Interdisciplinarities in Action: - 麻省理工学院人工智能研究专业

Interdisciplinarities in Action:
Cognitive Ethnography of
Bioengineering Sciences
Research Laboratories

Nancy J. Nersessian
哈佛大学

The paper frames interdisciplinary research as creating complex, 分散式
cognitive-cultural systems. It introduces and elaborates on the method of cog-
nitive ethnography as a primary means for investigating interdisciplinary cog-
nitive and learning practices in situ. The analysis draws from ﬁndings of
几乎 20 years of investigating such practices in research laboratories in pio-
neering bioengineering sciences. It examines goals and challenges of two quite
different kinds of integrative problem-solving practices: biomedical engineering
(hybridization) and integrative systems biology (collaborative interdepen-
登塞). Practical lessons for facilitating research and learning in these speciﬁc
ﬁelds are discussed and a preliminary set of interdisciplinary epistemic virtues
are proposed as candidates for cultivation in interdisciplinary practices of these
kinds more widely.

介绍

1.
Interdisciplinarity is widely cast as a hallmark of frontier twenty-ﬁrst cen-
tury research in the sciences and engineering. Interdisciplinary research is
customarily characterized as “integrative” and “innovative,” yet difﬁcult to

I appreciate the support of the US National Science Foundation in conducting this research
(DRL0106773, DRL0411825, DRL097394084). The research on which this analysis is
based was a joint undertaking with my research group. I thank the members of the Cog-
nition and Learning in Interdisciplinary Cultures (clic.gatech.edu) research group for their
extensive and creative contributions to data collection, 分析, and interpretation; 最多
尤其, Wendy Newstetter (co-PI), Lisa Osbeck, Sanjay Chandrasekharan, and Miles
MacLeod. We are grateful to the lab directors and researchers for welcoming us into their
work space, granting us numerous interviews, and being so generous with time. I appre-
ciate the comments by the participants of the workshop Investigating Interdisciplinary
实践: Methodological Challenges, Helsinki, 芬兰, 2015, where an early version of
this paper was presented, as well as the comments of the editors of this special section.

科学观点 2019, 卷. 27, 不. 4
© 2019 由麻省理工学院

土井:10.1162/posc_a_00316

553

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Interdisciplinarities in Action

达到. The difﬁculty lies in the complexity of the problems posed, 这
need to develop novel cognitive practices, and the fact that interdisciplin-
ary collaboration is fraught with difﬁculties that increase with the dis-
tances between the collaborating disciplines. Although there are a broad
range of empirical methods used to investigate these dimensions, studies of
the dynamic processes of interdisciplinarity practices, 那是, how inter-
disciplinarity is enacted in situations of scientiﬁc research and the chal-
lenges posed for researchers are scant.1 Further, although detailed
taxonomies of different kinds of interdisciplinarity have been elaborated
in the abstract since 1972 (克莱因 2010), richly nuanced accounts of inter-
disciplinary practices are needed when it comes to thinking about learning
and facilitating a speciﬁc kind of research. Ethnography has long been a
method used by anthropologists to study and interpret cultural practices
situated in naturalistic settings. Most importantly, ethnographic research
enables examining both the insider (“emic”) perspective of the participants
and developing the ethnographer outsider (“etic”) interpretation of
practices of interest. 最近, within the cognitive sciences and philoso-
phy of science, “cognitive” ethnography has emerged as a method specif-
ically for studying problem-solving practices situated in real-world science
环境.

Cognitive ethnography is particularly well-suited to examining the con-
ceptual, 推理, and learning dimensions of interdisciplinary problem-
solving, where differing and often incompatible epistemic practices and
values are in play. The method is perhaps uniquely suited to investigating
the processes of integration because it enables collecting ﬁne-grained data
as researchers attempt to solve interdisciplinary problems within a complex
context of cognitive, 社会的, 材料, and cultural resources and con-
菌株. Cognitive ethnography provides nuanced ﬁndings about speciﬁc
interdisciplinary practices—how they come to be as well as how they are
used—that not only enhance our understanding of interdisciplinarity but
also can help faculty and policy makers ﬁgure out how best to facilitate
研究, especially as they develop programs to educate the twenty-ﬁrst
century scientist. The NSF-funded research program my co-PI, Wendy
Newstetter, and I embarked on ﬁfteen years ago took up both the chal-
lenge of a nuanced examination of interdisciplinary practices and of using

1. Notable exceptions in philosophy and cognitive science, most of them recent, 在-
clude Brigandt 2013; Dunbar [1995] 1999; Goodwin 1995; 大厅, Stevens, and Torralba
2002; 大厅, Wieckert, and Wright 2010; Andersen and Wagenknecht 2013; Christensen
and Schunn 2007; O’Malley, Calvert, and Dupré 2007; O’Malley and Soyer 2012. 阿迪-
tionally philosophers have begun to attend speciﬁcally to what interdisciplinary “integra-
tion” means in cases of contemporary and historical science (see for instance Griesemer
2013; Leonelli 2013; Love and Lugar 2013).

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our ﬁndings to determine ways to facilitate research and learning in
emerging ﬁelds in the bioengineering sciences. We decided at the outset
that our research group would conduct cognitive ethnographies to cap-
ture interdisciplinarity in action. I begin with a discussion of what is
“cognitive” ethnography and why it is an important method for investi-
gating interdisciplinary research practices and proceeded to a description
of the research we conducted across four labs, ending with discussion of
some signiﬁcant ﬁndings about research practices and learning in these
different interdisciplinary settings.

2. What Is “Cognitive Ethnography”?
Cognitive ethnography utilizes standard ethnographic methods for collect-
英 (ﬁeld observations, participant observation, interviews) and analyzing
(grounded coding, thematic analysis, case study, 等等) 数据. 什么
makes it “cognitive” is primarily the focus of the research questions on
problem solving, which has long been held by cognitive science to play
a central role in cognitive processes such as learning, creativity, insight,
and cognitive/conceptual change. In keeping with the traditional cognitive
science framing, cognitive ethnography conceives of problem solving as a
form of information processing that uses representations and reasoning in
pursuit of goals. Where it diverges from the traditional framing is main-
taining that the relevant representations and reasoning are not only “in the
head” of an individual, but also situated in the problem-solving environment
and distributed across one or more individuals and select artifacts. 认知的
ethnography emerged as the methodological choice of “environmental per-
spectives” (Nersessian 2005) that have been seeking an understanding of cog-
nition as an embodied, artifact-using, situated, and socio-cultural process in
which the environment does not simply scaffold, but is integral in the process.
Proponents of this perspective—here I focus on the pioneering cognitive
anthropologists Edwin Hutchins and Jean Lave—have been utilizing cogni-
tive ethnography to move the study of human problem solving out of the
psychology lab, with its use of artiﬁcial problems, and into real-world settings
with problems of everyday life, ranging from ordinary activities to sophisti-
cated work practices.2

Although Lave and Hutchins took their research in different directions,
they were both equally critical of the overly linguistic and thing-oriented

2. There are several threads of pioneering, mutually inﬂuential contributions that
came together at that time—largely by researchers spanning departments at UC San Diego
that I cannot discuss here (see for instance, 林奇 1985; Norman 1988; and Engeström
1987). I came to appropriate and develop the method from the research I discuss and only
later discovered the conﬂuence of what had become by then separate endeavors.

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Interdisciplinarities in Action

construal of culture of cognitive anthropology and of the context-free,
body-independent “functionalist” construal of cognition of cognitive psy-
chology and AI. These construals together led to the notion within cogni-
tive science that culture provides representations (“content,” “ideas,” see,
例如, D’Andrade 1987) that cognitive processes or mechanisms operate on,
and thus the cognitive processes could be investigated separately and re-
moved from the contexts in which they are customarily exercised. 这
divide led to the paradigm of studying cognitive processes—memory, 雷亚-
soning, 表示, 学习, and so forth—in the artiﬁcial situations of
psychology experiments and AI modeling. Lave (Lave 1988) rooted her
critique in the problem of why it is that people are generally more
competent at various reasoning and problem-solving skills in real-world
contexts—such as arithmetic practices in grocery stores, in monetary prac-
tices among Brazilian street children, and in Liberian tailoring—than in
experimental settings or traditional school settings where educational prac-
tices are inﬂuenced by the view that cognition is all “in the head.” These
ﬁndings led her to argue that the “more appropriate unit of analysis is the
whole person in action, acting with the settings of that activity” (Lave
1988, p. 17). “Cognition” is to be understood as “stretched across mind,
身体, 活动, and setting” (Lave 1988, p. 18). Lave’s initial research used cog-
nitive ethnography as a method to investigate the reasoning and problem-
solving practices of “just plain folks,” for instance, people grocery shopping
for the best buy. Later she and followers extended it to a range of “commu-
nities of practice” (Lave and Wenger 1991).

Hutchins began studying “human cognition in its natural habitat,”
(哈钦斯 1995, p. xiii) in his research on inferential practices of Trobriand
Islanders regarding land tenure, which established that, seen in context,
their so-called “primitive thought” processes use the same logical operations
as Western thought (哈钦斯 1980). His most inﬂuential research, 尽管,
has focused on problem solving in technologically rich and well-deﬁned
problem environments: piloting planes and ships, where “natural” is ex-
tended to comprise not only ordinary settings but also work contexts,
IE。, “naturally occurring culturally constituted human activity” (哈钦斯
1995, p. xiii). In this research he articulated the analytical framework of
distributed cognition3 and the method of cognitive ethnography as a
means for systematically studying cognitive processes that are “distrib-
uted” across complex systems of interacting humans and artifacts in
real-world problem solving practices. Hutchins’ main theses underlying

It is important to underscore that Hutchins’ position is that distributed cognition
is an analytical framework and not an ontological claim as advanced by Andy Clark with his
“extended mind thesis” (哈钦斯 2011).

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this approach are that: 1) culture needs to be reconceptualized as a process,
not content to be added to cognitive mechanisms, determined independently,
和 2) cognition is a cultural process in which “people create cognitive powers
by creating the environments in which they exercise those powers” (哈钦斯
1995, 169). The dynamics of these “cultural practices account for much of
what is needed to account for the origin of human cognitive systems”
(哈钦斯 2011, p. 445). 因此, cognition and culture are integral to one
其他. Cognitive ethnography, 所以, provides a method for studying
the origins, 发展, and enactment of the situated problem-solving
practices of complex evolving distributed cognitive-cultural systems, 这样的
as those created by interdisciplinary researchers (Nersessian 2006; Nersessian
等人. 2003; Nersessian et al. 2002; Chandrasekharan and Nersessian 2015).
Cognitive ethnography, 当然, can be used to study disciplinary practices,
but it is an especially useful method for gaining insight into the nuances of
different kinds of interdisciplinary practices, 例如, what “integration”
方法, how it is to be achieved, and the challenges it poses in a speciﬁc
problem-solving context. 尤其, it enables focusing on how re-
searchers negotiate disparate and often conﬂicting modes of practice and
epistemic norms and values in the processes of problem solving (MacLeod
and Nersessian 2016; Osbeck and Nersessian 2017).

Research in the distributed cognition framework largely consists of
detailed observational case studies employing ethnographic methods.
The overarching objective, 然而, differs from socio-cultural studies that
aim mainly to ferret out the social, 文化, and material facets of a case.
As cognitive science accounts, cognitive ethnographies need to move be-
yond just creating the requisite “thick description” (Geertz 1973) of eth-
nographic analysis, with richly nuanced details of the speciﬁc case, to also
providing a more general, abstract account of cognition. The aim of the
cognitive science research is to understand the nature of the regularities
of cognition in human activity. We aim to do likewise in investigating
interdisciplinary scientiﬁc practices.

The conceptual and methodological framing of our research project was
designed to advance an account in which cognition and culture are mutu-
ally implicative in authentic scientiﬁc practices. We chose the method of
cognitive ethnography since it offered the possibility to analyze interdisci-
plinary problem-solving practices as situated in evolving distributed
cognitive-cultural systems (Nersessian 2006; Nersessian et al. 2003). 我们
aimed to capture in detail the dynamics of interdisciplinary integration
in the day-to-day struggles of the researchers as they made decisions
about how best to scope a complex problem, what resources (conceptual,
methodological, 材料) to bring to bear on solving it though building
型号, and how to justify the compromises (例如, abstractions, restrictions,

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Interdisciplinarities in Action

等等) they made in the process. In several cases we were able to
develop studies of individual researchers or groups as they worked on
solving a problem over months and even years.

Project and Methods Overview

3.
The bioengineering sciences use a range of engineering and biosciences re-
sources to conduct basic biological research in the context of application.
例如, the tissue engineering lab we investigated has been carrying
out ground-breaking research on basic endothelial cell biology to under-
stand the effects of forces on these cells as blood ﬂows through the vessel,
while having as a major objective to create living tissue substitutes for dis-
eased arteries. The bioengineering sciences occupy a major position in
twenty-ﬁrst century science, are inherently interdisciplinary, and are
复杂的: cognitively, technologically, and collaboratively. They self-
consciously seek to “integrate” concepts, 方法, 理论, and materials
from engineering, 生物学, and medicine. The impetus for this kind of
research has come primarily from engineering. This movement of engi-
neering into biology has given rise to a multifaceted interplay of quite dis-
parate conceptual frameworks, methodological approaches, and epistemic
价值观. Innovation in methodological practices is required to tackle the
小说, largely engineering-directed research problems being formulated
concerning complex biological systems. A signature practice of these ﬁelds
is investigating biological phenomena through designing, building, 和
experimenting with surrogate in vitro physical simulation models (com-
prising biological and engineering materials) or computational simulation
型号. The focus of our research has been examining the dynamics of
these problem-solving practices, 具体来说, the challenges around devel-
选项, 使用, 和学习.

Our investigations comprise four pioneering interdisciplinary university
research laboratories: two in biomedical engineering (BME): tissue engi-
neering and neural engineering and two in integrative systems biology
(ISB): one solely computational, the other a combined computational
and wet experimentation lab. We chose university labs because they are
largely populated by graduate students who are simultaneously pioneers
in research and learners. Our investigations have established that the forms
of interdisciplinarity practiced in BME and in ISB are quite different, leading
to different challenges for problem solving. The BME labs are populated by
graduate students in an educational program (that we were using our research
to help design) aimed at moving beyond collaboration between engineers and
biologists through producing hybrid bio-medical-engineering researchers. 在
both labs, the researchers tackle problems of bringing together cells and
tissues and engineered materials in processes of designing, building, 和

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experimenting with dynamical living hybrid models to simulate in vivo
biological phenomena. The ISB labs aim to understand, intervene on,
and control biological systems comprising integrated, interacting, 复杂的
networks of genes, proteins, and biochemical reactions. Solutions to the
problems posed require constructing computational simulation models in
need of rich experimental data, which creates an essential epistemic inter-
dependence among the participating ﬁelds: computational sciences, engi-
neering sciences, and biological sciences. The nature of the problems posed
in ISB requires both a high degree of specialization and collaboration. 这
computational lab comprises primarily engineers of various kinds, 但所有
conversant with systems engineering, and applied mathematicians who col-
laborate with bioscientists external to the lab. The combined lab is attempt-
ing to develop modelers (predominantly engineers), who conduct their own
experiments in the service of building computational models (“bimodal re-
searchers”; MacLeod and Nersessian 2013a), but also collaborate with exter-
nal bioscientists.

3.1. Data Collection and Analysis
Our objective in data collection can be summarized as: starting from an
open and broad stance about what might be relevant to our research ques-
系统蒸发散, to conduct a systematic long-term investigation involving numerous
scientists across a broad range of perspectives, 问题, and lab organiza-
tions so as to collect a range of different data from which to triangulate the
分析. Although there were variations in our research questions for the
two ﬁelds, they took the general form of: 1) what are the cognitive practices
used in problem solving and the challenges these present? 2) what supports
and facilitates learning? 和 3) how is interdisciplinary integration manifest-
ed in the lab research and learning processes? For the ISB study, our pre-
liminary research brought to the fore questions of identity, so we added
4) what are the implications for interdisciplinary identity in the appropria-
tion of different cognitive resources?

Each lab was investigated for approximately ﬁve years. 数据采集
in all labs comprised: audio-taped open and semi-structured interviews;
participant ﬁeld observation with note-taking; lab tours (initially those
given for us, then for visitors); arranged demonstrations of experimental
procedures and technologies involved in their data collection and analysis;
video and audio recorded lab meetings; journal club meetings; photo-
graphs of white boards and lab space evolution; and artifact collection:
grant proposals, paper drafts, powerpoint presentations, dissertation pro-
posals, emails, diagrams/sketches, 等等. The extent of our interview
and observational data is summarized in Table 1. All interviews and some
meetings have been transcribed.

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桌子 1. Data summary.

Laboratory

Interviews

Meetings

Field
观察结果 (小时)

BME A

BME D

ISB G

ISB C

40 (加 16 journal club)

15 (加 2 joint C and G)

∼300
∼500
∼20
∼250

Research Sites

3.2.
To understand better the different challenges of interdisciplinary problem
solving faced by researchers as they attempt to integrate engineering and
biology in these environments, I provide a brief overview of the kinds of
problems addressed in each lab. All the labs we investigated were pioneers,
conducting research for which there was little or no precedent when they
began. In the BME labs we conducted intensive data collection over the
ﬁrst two years and followed-up selected dissertation projects through to
completion, including additional interviews, for a total of ﬁve years.4 Both
labs designed, 建成, and conducted experiments on living physical
型号, locally called “devices.”

Lab A’s overarching research problems were to understand mechanical
dimensions of cell biology, such as the effects of the forces of blood ﬂow
on gene expression in endothelial cells, and to engineer living substitute
blood vessels for implantation in the human cardiovascular system. The dual
objectives of this lab explicate further the notion of an engineering scientist
as having both traditional engineering and scientiﬁc research goals. 考试-
ples of intermediate problems that contributed to the daily work included
designing and building living tissue models—“constructs”—that mimic
properties of natural blood vessels; using biomechanical forces to create
endothelial cells from adult stem cells and progenitor cells; designing and
building environments for mechanically conditioning constructs; 和的-
signing means for testing their mechanical strength.

Lab D’s overarching research problems were to understand the mecha-
nisms through which neurons learn in the brain and, 潜在地, to use this
knowledge to develop aids for neurological deﬁcits and “to make people
smarter” (director). Examples of intermediate problems that contributed

I discuss these labs in the past tense because both are now closed.

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to the daily work included: developing ways to culture, stimulate, 控制,
record, and image neuron arrays; designing and constructing feedback en-
vironments (robotic and simulated) in which the “dish” of cultured neurons
could learn; and using electrophysiology and optical imaging to study
“plasticity.” One researcher developed a computational model of the dish
model-system that played an unanticipated pivotal role in the research.

In the ISB study we had fewer resources and researchers, so we conducted
intensive data collection in each lab over the ﬁrst year and followed selected
dissertation projects through to completion for a total of ﬁve years. Both labs
built and experimented with computational simulation models.

Lab G’s research problems focus on computational and mathematical
modeling of biological systems at the genetic, metabolic, and cellular levels.
The focus of the modeling is on the interactions among different components
of biological systems (such as metabolic and signaling pathways), 而不是
on structural properties of speciﬁc components (such as DNA, Ribosome).
The problems addressed are wide-ranging. 例如, one of the problems
tackled by the lab was developing a model of the production and transport of
dopamine and of how this system is affected in Parkinson’s disease. 在这个
research the lab worked with experimental data provided by a medical
research group specializing in neurodegenerative disorders. Another problem
was developing a model of ethanol production using algae, based on data
provided by researchers at a biofuel company. 一般来说, the domain-driven
problems are provided by bioscience researchers of various kinds who
approach the lab to model their data. The overarching focus of the lab’s
own agenda is on methodological problems in modeling, especially develop-
ing mathematical techniques to improve the estimation of model parameters,
and the optimization of these parameters.

Lab C’s research is guided by an overarching problem: to understand the
impact of redox (reduction-oxidation) environment on proteins through
systems modeling approaches. Cells maintain a reduced internal environment
under normal physiological conditions. 然而, oxidizing molecules and
free radicals that are produced in the cell as a part of physiological processes
or that enter it can react with cellular components such as DNA, cell
membranes, and proteins. Such reactions have physiological consequences
and have been implicated in several diseases. Lab C’s research focus is on
the impact of alterations made by oxidants on proteins, which are part of
signaling pathways, and on the dynamics and outcomes of these pathways.
Based on her own training, the director has been developing some graduate
researchers with the ability to do biological experimentation in the service of
building and testing their computational models. One student also engaged
in engineering design through collaborating in developing a microﬂuidic
设备 (“lab-on-a-chip”) to produce single cell and population data that are

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more amenable to quantitative investigation (Aurigemma et al. 2013). 这
lab’s broad overarching problem translates into research projects as varied as
modeling chemotherapeutic drug resistance in acute lymphoblastic leukemia
cells and modeling senescence in T cells.

3.3. 数据分析
We used a variety of complimentary qualitative methods; 具体来说, 哪个-
itative data coding, case study analysis, thematic analysis, and cognitive-
historical analysis. The qualitative data coding methods we have been
using comprise: systematic, ﬁne-grained open coding and “grounded the-
ory” development. There is an extensive range of qualitative methods that
have been developed and critiqued over the last half-century, especially in
psychology and sociology (for overview see Patton 2002). Qualitative
methods applications are not formulaic or recipe-like, so we have needed to
tailor and innovate in data analysis with respect to our research goals and
问题, while adhering to the canons of what constitutes “trustworthy”
(Lincoln and Guba 1985) and “validated” (especially the American Psycholog-
ical Association standards; see Eisner 2003) data collection and analysis pro-
cedures. These canons require systematically collecting a range of different
data sufﬁcient for triangulating data from multiple sources from which to cor-
roborate and determine the referential adequacy of interpretations. Our re-
search conducted long-term studies that provided longitudinal data
consisting of persistent observations, multiple interviews of each participant,
and the kinds of archival data previously mentioned. Since I want to focus the
remainder of the paper on ﬁndings pertaining to interdisciplinarity, here I
brieﬂy mention only a few of our data analysis procedures relating to ground-
ed coding (Corbin and Strauss 2008; Glaser and Strauss 1967) since the codes
provide the basis for our interpretations.

We practiced what we dubbed “team ethnography.” More than one eth-
nographer had responsibility for observations and interviews in a given lab,
and the more senior members of our group worked across the labs. 我们的
weekly research group meetings provided the venue for discussing the eth-
nographic work with one another as it unfolded and for reaching consensus
on coding, theme development, and other forms of data interpretation as
we were relating our ﬁndings to appropriate cognitive, socio-cultural, 和
philosophical theoretical frameworks. Our research group varied in size
and composition over time, but remained highly interdisciplinary and thus
provided multiple lenses through which we could “theorize” the data.5

In qualitative research, the researchers are considered the “instruments” of data col-
lection and analysis. Each of us already had an interdisciplinary background when we joined
the project. Our group brought a wide range of perspectives to the project: philosophy and

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Coding development took place in several stages. During open coding,
coding pairs from different backgrounds in our research group worked
collaboratively on each transcript. Interviews were analyzed progressively,
line by line, from beginning to end, with the aim of providing an initial
description for as many textual passages or “meaning units” as seemed ap-
propriate to both researchers. Coders consulted with the entire research
group on the clarity, ﬁt, and logic of the codes assigned. Feedback was used
to make adjustments. Consistent with the goals of analytic induction
(codes emerging from data) and constant comparison (Lincoln and Guba
1985; Corbin and Strauss 2008), the coders resumed coding of additional
interviews, revisiting previous coding, and assessing descriptions for ade-
quacy and ﬁt throughout the process. Research group meetings reviewed
all codes and further grouped and arranged codes into superordinate catego-
ries and subcategories. We then related codes and developed the categories/
concepts as a start towards building “theory.” In this context, theorizing is
明白了, 宽广地, as formulating “a set of well-developed categories
(主题, 概念) that are systematically interrelated through statements
of relationship to form a theoretical framework that explains some phenom-
enon” (Corbin and Strauss 2008, p. 55).

We have been using the codes and categories to analyze the data further
and to examine them through various theoretical lenses. Our case study
analyses take the form of “thick descriptions” (Geertz 1973). The case stud-
ies follow practices of a speciﬁc researcher, or small group, as they worked
on solving a complex problem. The thematic analyses develop descriptions
and interpretations of various dimensions of the evolving cognitive-cultural
systems of the labs, including interrelated trajectories of problems, tech-
逻辑的, 型号, 研究人员, 学习, and development of practices, 英语-
pecially methods. The cognitive-historical analyses (Nersessian [1987]
2008) focus on examining the development and use of investigative and
learning practices through the lens of pertinent cognitive science research,
as well as using ﬁndings to extend and critique cognitive research.

Interdisciplinarities in Action: Selected Findings

4.
It is widely agreed that the chief characteristic of interdisciplinary research
is integration.6 Integration is what promotes creativity and innovation.

history of science (物理, 生物学, 心理学), cognitive science (人工智能, cognitive psychol-
奥吉, 哲学), linguistic anthropology, learning science, human-centered computing,
theoretical psychology, 精神分析, 建筑学, and industrial design.

6. A broad characterization of interdisciplinary research has been proposed by the US
National Research Council: “a mode of research by teams or individuals that integrates
信息, 数据, 技巧, 工具, perspectives, 概念, and/or theories from two or
more disciplines or bodies of specialized knowledge to advance fundamental understanding

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What is needed is both a more nuanced understanding of what “integra-
tion” means in the problem-solving practices of quite different interdisci-
plinary “epistemic communities” (Cetina 1999) and the speciﬁc challenges
encountered in trying to achieve it. Although it is not possible to provide
extended analyses here, cognitive ethnography enabled us to examine in
ﬁne detail how our researchers determined how to reconceive a complex
biological system with the engineering and computational resources at
hand so as to be able to solve—at least partially—the target problem. 这
process often required adapting concepts and methods they transferred
from engineering as well as creating new ones speciﬁcally arising from
the bio-engineering integration they were attempting.

As one can imagine, the ﬁndings from long-term investigations are rich
and varied. We do not claim to have captured all the nuances of the range
of interdisciplinary practices in either BME or ISB. An important goal of
ethnographic research of multiple sites is to assess transferability: to ascer-
tain what abstracted insights might be in common across sites and possibly
extended to the broader ﬁeld, and which are unique to a site. Many of our
ﬁndings of the challenges of integrating engineering and biology in BME
transferred robustly across the two labs. The ISB labs were different as
described earlier and various aspects of the modeling process differed.
然而, our major insights about the challenges of integrating biology,
工程, and computation in ISB problem-solving practices did trans-
fer. We have presented our ﬁndings to audiences of researchers outside of
our studies in each ﬁeld and have done sufﬁcient broad sampling of each of
the ﬁelds to feel conﬁdent that our research provides signiﬁcant insights
relevant to the practices and challenges of interdisciplinary research and
training across the ﬁelds. 部分 4.1 will provide an overview of our anal-
ysis of cognitive practices and challenges in BME problem-solving and
部分 4.2, in ISB. 部分 4.3 will focus on some the challenges of col-
laboration in ISB.

4.1. BME Problem-Solving
The overarching problems BME poses are directed towards using engineer-
ing design methods and principles to create interventions for speciﬁc med-
ical disorders. Examples include constructing living implants that can
perform normal functions of arteries and developing brain-controlled pros-
thetic devices that neurons can learn to control. The kind of biomedical
engineering we investigated developed programs of in vitro research using
physical simulations models (called “devices” in our labs) to investigate

or to solve problems whose solutions are beyond the scope of a single discipline or ﬁeld of
research practice” (NAS, NAE, and IM 2005, p. 26).

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both selected aspects of the biology of in vivo systems and create novel
artifacts and technologies for medical application. Devices are hybrid arti-
facts where cells or cellular systems interface with nonliving materials in
model-based simulations run under various experimental conditions
(Nersessian [2008] 2009; Nersessian and Patton 2009). This kind of re-
search program was developed by engineers who wanted to investigate
problems such as the effects of shear stresses on endothelial cells in arteries,
but found it difﬁcult to interest biologists in the problems. The in vitro
research approach was developed because many of the problems the ﬁeld
poses either require a level of control not possible to achieve in animal re-
search or would be unethical. In each BME lab there was one or more de-
vices central to the evolving research program. Because the devices are
created to address the speciﬁc research problems of a lab, they are usually
设计的, constructed, and redesigned in-house through several iterations.
The devices participate in experimental research in various conﬁgurations
of hybrid “model-systems.” As one researcher explained, they “use that
[notion] as the integrated nature, the biological aspect coming together with the
engineering aspect, so it’s a multifaceted model-system.”7 Cognitive ethnography
enabled focusing on the nature of the on-going challenges faced by re-
searchers as they ﬁgured out how to design, 建造, and experiment with
these hybrid “multifaceted model-systems” so as to replicate the in vivo
phenomena at the right level of abstraction to provide useful information
about speciﬁc processes in a complex biological phenomenon, while also
keeping the cells and tissues alive over extended periods. The “dish” model-
system of Lab D provides a brief exemplar of these challenges.

The main research problem of Lab D was to understand learning in net-
works of living neurons, which they operationalized in terms of the net-
work’s ability to form connections and reorganize itself (“plasticity”). 作为
with most engineering, understanding of the phenomenon is related to
the ability to control it, 在这种情况下, to create supervised learning in neuron
cultures. Prior to setting up his own lab, the director did postdoctoral
research in another lab to help develop a platform for stimulating and re-
cording neuron cultures, the “multi-electrode array” (MEA). The construc-
tion of the dish model-system requires breeding rats with neural cells
expressing green ﬂuorescent protein (for optical imaging) 和技术
for dissociation and plating of neural and supporting glia cells onto the
MEA (electrodes poking up for recording and stimulation). The MEA has
been constructed so as to enable the cells to form a long-lasting (months to
年) living culture. The dish comprises only a monolayer of cells both be-
cause of the design of the electrodes and because it is more likely to live

7. All italicized quotes are from interviews with lab members.

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更长. The researchers rationalized that complexity-reducing choice by ar-
guing that if learning can be produced in a monolayer of cortical neurons,
then they would have a highly signiﬁcant result (which they did within
four years). They designed an environmental chamber to house the dish so
that both optical imaging and electrophysiological investigations were
可能的. Recording, simulating, and analyzing the data required lab mem-
bers to build a special set of software tools; likewise, for visualizing dish
electrical activity. 最后, for building out this “open-loop” model-system
into an embodied model-system that would provide feedback to the dish, A
number of computational “animats” and real-world robots (“hybrots”)
needed to be designed to interface with the dish though programs that
appropriately model such things as muscle movement.

As mentioned previously, this kind of in vitro research was initiated by
engineers who either could not recruit bioscience collaborators or who
found such collaborations inherently difﬁcult because biologists lack the
requisite quantitative and engineering knowledge to facilitate collabora-
的 (Nersessian 2017a). 因此, the senior BME researchers in our setting
enlisted our assistance in devising an educational program that aimed at
developing their graduate students into hybrid researchers to meet the
goals and challenges of twenty-ﬁrst century BME. The stated aim was
to create “interdisciplinary integration” at the level of the individual re-
searchers.8 We coded this integration as we saw it enacted in the labs as
“hybridization”—hybrid bio-medical-engineering researchers capable of
carrying out individual projects with hybrid devices and with the ability
to collaborate ﬂuidly with disciplinary colleagues in their work beyond the
labs (what we call “boundary agents”).

In the BME context then, integration as hybridization takes on the
meaning of ﬁtting together, blending or fusing into an inseparable whole,
which creates something genuinely novel. One way in which we analyzed
the nature of the fusing and its challenges for problem solving was through
expanding an emergent category from our coding, “interlocking models,”
into a major analytical theme. Interlocking models is a multidimensional
system-level notion that serves to articulate how components of the lab
construed as a distributed cognitive-cultural system are ﬁtted together.

8. They later adopted the characterization of “interdiscipline” meaning “interdisci-
plinary discipline” for their ﬁeld. “Many educational programs in BME might be described
as ‘engineering with a little biology thrown in.’ We maintain that practitioners for the
twenty-ﬁrst century need to be trained in a truly integrative fashion. BME is best under-
stood as an ‘interdiscipline’; 那是, a ﬁeld that is inherently interdisciplinary. BME is sit-
uated at the intersection of three disciplines: 生物学, 工程, and medicine. All three
are essential to the practice of a biomedical engineer” (from proposal submitted for State of
Georgia Regents’ Award for Best Educational Program, which they were awarded).

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Dimensions pertaining to the challenges of interdisciplinary facets of the
cognitive practice of model-based simulation with hybrid devices include
conceptual (“mental models”) and artifactual integration. Model-systems
are designed and constructed to comply with interlocking biological and
engineering constraints. The daily challenges of both research and learning
center on determining the appropriate, selective interlocking of biology
and engineering concepts, 方法, and materials for the problem at hand.
With respect to interdisciplinary integration, the interlocking models re-
quired for conceiving, designing, and experimenting with dish model-systems
(as developed through our codes) comprise at least those in Table 2.

The BME kind of interdisciplinary problem-solving requires the re-
searcher to interlock the relevant biological and engineering constraints
for creating, experimenting, and reasoning by means of physical simula-
tion models. Throughout the research, problem-solving processes require

桌子 2. “Interlocking models” category.

Interlocking Models

Biological and engineering models in the wider community

(as detailed in journals, textbooks, 等等)

cell biology

biochemistry

神经科学

电气工程

neural engineering

mechanical engineering

Bioengineered in vitro artifact models

dish

animat

hybrot

MEArt

ETC.

Researcher mental models of

in vivo and in vitro phenomena

devices qua in vitro models

devices qua engineered models

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using various abstractive and evaluative procedures to select and merge rel-
evant constraints. This selectivity enables focusing on features relevant to
the problem-solving process while bracketing potentially irrelevant fea-
特雷斯. 例如, the choice of using only a monolayer of cortical neurons
would be revisited if more elements were determined to be relevant epi-
stemically, and the MEA would then also need to be redesigned to accom-
modate those changes.

ISB Problem-Solving

4.2.
ISB is a young ﬁeld, though it shares objectives with an older systems bi-
ology philosophy. A major goal is to develop analyses of complex nonlinear
biological phenomena at the system level. The traditional approach of
well-controlled experimentation focused on characterizing select compo-
nents or processes is necessary but not sufﬁcient for investigating how
higher-level functionality emerges from myriad interactions at lower
级别. The conﬂuence of new kinds of data production and collection
(high-throughput) 技术, computational resources (例如, 高的-
performance computing and novel parameterization algorithms), and the de-
velopment of curated biological data bases and internet search engines for
seeking biological literature has made it possible to bring quantitative and
computational methods to bear on the problem of developing an integra-
tive analysis of the behavior of complex biological systems at all levels,
from intracellular interactions to ecosystem processes. Finding solutions
to the problems the ﬁeld is posing creates an essential epistemic interde-
彭登斯 (MacLeod and Nersessian 2016; Andersen and Wagenknecht
2013) among the participating ﬁelds: various engineering ﬁelds, compu-
tational sciences, biological sciences. ISB at present does not have a uniﬁed
vision of what a researcher needs to learn/know to be an effective problem
solver. The nature of the problem-solving requires both specialization and
collaboration.9 As with Lab C there are some attempts to develop hybrid
modeler-experimentalists, 然而, in the present situation modelers
(mainly engineers, applied mathematicians, and physicists) and experi-
mentalists (mainly molecular biologists and biochemists) need to collabo-
速度. As will be discussed in section 4.3, with little knowledge of one

9. We have been characterizing this kind of interdisciplinary ﬁeld loosely as a “trans-
discipline” but deﬁnitions of “transdisciplinary” in taxonomies are vague and often contra-
dictory, and do not quite capture the nuances of ISB. The kind of interdisciplinary
integration we witnessed has features of what Peter Galison (Galison 1997) calls “interca-
lation” where ﬁelds keep separate identities and practices while possibly transforming one
another in signiﬁcant ways. But the need for working partly in the ﬁeld of the other seems
not captured by his analysis. “Symbiosis” is perhaps a better characterization of the nature
of ISB interdisciplinarity.

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another’s ﬁelds, collaboration is fraught with difﬁculties. In the present
state of the ﬁeld our research indicates the onus is on the modeler to be
the boundary agent, stepping into the biological arena to build their
型号.

Constructing computational simulation models of complex biological
systems is the overarching problem posed by ISB. Interdisciplinary “inte-
gration” in the labs we studied is largely achieved by infusing experimen-
tal data gathered from a range of disparate sources into the computational
models that are built in iterative processes. 理想情况下, the model output
provides both insight and understanding into the system phenomena
and hypotheses to guide experimentation. Model-building requires trans-
ferring and adapting concepts and methods developed mainly for model-
ing engineering systems to modeling biological systems, such as modifying
wave smoothing techniques from signal processes in telecommunications to
smooth noisy biological data. “Integration” at the conceptual level means, 作为
one researcher noted: “the tasks of this new frontier require thinking beyond linear
chains of causes and effects: thinking in terms of integrated functional entities; 思维
in systems, 网络, and models.” The kinds of modeling problems researchers
tackled during our research included response of cancer cells to chemotherapy
药物, sustainable biofuel production, arteriosclerosis as an inﬂammatory pro-
过程, and yeast metabolism. The same modeler could be working on cancer
today and yeast tomorrow. The Lab G director maintains that modelers can
tackle a range of biological problems because they have “ﬂexibility to recognize
shared features of control/regulation across disparate domains,” which comes from
experience with engineering systems. But their understanding of control/reg-
ulation needs to be adapted to biological systems. Because the domain is con-
tinually shifting, our modelers all maintain that deep knowledge of a speciﬁc
biological ﬁeld would not be helpful. 因此, collaboration with experimental-
ists with deep knowledge of the biology of the problem at hand is critical to
the objective of system-level analysis. Since Lab G is the predominant way of
working at present, I brieﬂy overview its practices.

Lab G uses laptop computers and builds ordinary differential equation
(ODE) models primarily of gene regulatory, cellular, metabolic, and cell-
signaling networks. The researchers bring whatever concepts, 方法,
and bits of theory from systems engineering they ﬁnd useful to bear on
building models of biological systems for which they were not designed.
此外, these researchers also work on developing new parameteriza-
tion algorithms in order to overcome the problem of inadequate data. 他们
address problems presented to the lab director by outside collaborators from
academia and industry. These collaborators have little grasp of modeling and
usually have insufﬁcient data or data of the wrong kind (例如, steady state
instead of time series) to build robust and informative models. The burden is

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on the modeler to derive the needed data from the literature, but most often
they are left with many undetermined parameters. Often it is impossible to
get timely new experiments done to check the predictive hypotheses their
models yield. The problem-solving processes of one modeler whose goal was
to produce a model of lignin production in alfalfa provides a brief exemplar.
Industry bioscientists had been working on converting alfalfa to a biofuel
and had created several transgenic species, but these were “recalcitrant” in
producing a biofuel. They approached Lab G with a problem: to model the
lignin pathway of alfalfa to determine if it is possible to “tweak” it and
break down lignin so as to better optimize current transgenic biomass-
producing species to produce a biofuel. The modeler’s chief target was to
build a model that would connect concentration levels of the building
blocks of lignin and the key reactions known to be important in the gen-
eration of sugars. The researchers would not provide him with all their
data since they had not published them (which took another six months)
and seemed not to realize the modeler both needed the data to build the
model and would not publish them himself. A search of the literature
provided data too sparse to model alfalfa, so while he waited the modeler
decided to model a closely related woody species, poplar, for which there
were more data. He hoped some of what he produced would transfer to the
alfalfa model, but this only worked at steady-state (wild-type equilibrium)
not for the transgenic species. Building out the alfalfa model now using
the collaborator data from seven transgenic species left him with twenty-
seven open parameters. He did model optimization for only a few “signiﬁcant
参数,” determined as those that correlated maximally over thousands of
model instantiations with changes in the targeted ratio of monomers. 那里-
maining parameters were set to values the modeler deemed “biologically rea-
sonable.” In the end, ﬁve optimized models that converged on similar math
relations for the target variables were found to test well and gave similar pre-
措辞 (with no guarantee a global optimal was found). With a consistent
convergence, he argued the outcome of ﬁve well-performing models provided
“model validation.” What is remarkable is that in the process of getting the
model to ﬁt, the modeler needed to hypothesize major changes to the long-
established lignin biological pathway. In particular he hypothesized that an
unknown factor outside the current pathway is having a signiﬁcant regulatory
effect on it. Lacking biological knowledge, he called the factor “X.” This hy-
pothesis piqued the interests of his nonresponsive biological collaborators,
who then determined experimentally what that factor was, and together they
published these results—a major biological ﬁnding.

As witnessed in both our labs, without effective collaborations, 缺乏
biological knowledge and insufﬁcient data increase complexity of the
modeling work. The complexity of biological networks includes frequent

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feed-forward and feedback effects and many elements play multiple roles in
a network. Every problem requires modelers to adapt or tailor methodo-
logical strategies to transform it into one they have the potential to solve.
They are required, 他们自己, to search through the available biological
literature and data bases to build out the biological metabolic and signal-
ing pathway diagrams of the system under investigation sufﬁciently to
inform the modeling process. Usually the researcher starts from a small
piece of a pathway provided by a collaborator or found in the literature
and then ﬁlls it out making “guesses” about “what is reasonable” to
add/alter in conjunction with running simulations, with and without
件, as they build the model. They need to predict what effects a mod-
iﬁcation of the biological pathway representation will have, and locate
where a modiﬁcation needs to happen to solve the problem. They try to
check their guesses with their collaborators, but often ﬁnd them unres-
ponsive. 更远, what resources are to be used in building the model
are largely at the discretion of the modeler. Systems biology lacks the estab-
lished domain theories that in physics-based sciences provide representa-
tional resources and methods for building reliable simulation models. 在
our analysis, we developed the superordinate category “managing complex-
ity” to capture a range of codes that emerged with respect to the challenges
researchers face in building simulation models. We expanded this category
into a major theme for analyzing, 尤其, the cognitive challenges for
problem-solving via simulation model-building (Chandrasekharan and
Nersessian 2015; MacLeod and Nersessian 2013b; MacLeod and Nersessian
2015). Every model is a strategic adaptation to a set of constraints ranging
from those of the complexity of the biological problem to the fact that sim-
ulation experiments and real-world experiments take place on vastly differ-
ent time scales to the human cognitive constraints to the challenges of
collaboration. Most of these constraints cannot be mitigated, but our inter-
views with modelers and experimentalists led to insights into small learning
interventions we thought could be useful to enhancing collaboration.

Challenges and Strategies for Collaboration in ISB

4.3.
Across interdisciplinary ﬁelds generally, the dilemma is whether to educate
researchers as specialists or polymaths to meet their problem-solving de-
要求. Our investigations have led us to see the response to the “specialists
or polymaths dilemma” as lying in compromises that are adapted to the
speciﬁc situation of the research. Cognitive ethnography provides a unique
means to investigate the details of these compromises and adaptations as
they are made in the problem-solving process. In ISB problem-solving,
modelers and experimental collaborators both have the objective of
producing a computational model/simulation that should be biologically

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informative, especially with respect to providing experimental guidance.
Our focus has been on modelers but our analyses also have been directed
towards the interdependence with experimentalists and what makes for ef-
fective collaborative problem-solving (cognitive and cultural dimensions).
Although the requirements for effective problem solving in these contexts
lie more towards the specialist end of the spectrum for both, 我们发现
effective collaboration requires more than cursory acquaintance with the
collaborating ﬁeld. Yet what we witnessed in the labs we investigated
(and have been told by numerous other researchers is the current state of
the ﬁeld more globally) is that modelers have little understanding of the
possibilities and constraints of experimental practices and experimentalists
have little understanding of the nature and requirements of model
building—and neither has an understanding of the epistemic values of
另一个. Although the bimodal route of Lab C, where modelers learn
to conduct their own experiments in the service of model building might
seem the way to go, the Lab G director pointed out that there is a “phil-
osophical divide among system biologists” as to whether modelers should con-
duct their own experiments. 每个, as he said, is a “full time job, and if you
don’t want to do two full time jobs, something will suffer from it.” Even the bi-
modal researchers in our study, while recognizing the advantages of creat-
ing their own experimental data, expressed that concern, and some said
they envisioned having students in their labs focused on one or the other.
Our strategy was to determine, from the nature and challenges of the
problem-solving practices we witnessed and from those discussed in inter-
意见, 什么, at a meta-level, are some learning requirements for effective re-
搜索. 然后, because each side stressed the limited time available to spend on
work that was not strictly modeling or experimenting, we needed to deter-
mine how such learning might be achieved using a “small interventions, 大的
payoff” approach. The problem-solving practices around the theme of manag-
ing complexity, collaboration, and many others that could be noted, entail
signiﬁcant demands for participating in the cognitive-cultural systems of
ISB research. Our analyses identiﬁed interrelated characteristics that lead to
effective collaboration and problem solving: 1) cognitive ﬂexibility, 2) 国际米兰-
actional expertise, 和 3) epistemic awareness. From an epistemological per-
观望的, these characteristics are epistemic virtues for conducting good
interdisciplinary science, 那是, interdisciplinary virtues. According to Linda
Zagzebski, a virtue is “a deep and enduring acquired excellence” motivated by
and reliably successful at achieving intellectual ends (Zagzebski 1996,
p. 137). She asserts, following Aristotle, who ﬁrst introduced the notion
that there are intellectual as well as moral virtues, that virtues are acquired
by practicing them. What we are calling interdisciplinary virtues have
socio-cultural as well as cognitive dimensions in that cultivating them

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promotes the development of collaborative communities of researchers. 我们
proposed to investigate if these speciﬁc interdisciplinary virtues could be
cultivated through targeted learning experiences.10

Cognitive ﬂexibility, 在这种情况下, is the ability to see or understand a
problem from different perspectives, which facilitates both the kinds of
adaptation needed to transform a complex problem into one that can be
解决了, as well as collaboration. Strictly speaking in developmental psy-
chology cognitive ﬂexibility is an executive function that develops as the
prefrontal cortex develops and not through learning. 然而, in educa-
tional ﬁelds the term is being used broadly in relation to learning as we use
它 (看, 例如, Spiro et al. 1994). Interactional expertise is a notion intro-
duced by Harry Collins and Robert Evans (Collins and Evans 2002) 到
characterize the nature of the expertise required of sociologists doing ﬁeldwork.
It marks a division between the development of conceptual understanding
of the practices of collaborators, which enables each to engage linguistically
with the practices, and the ability to perform the practice. 柯林斯, 埃文斯,
and Michael Gorman extended the notion to interdisciplinary collaboration,
and stress, additionally, that it is also “tacit knowledge-laden and context
speciﬁc,” (柯林斯, 埃文斯, and Gorman 2007, p. 661).11 “Epistemic awareness”
is a term we introduced to capture the epistemological dimension of problem
solving (Osbeck and Nersessian 2017; Nersessian 2017b). It comprises a
metacognitive awareness of epistemic identity and epistemic values and the
role these play in the research. Epistemic awareness is the ability to reﬂect
both on the epistemic dimensions of one’s own discipline and research prac-
tices and on those of the collaborators.

These virtues are not normally emphasized in disciplinary contexts.
一般来说, the skills associated with them are not easily acquired on one’s
own. The development of a full “hybrid” curriculum in BME cultivates all
of these interdisciplinary virtues (和更多) through processes of learning
domain knowledge, 方法, 概念, and epistemic dimensions of the
participating ﬁelds, and using these in problem solving over a number

10. These are not a complete set of interdisciplinary virtues, just what we found to be the
most important for promoting effective research collaboration. 例如, resilience in the
face of impasses is another we identiﬁed, since in the kind of pioneering science we witnessed
failure and impasses are ever-present. Another is methodological versatility, having multiple
methods in one’s toolkit. 更远, although I discuss the ISB case here, we found that these
characteristics promoted good interdisciplinary research in both BME and ISB. The differences
lie in the ways in which they are cultivated given the current states and aims of the ﬁelds.

11. How to distinguish the notions of interactional and contributory expertise has
been the subject of an extensive debate in the literature that we need not consider for
our purposes (看, 例如, 安徒生 2016; Collins and Evans 2015; 柯林斯, 埃文斯, 和
Weinel 2016; Goddiksen 2014).

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of years. These researchers can be independent BME investigators, 和我们一样
saw in our studies, but also can act as boundary agents in collaborations
with experimentalists, medical researchers, and engineers who are not
BMEs. At least in the current state of ISB (and quite possibly a necessary
feature of this kind of research), the “hybrid” curriculum is not desired as
modelers and experimentalists need deep training in one discipline, i.e.
sufﬁcient to be solely a computational scientist (工程师, applied mathe-
matician) or an experimentalist (bioscientist, medical researcher). But to
realize the full potential of ISB requires some degree of penetration of each
kind of researcher into the ﬁeld of the other. At a minimum this means
modelers learn to adapt what they know to complex biological problems
across a range of areas, as well as learning to know what biological infor-
mation they need and how to seek it, and experimentalists learn to know
enough about the nature and potential of modeling biological systems to
produce the kind of data needed and to know, as one experimentalist put
它, “the right kinds of questions” to ask in furthering a collaboration. 之内
our project we experimented with some minimalist learning interventions
aimed at developing the noted characteristics early in order to mitigate
some of the struggle of collaboration.

Because the model is central in ISB problem solving, the engineers/
computational scientists are taking the lead in moving the ﬁeld forward.
As we saw, modelers do more than just feed biological data into a model
and provide predictive outcomes to experimentalists. They have to under-
stand how to search the literature to ﬁnd relevant data and build out the
biological pathway, both of which require discernment and judgment
about biological phenomena, what it is feasible to do in experimentation,
and the reliability and relevance of the data, as well as the ability to discuss
problems with experimentalists as they are developing the models. 上
other side, sophisticated biological experimentation requires equally spe-
cialized training, but to be able to collaborate effectively with modelers,
we found that experimentalists need to understand the basics about how a
model is constructed so as to, 至少, devise experiments to pro-
duce the kind of data modelers need to construct and test, experimentally,
informative models.

I can only report brieﬂy on two interventions we undertook with the
newer researchers in our labs that proved quite successful. On the model-
ing side, as we saw, modelers develop cognitive ﬂexibility not through tak-
ing numerous biology classes, but through efforts to recast phenomena
from disparate biological domains in terms of features of engineering sys-
特姆斯, especially control and regulation. What biology classes they do take
are usually more theoretical or bioinformatics classes without labs, so they
have little understanding of how biological data are produced, 哪个

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creates a major impediment to collaboration. We were told that a full se-
mester rotation in a biology research lab would take too much time away
from modeling work. We proposed an intensive “experimental summer
camp” experience for modelers, in which they spend a month in an exper-
imental lab collaborating with them engaged in a real piece of research to
learn hands-on what it takes to design and execute experiments, 也
something of the way experimentalists think about biological phenomena.
The modelers were not absolute novices to biology since they had been
conducting the literature searches and building the pathways as we dis-
cussed above. 然而, they had no idea of the complex environment of
the biological lab or sense of the nature and costs (time and ﬁnancial) 的
experimental practices through which data are collected and analyzed. 这
students described the following gains: increased self-conﬁdence, comfort
with experiments, and knowledge of experimental procedure; enhanced
ability to anticipate the needs and questions of experimentalists, to under-
stand experimentalist reasoning processes, and to evaluate experimental
文学; and new appreciation for the difﬁculties/constraints of experi-
mentation and, interestingly, for why their advisor kept telling them to
model trends, not every data point—data can be noisy.

On the experimentalist side, learning about model-building cannot be
achieved by visiting a modeling lab. Hands-on experience requires a more
structured approach. 幸运的是, the department was interested in de-
veloping a new introductory graduate course in biosystems modeling in
which students from the biological sciences would develop conceptual
understanding of modeling and basic modeling skills while working on
systems biology problems with engineering/computational students, WHO
would be learning to adapt their knowledge and skills to modeling bio-
logical systems. We worked with both lab directors through several iter-
ations of the course (Voit et al. 2012). Although several biology students
have taken the course, during our study only one was from our labs.
尤其, she had had an early, unsuccessful research collaboration with a
modeler. She described a new awareness of the affordances of models and a
new understanding of math as a ﬂexible tool for “actual real world applica-
tion.” She reported being a “changed woman,” with respect to her attitudes
about modeling after the class. 尤其, she explained that now she un-
derstood “what he needed” and not only “what questions she should have been
asking” but “what questions he should have been asking” her.

Although only a start, our ﬁndings suggest the value of speciﬁc, 时间-
limited learning experiences for increasing cognitive ﬂexibility, epistemic
意识, and interactional expertise, which contribute to more effective
collaborations, better reasoning, greater awareness of the affordances of
方法, and enhanced ability to reﬂect on one’s own perspective and that

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of the other. 总共, even small, targeted learning interventions can have
big payoffs to beneﬁt collaboration and thus problem-solving potential in
the ISB research space.

结论

5.
The goals of our project were multifaceted, but a major one was to investigate
emerging interdisciplinary cognitive and learning practices around problem-
solving in frontier research laboratories in the bioengineering sciences.
Cognitive ethnography was necessary to fathoming how interdisciplinary
problem-solving is enacted in situ. We had no hypothesis about the nature
of the inderdisciplinarity we would encounter when we entered the BME
labs. What we learned from our initial interviews, observations, and dis-
cussions with faculty constructing the ﬂedgling BME program was that: 1)
in these labs engineers were tasked with conducting basic biological re-
search through constructing living physical simulation models, part cells
and cellular materials and part engineered materials, 和 2) faculty wanted
to build an educational program that would require students to “integrate”
all three dimensions of BME from the outset. 部分 4.1 discussed brieﬂy
how hybridization is achieved through the conceptual, methodological,
and material integration required to design and construct physical model-
systems and conduct experiments by simulating selected biological pro-
过程. Based on our analyses we argued that this form of interdisciplinary
problem-solving requires learning to build interlocking models along
many dimensions. We participated in developing a novel curriculum to
foster this kind of problem solving.

We began our investigation of the ISB laboratories with preliminary
research for preparing the grant proposal, which led to a hypothesis that
problem solving in these labs could not be characterized as hybridization of
the sort in BME. In ISB we needed to investigate the implications of im-
porting systems engineering concepts and high-level computational and
mathematical methods developed for other purposes into the biological sci-
恩塞斯. We found that the nature of the systems-level problems formulated
in the emerging ISB ﬁeld creates an epistemic interdependence among the
collaborating members of the ﬁelds: 工程, applied mathematics,
and biosciences. Problem-solving requires both collaboration and that at
least some participants operate beyond their disciplinary practices and
within those of another participating ﬁeld with little training to do either
in the present state. 部分 4.2 discussed brieﬂy how it falls on the mod-
eler to manage the complexity of the problem-solving process, 包括
determining how to adapt engineering concepts and methods to biological
problems and how to ﬁnd the biological data necessary to build, simulate,
and test their models, as well as ﬁnding ways to help experimentalists

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understand their models, which at this point in time are seen as “black
盒子,” and understand the data needs for building and testing them.
部分 4.3 discussed the chief interdisciplinary virtues we determined
from our investigation to be required for effective collaboration and our
minimalist strategies for fostering more effective collaboration in ISB.

Our cognitive ethnographic research establishes that a method that was
pioneered for examining cognitive practices in areas where problem-solving
tasks and goals are largely well-deﬁned can be extended fruitfully to the
open-ended problem-solving environments of emerging interdisciplinary
sciences and engineering. 的确, cognitive ethnography provides the pri-
mary means for developing ﬁne-structured analyses of varieties of interdisci-
plinary as they are enacted in real-world, real-time situations of practice. 它
provides a unique level of granularity for understanding the nature and chal-
lenges of these exploratory, incremental, and nonlinear problem-solving
实践, their development, and the epistemic principles guiding them.
Insights gleaned from intensive case studies can be used to develop strategies
for facilitating speciﬁc varieties of interdisciplinary learning, 一体化,
and collaboration. Findings from cognitive ethnography can also be used
to enrich and validate ﬁndings from more global methods of studying inter-
disciplinary such as bibliometric analyses of patterns of interaction and in-
ﬂuence (看, 例如, Roessner et al. 2013).

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