation of tissue and even sepsis – a deadly infection of blood or
          tissue. Understanding the balance between infection resolution
          and inflammation is the goal of the new Pathogenomics of Innate
          Immunity Genome Canada Competition III project.
                                                                 (PI2 2006) 3

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
3
  Pathogenomics of Innate Immunity (PI2) project website (2006) About the
project, http://www.pathogenomics.ca/, accessed 17 December 2009.
148 X                                                                  Tecnoscienza - 5 (1)


    If the goal of the PI2 project is to understand “the balance between
infection resolution and inflammation”, then InnateDB’s role in that pro-
ject was to create a roadmap of the immune system. The metaphor of the
roadmap is apt for non-scientific writers and audiences to deploy when
trying to make sense of InnateDB, and was also a guiding metaphor for
members of the InnateDB development team. It is worthwhile for one of
bioinformaticians to explain themselves how this metaphor of the
roadmap can facilitate an understanding of InnateDB:

            We just kind of want to make a roadmap to the immune sys-
        tem that, you know, when people… If you just look at a list - say
        you go to an atlas and you look at the index. Oh, very exciting; it's
        just a list of places. You can't really picture that. But then when
        you open things, when you open your atlas to a map page, you say,
        “Oh, this city is connected to this city by this road. Oh, these cities
        are in the same country. Oh, these cities are in a different coun-
        try”. And it's just like that. In the past, people have been analyzing
        their array data by just looking at a list. And they've never really
        put that list into biological context. So we are giving them a map.
        And we are giving them a map that's laid out well... But once you
        lay things out in their proper context - this goes here, this goes
        there, this goes there, this is in this part of the cell, this is in this
        part cell- then it makes it so much clearer, and people can start to
        follow relationships and trace pathways [and think]: “Oh, this re-
        ceptor up here is being activated. And all these genes down here
        are getting turned on. Maybe that receptor is linked to this set of
        genes somehow”.

    While the broader PI2 project had numerous goals, one of the distinct
objectives was to identify the key molecules involved in infectious disease
response, which might ultimately give rise to new prophylactics or treat-
ments. According to one of the InnateDB Project Leaders, “if you can
target those key central molecules, perhaps you can predict therapeutic
effects on the outcome of disease or the outcome of information”. As a
result when genes are identified to have an association to disease response
InnateDB can be used to model the pathways and networks of those
genes and proteins across different datasets. If the concurrent systems-
level analysis does identify mechanisms within the pathway, then lab biol-
ogists will conduct wet experiments for the confirmation or dismissal of
the mechanisms within the identified pathway.
    Importantly, InnateDB also boasts supplementary interactions that
innate immunity genes participate in, and because it has been created as
resource to include all human and mouse pathways of interactions at sys-
tems biology level its relevance is not limited to innate immunity. Further,
unlike bioinformatics resources that contain large amounts of annotated
data, InnateDB comes equipped with a suite of tools through which re-
Douglas                                                                           149
searchers can conduct analysis directly in the InnateDB website. What is
more InnateDB is an open source and open access database and analysis
environment. While there is a tradition (or even convention) in computer
science (and perhaps even in bioinformatics) to design databases to be
open access and open sources, the bioinformatics Primary Investigator
describes the importance of open source and open access characteristics
in some length:

               Yeah, so for open source it’s important that you realize that
          open source doesn’t mean “free”, you know, so it just means that
          when you make software you can actually… the way you make
          software you write a program, and you can release that program to
          somebody and then they can run that program. Or you can pack-
          age it up into an executable - a wxe file - and so that it’s actually
          just in this binary code that you can actually see what the original
          program was, and you can release that…The open source model is
          where you just keep that package open so…you still have the abil-
          ity to see that code, see that program, see how it works, know ex-
          actly how it works so you can either modify it for your own uses,
          or…redistribute it as some other version, or you just might want to
          see how it works to understand why it’s doing… and so there’s
          definitely been a growing movement of people that really want
          that, because they’re frustrated with the sort of closed black box
          kind of software and for example in our pathogenomics project we
          found it very useful because there was definitely some microarrays
          software that was black box like that…

    While the open source characteristic of InnateDB refers to the process
of keeping lids lifted on black boxes so that users can see the computa-
tional processes that have gone into making the database function the way
that it does, the open access refers to a similar characteristic of transpar-
ency. Within the open access model all users are provided free right of
entry into the database, and the data contained within the database is free
to access, download, and use for one’s own research purposes.
    Part of the data contained within InnateDB is itself an amalgamation
of three or four different types of data that come from four or five differ-
ent categories of open access data. This data that is present within the da-
tabase is mostly “gene, proteins, and interactions and signalling responses
involved in the mammalian innate immune response”4. These different
kinds of data are collected from gene lists, external interaction databases,
and external pathway databases, which are all integrated within Innat-
eDB, and all open access. There are also links to external databases con-
taining immunology-relevant data, but it is not clear if this information is
integrated within InnateDB. As new data is compiled in these external

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
4
 InnateDB website Home page, www.innatedb.ca/index.jsp, accessed 1 March
2014.
150 X                                                         Tecnoscienza - 5 (1)

databases it is regularly uploaded into InnateDB by website administra-
tors. Keeping abreast of novel pathway and interaction data can be
achieved in part by those with computer science backgrounds as they
amalgamate existing databases into InnateDB; however, one of the dis-
tinct characteristics of this tool for translational biomedicine is that it is
also manually curated - a point which we will return to as a key design
feature of the system.
    Alongside the massive amounts of curated and standardized interac-
tion data that is accessible through InnateDB there is also a multitude of
search mechanism available to mine the data. A researcher can make use
of the search functions included in the suite of tools provided by Innat-
eDB to investigate genes and proteins of interest, or view statistics for
manually-curated molecular interactions that are relevant to innate im-
munity and submitted weekly by curators. Further searches can be con-
ducted for “experimentally-verified molecular interactions by
gene/protein name, interaction type, cell type, etc.” as well as searches for
147,240+ interactions & 4,400+ pathways5.
    Not only can a researcher mine the gene, protein, and interaction data
that is provided through InnateDB, but because it has been concurrently
constructed as a suite of tools researchers can also upload their own data
and conduct particular kinds of analysis immediately on the InnateDB
website. Gene expression data can be uploaded by anyone, and then
through the use of a piece of software called Cerebral researchers are
“able to interactively visualize interaction networks with expression data
overlaid; carry out Pathway, Gene Ontology and Transcription Factor
Binding Site over-representation analysis, construct orthologous interac-
tion networks in other species and much more”6. Not only are these tools
provided as an integral part of InnateDB, but video tutorials also exist on
the website so to help users familiarize themselves with how these tools
can be most effectively used. In light of the central role that is played by
Cerebral in the InnateDB analysis environment, it will receive more atten-
tion later when we more directly describe the particular design features of
the system that can facilitate the translation of data into valuable biomed-
ical information.



5. Designing Bioinformatics Systems for Translational
Science and Medicine


!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
5
  InnateDB website Home page, www.innatedb.ca/index.jsp, accessed 1 March
2014.
6
  InnateDB website Home page, www.innatedb.ca/index.jsp, accessed 1 March
2014.
Douglas                                                                            151
5.1. Particular Design Mindset

    In order for InnateDB to be usable tool for translational science to
both those inside the PI2 network and to those outside of it who may or
may not have a computational background, the researchers building the
system had to adopt a particular design mindset. Classical German soci-
ologist Max Weber used the word “verstehen” to describe the process
through which the social researcher would develop an interpretive under-
standing of meaning and human activity (Ritzer 2007). By approaching a
human’s actions from their point of view Weber hoped to gain an appre-
ciation of the way in which they constructed and gave meaning to their
own world. In doing so the social actor is not seen as the mere object of
investigation, but rather as a subject. Here we can adapt this concept to
the social study of science and technology to explore the case of Innat-
eDB and the particular mindset the bioinformatics system designers de-
ployed in making a tool that would be broadly usable for translational ac-
tivities. The system architects -whom were largely computer programmers
coming from a computer science background- needed to develop a level
of interpretive understanding (or verstehen) of diverse prospective users
of InnateDB, so that it could be appropriately configured to their needs.
While the system architects and designers had a general understanding of
what members of the PI2 team would be using the system for, they had to
develop a more nuanced understanding of systems biology so that Innat-
eDB would be equipped for the kinds of translational work that such re-
searchers would be undertaking. One of the developers described this
learning process in these words:

               I didn’t anticipate that people would be uploading entire
          GeneChips of data. I thought it would probably be 100 or 200
          queries at a time. And so I sort of…the way it was designed, it was
          sort of a design in the manner to handle these many pieces of data.
          But when you get into, sometimes…like 25,000 or 30,000 genes
          being uploaded is quite a load on the server. And it sort of brought
          it to a crawl at first until we said: “Okay. I’ve got to step back and
          rewrite this”. So, it was a few extra months, but it definitely paid
          off.

    InnateDB also boasts a team of curators that manually keep gene,
pathway, and interaction data current. Their training in biologically-
relevant disciplines means that they can sift through individual pieces of
published data – as opposed to already curated data that is found in the
other interaction and pathway databases. They are then able to make de-
cisions with regards to the accuracy and relevance of that data to Innat-
eDB, and submit it to the system. Without this curation the database be-
comes a rather static entity, and its practical value concurrently decreases
to the PI2 project team as well as those interested in innate immunity and
152 X                                                                Tecnoscienza - 5 (1)

systems biology more broadly. Curating originally began as an examina-
tion of data concerning single genes, and if the quality of that data could
be confirmed then it would be uploaded into InnateDB for subsequent
use. However as InnateDB grew, curating extended towards the examina-
tion of specific pathways with curators themselves playing an increasing
role in deciphering the balance between infection resolution and inflam-
mation. While these curators are not the analytical bioinformaticians who
conduct the systems-level analyses that identify mechanisms within the
pathway, nor are they the lab biologist that produce experimental con-
firmation or refutation of the mechanisms within the pathway, they are
key players in the pathway identification process. Having a level of inter-
pretive understanding of how systems-level bioinformaticians go about
assembling these pathways greatly facilitates the work of the curators by
sensitizing them to the kind of data that they should be on the look-out
for. In turn, systems-level bioinformaticians increasingly grow to trust the
data within InnateDB when they know its character, quality, and stand-
ards, which then facilitates their analytical work. One of the curators ex-
plains this dynamic when asked about the potential for training to in-
crease her analytical role in the project:

             But from a bioinformatics point of view, to understand how it
        kind of is related to this database, like that’s the whole point right,
        is to analyze data basically. So from me, I think it would be more
        interesting to kind of learn the aspects of that [analysis], but our
        job description is to look for particular protein-protein, or protein-
        gene interactions. So you don’t necessarily need [added under-
        standing of the analytical processes], it’s just kind of an added
        thing that might actually increase the analysis, or maybe things that
        you kind of pick-up on that other people may need later on. Be-
        cause I think [the project leader] also kind of looks at it with the
        perspective of: “How he would analyze his data”, but when it
        comes to curating, I ask for certain things that maybe weren’t on
        the website, but might help us later on to do the pathway curation.
        But over all, it’s supposed to help out data analysis.

    By demonstrating a level of end-user verstehen, manual curators also
affect the development of the data that goes into InnateDB, which influ-
ences the analytical applications that the data is used for. Seeing the rela-
tionship between curators and system-level analysts in this way conforms
to the colloquialism of ‘garbage-in-garbage-out’ which is well worn within
database and bioinformatics cultures. The PI2 analysis emergent from
InnateDB will only be as good as the interaction and pathway data that is
boxed-up inside of it, so it is clear why high quality curated data is central
to the project. Part of the process of obtaining high quality curated data is
to equip curators with a bigger picture of what the data would be used
for in bioinformatics terms. Therefore it seems useful -if not necessary-
for each member of the project team to have an appreciation of what oth-
Douglas                                                                    153
er team members are doing and what their job entails so that they can do
their own job better. For analysts to do their job well they need to know
that they have good interaction or pathway data to conduct their analysis
with, and for curators to do their job well it is good to know the larger
analytical picture (i.e. the purpose of the databases is about, what it is
meant for and what it is meant to accomplish) so that they can input the
right kind of data with the right kind of annotations. This suggests that
understanding the roles and goals of other project team members is high-
ly relevant to the success of multidisciplinary research, and ultimately to
the achievement of translational goals. While we have seen here how a
particular design mindset that takes into account the prospective user of
the technology is critical in the construction of a useful translational tool,
the following section explores how particular design processes are simi-
larly important in achieving this goal.


5.2. Particular Design Processes

    InnateDB was first released for public use in May of 2008. However,
before it could effectively “go live” a number of design processes were
undertaken that included a limited release strategy and a rigorous peer
review, which helped it to become a useful tool to the PI2 network and
beyond. In his work Democratizing Innovation, Eric von Hippel shows
that “much of the information needed by product and service designers is
‘sticky’” (von Hippel 2005, 67). Different users have diverse needs and
capabilities that require inscription into a system so that its’ utility can be
maximized. As a result, unsticking those needs and capabilities and get-
ting them to the designers is of paramount importance in the develop-
ment of useful technologies such as bioinformatics systems. One of the
ways through which this was accomplished with InnateDB was by releas-
ing drafts of the system prior to its public release to select colleagues in
the innate immunity community and to the PI2 project team. The role of
the limited release strategy should not be underestimated, as prospective
users of a technology are proving to play an increasingly central role in
up-stream innovation processes. With a working version of the database
in place, and with some data now loaded in, PI2 team members from oth-
er components of the project were invited to access the system and exper-
iment with its uses while the database was still in its developmental stages.
Incorporating project team members outside of the database develop-
ment team at this stage was important for a number of reasons. First, the
development of any system is bound to have bugs, and identifying prob-
lems with the operations of InnateDB would be crucial before it was to
be released to the public. More importantly PI2 project team members
were brought into the development process so that their needs could be
readily identified and configured into the design of the technology. While
the developers of InnateDB would certainly consider themselves bioin-
154 X                                                                Tecnoscienza - 5 (1)

formaticians their familiarity with biological sciences varied. As a result
experimental biologists were consulted to provide feedback on the sys-
tem. One of the InnateDB’s designers explained the content and function
of that feedback:

            [Biologists provided feedback on] all sorts of levels to, you
        know, to broadly… kinds of things you want to do, you know,
        feedback to the extent of what are the biological questions that
        they want to be able to use the system to use, down to pretty nit-
        ty-gritty questions of, you know, in our visualization system, you
        know: “Do you want to see broad spectrums of colours or do
        you just want to keep it pretty simple? Yes/No, kind of colours?
        [up-down] kind of thing?” Yeah, so from quite a broad spec-
        trum of very nitty-gritty stuff to big picture types of big questions
        they want answered.

    Two bioinformaticians who conducted system-level research and who
were familiar with the challenges of databases and tools were also a part
of the process through which InnateDB would be improved upon. Both
of these bioinformaticians were members of the PI2 project, and involved
with the construction of InnateDB, but were not the developers respon-
sible for the schema, submission system, or search mechanisms. As one of
these key figures point out:

            The other thing that I think was kind of critical is that, alt-
        hough I’ve worked in bioinformatics for about 10 years now, my
        background and interest is on the biological sciences side of
        things. I think having someone with that background making the
        key decisions on the direction of the thing was very beneficial to
        ensuring that it was relevant to a biologist, and a lot of these things
        are developed by people with computer science backgrounds who,
        you know, can come up with great algorithms or whatever but
        don’t have the same insight into how a biologist wants to see
        things.

    As the above excerpt demonstrates, these two bioinformaticians were
of a particular ilk which made them crucial to the development of Innat-
eDB. Not only did these two actors have more familiarity with the com-
puter science end of databases, which allowed them to engage with the
developers on a deep level that the average experimental biologist was
unable to do, but more importantly they were prime examples of the
kinds systems-level end-users of InnateDB. Problem areas of InnateDB
were identified through the early deployment of the suite of tools for
high-level analysis and future improvements were also prospected.
    Another important aspect of the design process that has facilitated the
construction of a translational bioinformatics system was the peer-review
process, and subsequent publication of both the article that describes In-
nateDB (i.e. Lynn et al. 2008) and the actual database system itself. While
Douglas                                                                             155
it may seem obvious that a journal would access the functionality of a sys-
tem like InnateDB in the peer-review process, this strategy seems to rep-
resent a departure from traditional articles in bioinformatics. One of the
members of the PI2 network described it this way:

               I mean bioinformatics when it started, and bioinformatics real-
          ly only started to become big ten to fifteen years ago. And for the
          longest time, it was such a specialized field that people were doing
          it for sort of discovery sake, and not really making tools that were
          ideally suited to an end-user. Even when I was doing my PhD
          [2001-2005], I’d say half of the papers I read that reported sort of
          a relevant method to what I was doing were just algorithm papers.
          There was no software; there was no website to go along with it. It
          was just telling you the method, “We did this, and he’s our paper
          with some math showing how we did it, but you can’t actually do
          this unless you create this entire system and do this entire training
          dataset”. So that was the prevailing mindset in bioinformatics, and
          I think that was probably, I don’t know maybe it was sort of a cul-
          tural thing. The scientists that first got there, they were these spe-
          cialized scientists. They didn’t really care; they were just doing this
          for discovery sake. But then the people that have gotten into bioin-
          formatics more recently, people of sort of my generation, or a cou-
          ple of years older and sometimes younger realized the importance
          of the user community. Because we had to do our Master’s and our
          PhD’s seeing these methods that looked really interesting, and not
          being able to use them anywhere. So I think to our generation of
          bioinformaticians the notion of open source is a big thing. Making
          your work available to people. And people realize that your tool
          can be open source, and available to the world, but if it’s not de-
          signed well people aren’t going to use it.

    In light of transformations within bioinformatics to publish functional
tools rather than a theoretical algorithms and methods, InnateDB had to
be up and running before the review process could get under way. In this
respect the publication process that the InnateDB paper had to navigate –
before it could be deployed to grow its user community – had to pass a
kind of usability test in the form of peer review. While peer reviewers
may not have embodied the traditional notion of ‘user’ that is conjured
up in one’s mind when technologies are discussed, and nor does the pub-
lication process meet conventional understandings of technological ‘use’,
both would prove to be an essential hurdle that the team had to overcome
in their attempt to manifest InnateDB’s translational potential:

              Writing any paper takes a while to complete, a big paper like
          this. The reviewer comments were probably the most positive
          comments I’ve got on anything I’ve ever been involved in before.
          They were hugely positive comments and the suggestions that they
          wanted to do were very relevant and things that we would have
          wanted to do and that we just, we did it for them. For example, we
156 X                                                              Tecnoscienza - 5 (1)

        used to allow users to upload just four gene expression datasets at
        one time and they wanted to increase the capacity to be able to do
        more datasets in one go, so we increased it so that you can now do
        up to 10 different conditions at any one time. We had some other
        limits to do with computational power in terms of the number of
        interactions you could return in any one search, and they felt that
        if we could find a way around that it would be better not to have
        any limits. Our original thinking was that, you know, we had pretty
        generous limits - like we’re talking you could return up to 10,000
        data points kind of - and if you really wanted to do any more than
        that you were probably at a fairly advanced bioinformatics level
        and you can just download the entire database including all the da-
        ta and then analyse it, but they would have preferred that the limits
        be removed. So we came up with a computational approach to
        mean that we could do that. And so now we don’t have limits in
        our searches now, you can return all the data in any search.

    What this section shows is that there is a clear link between the publi-
cation process, the role of users in the design and development stage of
bioinformatics tools, and potentialities of translation.


5.3. Particular Design Features

    For InnateDB to be a useful tool for making sense of vast amounts of
sequence data it also had to include a suite of tools to aid researchers
conducting analyses into problems of systems biology. One of the analyti-
cal tools that can be found within InnateDB is called Cerebral. This tool
was created as a Postdoc project by researcher within the PI2 network to
facilitate the research into innate immunity by the team, but also to act as a
tool for the wider biological community in general. As she explains herself:

             Sure, well I was always sort of peripheral to the InnateDB pro-
        ject, I was brought on to work on Cerebral, which is a spin-off, you
        know it’s a component of InnateDB, but in and of itself it’s, its
        own project. And so I really, you know when I was doing the Cer-
        ebral work; I tried to develop it for the larger community.
             [Researcher]: Which larger community, sorry?
             Biology in general, anybody interested in visualizing networks
        in a pathway like fashion. So you know, it’s all basically, its crea-
        tion was inspired by InnateDB, and sort of went along with Innat-
        eDB, but I always kept my eye towards a larger audience when de-
        veloping it. So, I was always sort of on the periphery. I’d be in-
        cluded in some of the InnateDB meetings, and things, just to pro-
        vide guidance as one of the ultimate users of the database.

   Cerebral – or CEll REgion-Based Rendering And Layout – is a tool
that allows analysts to visualize biological information in traditional sig-
Douglas                                                                           157
naling pathway/system diagrams. It is not a stand-alone tool, but rather a
plug-in for one of the most widely used bioinformatics tools called Cyto-
scape, which: “is an open source bioinformatics software platform for
visualizing molecular interaction networks and biological pathways and
integrating these networks with annotations, gene expression profiles and
other state data” (Cytoscape 2012). As a plug-in, Cerebral brings many
features useful for pathway and interaction analysis that Cytoscape lacks,
and seeks to supplement -rather than supplant- the existing visualization
tool: “Cerebral is a plug-in that enhances Cytoscape's functionality by us-
ing extra annotation provided by the user to both automatically generate
a more pathway-like representation of a network and to provide an envi-
ronment for the visualization, comparison, and clustering of expression
data from multiple conditions” (Barsky et al. 2007; Cerebral 2012). While
Cerebral could have been developed as a standalone tool, Cytoscape has
created a certain degree of technological lock-in within the bioinformatics
user community that has been facilitated by its open source and open ac-
cess character. Releasing a tool outside of Cytoscape software platform
would undoubtedly reduce the numbers of users accessing Cerebral
thereby diminishing its capacity as a piece of translational science. One of
the developers of Cerebral explained it this way:

               “By piggybacking on a big endeavour like that, there’s two
          main advantages to the plug-in developer both of which are entire-
          ly selfish. 1) Is its way less work…if you’re looking at it [Cyto-
          scape] from an infovis perspective, you’re kind of like: “Oh, why
          did they decide to do this?”. And the rendering engine is goofy,
          and all that stuff. So initially [our collaborators] looked at Cyto-
          scape, and they’re like, “Oh gees this is a piece of crap, can we
          please just build our own version”. And I was pretty adamant that,
          “No we gotta do it in Cytoscape”, I mean there’s so many func-
          tions beyond the visualization that we would have to code into one
          of these bits of software that would take years, and years, and years
          to do something that even did a tenth of what Cytoscape does. So
          it’s saving you a pile of work by piggybacking on something, and 2)
          it’s also giving a huge user community too.

    One of the ways that Cerebral enhances Cytoscape’s functionality is
by integrating ideas and lessons from the emergent interdisciplinary fields
of information visualization and visual analytics. Information visualization
– or infovis – is “the use of computer supported, interactive, visual repre-
sentations of abstract data to amplify cognition” (Card et al. 1999). Data
can take both numerical and non-numerical form, such as genes and pro-
teins. Visualization can aide users from various disciplines to address a
variety systems-level problems in biology because “visual representations
and interaction techniques take advantage of the human eye’s broad
bandwidth pathway into the mind to allow users to see, explore, and un-
derstand large amounts of information at once. Information visualization
158 X                                                                Tecnoscienza - 5 (1)

focused on the creation of approaches for conveying abstract information
in intuitive ways” (Thomas and Cook 2005). Visual analytics (VA) on the
other hand is an outgrowth of infovis, which “combines automated analy-
sis techniques with interactive visualizations for an effective understand-
ing, reasoning and decision making on the basis of very large and com-
plex data sets” (Keim et al. 2008). Whereas infovis is concerned with
principles, ideas, and assumptions concerning how users see and use in-
formation, VA is more about the development of tools resultant from the-
se visualizations to facilitate analytical reasoning.
    In the case of InnateDB infovis principles were used in the develop-
ment stages of Cerebral and the tool can boast of both infovis and VA
characteristics in its most recent incarnation. When asked what role info-
vis would play in developing bioinformatics systems that are useful tools
in the translation process and resolving biological problems, one of the
developers of Cerebral responded this way:

             Infovis is going to be huge, huge, huge. And Cerebral and a
        few other sort of similar type tools are really the first ones to bring
        visualization to bioinformatics. I think Cerebral was probably the
        first one to bring principles from information visualization to bio-
        informatics. You know tools like Cytoscape were obviously around
        for a while that would create a visual representation of data so you
        could interact with it easily. But they didn’t really do any research
        into infovis principles and ideas when they built Cytoscape. But
        when we built Cerebral, we had our two infovis collaborators so
        they brought in all these things that we sort of never heard of be-
        fore and never considered in biology that just made Cerebral that
        much better. Because all this research into a how a user looks at
        screen or where do they look, what colours do they respond to,
        what shapes do they respond to all of that went into Cerebral, and
        it really was the first instance of that happening. But, I think visual
        analytics are going to be huge…So if you can make things as sim-
        ple and as universal as possible, then you’re well on your way for-
        ward to satisfying as many people and getting a huge user commu-
        nity as you possibly can. So I think as bioinformatics professionals
        recognize this, they’re going to be making their tools more usable
        by adopting visual methods.

    Through their integration of Cerebral into the construction of Innat-
eDB the project team was not simply coupling a suite of analytical tools
with a database; rather, they were creating a research resource that would
be as widely usable as possible. Further, it is important to note that this
was not required by their funders, and they were under no obligation to
create their own project tool this way; rather, it was an initiative they took
of their own volition. Not only would their choices of particular design
features allow them to tap into a larger research community associated
with Cytoscape, but by designing a visual analytics tool like Cerebral with
infovis principles the PI2 team were purposefully creating a research re-
Douglas                                                                       159
source that would extend beyond their own project and into the wider
biological research community without a hardcore computational back-
ground.


6. Conclusion

    We have argued here that a number of features of InnateDB have
functioned to make it a research and development resources both for in-
ternal use of the PI2 team and as platforms for the broader biomedical
community to engage in translational work. Specifically we have shown
how the development team took on a particular design mindset through-
out the construction process in which they constantly envisaged who their
diverse users might be, and how they might use the system. By deploying
a level of interpretive understanding – or verstehen – of their users the
InnateDB team was able to construct a tool more suitable to diverse user
needs. Furthermore, through an appreciation of how systems biologists
would use InnateDB the architects of database were able to make im-
portant alterations to the amount of gene data that could be uploaded by
users, and the curators were able to improve the data that they were in-
putting into the database so as to minimize the ‘garbage-in-garbage-out’
phenomenon. Both of these changes stand to have an impact on the abil-
ity of systems biologists to move their work along in the translational pro-
cess. We have also shown how particular design processes related to the
limited release strategy and peer review worked to not only debug the sys-
tem, but to construct a tool that was more useful for the kinds of system-
level analyses needed to advance translation in innate immunity and be-
yond. Finally, work here has made clear how design features related to in-
formation visualization and visual analytics make InnateDB a resource
and tool increasingly usable to those who may not have a computational
background. By designing a system that is more usable, the potential us-
ers of the system expand, and then so too does the potential to make
sense of data contained within the database.
    By creating resources and tools for the broader biological community
to use, activities like the construction of InnateDB could be considered a
particular form of translational science, or what we have referred to else-
where as ‘civic translational science’ (CTS) (Atkinson-Grosjean and
Douglas 2010; Lander and Atkinson-Grosjean 2011). The motivation be-
hind the labelling of CTS practices is not to construct a hard and fast def-
inition that will be true for a specific set of activities, but rather to call at-
tention to a broader set translational dynamics that exist beyond the clinic
or the market. Iterative movements between bedside and bench (and
back again) can characterize clinical TS (see Lander and Atkinson-
Grosjean 2011 for e.g. within PI2 network), which are mirrored by bench
to bedside (and back again) movements in the commercial TS and tech-
160 X                                                                Tecnoscienza - 5 (1)

nology transfer. However, the development of an open source and open
access resource like InnateDB that facilitates the translation of massive
amounts of gene and protein data into usable health information for clin-
ical and commercial developments is not well suited to such clinical or
commercial representations of TS. What InnateDB shows is the im-
portance of movements between developers and a host of prospective us-
ers (and back again) in the production of research and analysis tools. In
the case presented here those users were the wider scientific polis or aca-
demic non-peers who would use InnateDB, but the concept needn’t be
applied strictly to such users. For instance, in other cases users might also
include factions of the public, as “civic science” has elsewhere been “used
interchangeably with participatory, citizen, stakeholder and democratic
science, which are all catch words that signify various attempts to increase
public participation in the production and use of scientific knowledge”
(Bäckstrand 2003). Rather than exacerbating ambiguities that already ex-
ists around the notion of “civic science” or “civic scientist” (Clark and
Illman 2001), our intention here has been to broaden what counts as
translation science and medicine to include the construction of bioinfor-
matics systems, and to show how such systems can be more beneficially
constructed to fulfil translational tasks.


Acknowledgments

    Funding for this research was provided by Genome Canada through the
Pathogenomics of Innate Immunity (PI2) project. This work was made possible
by the interview participants within the PI2 project, for which much thanks is
owed. At the time the research was conducted, the author was affiliated with the
Centre for Applied Ethics at the University of British Columbia, Canada and
would like to thanks his project team partners there for their support and contri-
butions to this work: Janet Atkinson-Grosjean, Bryn Lander, Cory Fairley, and
Lilly Farris. Thanks to the two anonymous reviewers who provided helpful feed-
back and to the Journal’s Special Issue Guest Editors Federico Neresini and As-
sunta Viteritti for facilitating this process. The author has no conflicts of interests
relating to this work.



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Douglas                                                                   163
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