Tracking the emergence of synthetic biology
Tracking the emergence of synthetic biology
Philip Shapira 0 1 2 3 4
JEL Classification I 0 1 2 3 4
0 School of Public Policy, Georgia Institute of Technology , Atlanta, GA 30332-0345 , USA
1 Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester , Manchester M13 9PL , UK
2 & Philip Shapira
3 Enterprise Innovation Institute, Georgia Institute of Technology , Atlanta, GA 30308 , USA
4 Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals, Manchester Institute of Biotechnology, University of Manchester , Manchester M1 7DN , UK
Synthetic biology is an emerging domain that combines biological and engineering concepts and which has seen rapid growth in research, innovation, and policy interest in recent years. This paper contributes to efforts to delineate this emerging domain by presenting a newly constructed bibliometric definition of synthetic biology. Our approach is dimensioned from a core set of papers in synthetic biology, using procedures to obtain benchmark synthetic biology publication records, extract keywords from these benchmark records, and refine the keywords, supplemented with articles published in dedicated synthetic biology journals. We compare our search strategy with other recent bibliometric approaches to define synthetic biology, using a common source of publication data for the period from 2000 to 2015. The paper details the rapid growth and international spread of research in synthetic biology in recent years, demonstrates that diverse research disciplines are contributing to the multidisciplinary development of synthetic biology research, and visualizes this by profiling synthetic biology research on the map of science. We further show the roles of a relatively concentrated set of research sponsors in funding the growth and trajectories of synthetic biology. In addition to discussing these analyses, the paper notes limitations and suggests lines for further work.
Emerging technology; Synthetic biology strategy; Map of science; Research sponsors
Synthetic biology is, according to a National Academy of Sciences (2013, 2) report of
working parties from the US, the UK, and China, ‘‘an emerging discipline that combines
both scientific and engineering approaches to the study and manipulation of biology.’’
Similar descriptions have been put forward by other commissions and studies. For
example, a joint opinion by three scientific committees of the European Commission
(Breitling et al. 2015)
emphasizes the role of design and engineering approaches by stating
that synthetic biology is ‘‘the application of science, technology and engineering to
facilitate and accelerate the design, manufacture and/or modification of genetic materials in
living organisms.’’ A report of the Secretariat of the Convention on Biological Diversity
(2015) suggests that while there is no agreed international definition, the key features of
synthetic biology include ‘‘the de novo synthesis of genetic material and an
engineeringbased approach to develop components, organisms and products.’’
Proponents of synthetic biology suggest that its capabilities to design and redesign
biological components and systems will address global food and energy challenges, propel
industrial transformation as sustainable bio-engineered processes replace current
petrochemical technologies, and offer new gene-based methods to target human medical
conditions and insect-borne diseases
(Church and Regis 2012; Weber and Fussenegger 2012;
National Academies of Science 2013; Le Feuvre et al. 2016)
. The growth of synthetic
biology has been boosted by a series of scientific and technological developments. These
include improvements in DNA synthesis (longer fragments and higher accuracy), reduced
DNA synthesis and sequencing costs, new capabilities not only to read but also to edit and
rewrite the genes and cells of organisms, advances in bio-engineering design and modeling
techniques, enhanced tools for biological assembly and engineering, the development of
standardized biological parts, and the use of automated and data-intensive methods to
speed up discovery and testing
(Canton et al. 2008; Cheng and Lu 2012; Keasling 2012;
Church et al. 2014; Lienert et al. 2014; Breitling and Takano 2015; Shih and Moraes 2016)
The spread of synthetic biology has also been accelerated by targeted research programs
and public policies, including funding by multiple federal agencies in the US
Center 2015; Si and Zhao 2016)
, by the UK’s network of synthetic biology research centers
and its national synthetic biology roadmap
(UK Synthetic Biology Roadmap Coordination
Group 2012; Synthetic Biology Leadership Council 2016)
, by European Union projects
(ERASynBio 2014), and by growing support in China (Synbiobeta 2016). Increased
synthetic biology R&D investment and intellectual property acquisition by leading private
sector companies in pharmaceutical, agricultural, chemical, and other sectors
Carbonell et al. 2016)
, new business start-ups with ambitious goals such as cow-free milk
or open-source insulin
(Qiu 2014; Tucker 2015)
, community-based bio-hacking labs
, and the iGEM international synthetic biology competition
et al. 2015)
have also contributed to the emergence of the domain. At the same time,
ethical, risk, equity, and other policy concerns and have been raised about the potential
implications of applications of synthetic biology
(Tucker and Zilinskas 2006; ETC Group
2010; OECD 2014; Engelhard 2016)
. These concerns have highlighted attention to the
importance of responsible research and innovation in synthetic biology
Stemerding 2013; Li et al. 2015; Shapira and Go¨ k 2015)
In this context of rapid scientific advancement, increased public and private R&D, and
stakeholder debate about the regulation and governance of synthetic biology, methods that
can track the growth of research and innovation in synthetic biology are essential to inform
engagement, policy deliberation, and management, and to provide evidence for
decisionmaking. While there is a degree of high-level expert convergence on the conceptualization
of synthetic biology, there are blurry boundaries between the technology in question,
legacy technologies, and other new technologies that might be related to it
Biotechnology 2009; Thomas 2014)
. There are epistemic debates about the distinctions
between synthetic biology, systems biology, and genetic engineering
(O’Malley et al.
2007; Calvert 2008)
. Synthetic biology has a legacy that extends back to the human
genome project of the 1990s and early 2000s
(Shapira et al. 2015)
and earlier advances in
understanding genes. At the same time, synthetic biology has relationships with advances
in other disciplines, including engineering, biochemistry, agriculture, and informatics.
Recent online discussions, hosted by the Biosafety Clearing House under the Convention
on Biological Diversity (2015), demonstrate a range of perspectives from different
countries and various stakeholders, about an operational definition of synthetic biology.
This paper puts forward a bibliometric approach to delineating synthetic biology. We
recognize the broad notion that synthetic biology involves the design and engineering of
biological components and systems at the genetic level. We also acknowledge that there is
significant debate about details that affect the operationalization of a bibliometric definition
of synthetic biology. We thus tread carefully through these debates, realizing that they are
not yet resolved, to put forth a pragmatic strategy for creating a bibliometric definition of
synthetic biology. There is relatively little work so far available on the bibliometric
definition of synthetic biology, and our review of several of the definitions published to date
finds them either too narrow or too expansive. We seek to contribute by refining an
approach that better captures the complex scope of synthetic biology. We employ a
multistage method, drawing from two publication indices (Web of Science and PubMed). The
approach is used to identify scientific papers published in the synthetic biology domain and
to trace patterns of emergence including international spread, funding, and disciplinary
The next section describes our search strategy and the steps and procedures involved.
This is followed by a comparison of our results with those of other recent bibliometric
definitions of synthetic biology and by an analysis of patterns of synthetic biology
emergence indicated by the synthetic biology publications captured by our approach. The
last part of the paper discusses the analysis and its limitations, draws conclusions and
suggests lines for further work.
Synthetic biology bibliometric search approach
Bibliometric approaches that quantitatively analyze publication, patent, and other research
output indicators are commonly applied to comprehend the scale and direction of research
and innovation in emerging domains of science and technology
(Moed et al. 2004; Small
et al. 2014)
. Emergent domains are intrinsically characterized by ambiguity about the
sources and nature of domain novelty, contestations with established fields, uncertainty
about coherence and growth trajectories, and resultant blurriness in domain delineation
(Cozzens et al. 2010; Small et al. 2014; Zhang et al. 2014, 2016; Rotolo et al. 2015)
Definitional problems are compounded where, as is often the case, emerging domains of
science and technology span or arise from multiple disciplinary fields (Wagner, et al.
2011). Various bibliometric methods have been put forward to address the challenges of
defining emergent domains, with each method having its own set of strengths and
weaknesses. Indexed-based methods (using categories already defined by publication
databases) promise simplicity but typically lag the emergence of new domains of science
and technology (Cozzens et al. 2010). Methods based on expert-defined key words
et al. 2008; Kuzhabekova and Kuzma 2014)
or semi-automated searches with expert
review or other tests for relevance
(Mogoutov and Kahane 2007; Oldham et al. 2014)
relatively straightforward to carry out but rely on agreement among the experts and on
adeptness in defining and reviewing search terms and their results. Citation or co-citation
approaches, for example those that identify an agreed corpus of publications at the core of
an emergent domain and then capture citations to that corpus
(Zitt and Bassecoulard 2006)
add in an element of peer network expertise (those researchers who reference the core and
one another). However, such methods are hard to replicate (requiring access to full citation
databases) and there are caveats about the interpretation of co-citations. A further method
is to identify a set of journals dedicated to a domain, including through use of measures of
core journal association
(Leydesdorff and Zhou 2007)
. This method has limitations in a
new domain that is interdisciplinary and where publications appear in many different
journals, including disciplinary and multidisciplinary journals, alongside out-of-domain
(Huang et al. 2010)
In addressing the bibliometric challenges of defining emerging technology domains, it has
been observed that elements of these methods can effectively be combined
Wen et al. 2017)
as well as reinforced by adding contingent and iterative features
(Arora et al.
2013; Huang et al. 2015)
. The approach put forward in this paper is thus one of pragmatic
refinement, where we draw on insights from multiple methods to develop an approach to
defining synthetic biology that combines a reasoned strategy, insights from existing searches,
and replicability. The search approach that results should not be viewed as drawing a sharp
boundary around the domain: indeed, the embryonic and interdisciplinary nature of synthetic
biology means that there will be porosity in any delineation. With this in mind, the search
strategy is presented as a public tool so that searches can readily be replicated, updated,
modified, and refined by others, and to facilitate subsequent search term enhancements as the
synthetic biology domain evolves
(for an example of a similar evolution in the bibliometric
search strategy for another emerging technology, see Arora et al. 2013)
Our technique for developing a bibliometric definition of synthetic biology starts with a
corpus of synthetic biology benchmark papers from which we build out procedures to
capture other papers that can be considered for incorporation into the synthetic biology
domain. We aim to include papers that are clearly acknowledged as synthetic biology as
well as papers that should be included as part of the synthetic biology domain, even though
they may not explicitly use ‘‘synthetic biology’’ in their title, abstract or key words. We
also seek to exclude papers that are in related or other fields but which are not using the
concepts, methods, or sources that are associated with synthetic biology. A four-step
procedure is pursued (see Table 1). First, we gather a set of benchmark synthetic biology
publication records. Second, we extract additional keywords from the abstracts of these
benchmark record abstract by using Natural Language Processing (NLP). Third, we test
and refine these keywords, and also delineate exclusion terms. Finally, we include papers
published in dedicated synthetic biology outlets. These steps are detailed below.
Retrieving synthetic biology benchmark records
Our search strategy begins by identifying synthetic biology benchmark publication records
from PubMed, provided by the National Center for Biotechnology Information (http://
www.ncbi.nlm.nih.gov/pubmed). PubMed has a set of curated publications that are expert
classified as synthetic biology in the Medical Subject Heading (MeSH) terms. MeSH terms
provide a controlled vocabulary that has been developed to enable more accurate searching
of articles within PubMed. We used the MeSH major topic = ‘‘synthetic biology’’ to
extract synthetic biology related papers and also to test various keywords. Our extraction of
papers using the synthetic biology MeSH topic excludes records that have non-technical
qualifiers such as trend, economy, or education. We used records published from 2011 to
2014 because the MeSH term for synthetic biology was not available until 2011. The
benchmark set of PubMed synthetic biology publication records comprised 401 valid
Adding keywords from keyword (co-) occurrence analysis and prior studies
In addition to using the PubMed extracted records in our synthetic biology corpus, we
generated and tested further keywords for application to other databases (such as the Web
of Science). We initiated this process by extracting candidate keywords from abstracts in
the PubMed synthetic biology records. We employed a natural language processing (NLP)
service for keyword extraction. The service we used was AlchemyAPI, which was acquired
by IBM in 2015, hence we denote as Alchemy-IBM NLP. (This service has subsequently
been incorporated into the IBM Watson Developer Cloud, see: https://www.ibm.com/
watson/developercloud/natural-language-understanding.html.) Alchemy-IBM NLP uses a
deep learning technology for text analysis including keyword extraction. Uses of this text
analysis function include studies of movie review comments and social media responses to
(Singh et al. 2013; Simon et al. 2014)
. A study by
Jean-Louis et al.
showed that the Alchemy-IBM NLP outperforms other commercial keyword
extractors in terms of precision and recall. For keyword extraction, Alchemy-IBM NLP
provides a relevance score that estimates the relationship of a keyword to the context of
input text. The relevance score is generated from an algorithm that combines information
such as the location of words in the given text, other words around the keyword of interest,
and keyword frequency. The score ranges from 0 for ‘‘unrelated’’ to 1 for ‘‘absolutely
related.’’ We tested results from varying the threshold of the keyword relevance score,
including manually checking the publication records associated with potential keywords.
For a relevance threshold below 0.5, there were 216 potential keywords. After checking,
keywords with relevance scores less than 0.5 were dropped because these were judged to
bring in too many unrelated publication records. Additionally, keywords having only one
occurrence were eliminated, the top 10% frequency keywords were retained, and the
remaining keywords were combined and assessed according to their keyword
co-occurrence pattern. This round of refinement yielded 94 keywords. Then, we added for further
testing 99 keywords specified in the synthetic biology definitional paper by van Doren et al.
(2013) and three keywords specified in the synthetic biology definitional paper by
et al. (2012)
. The total pool of candidate keywords was then subject to additional
processing as discussed below.
Keyword screening using noise ratio testing and face validity review
Testing of the pool of candidate keywords was based on a comparison of the benchmark
synthetic biology papers from PubMed and 1000 randomly downloaded PubMed
publications published in a comparable period to the benchmark records. Using these records,
two measures were calculated, HitRatiob and HitRatior, respectively for benchmark
publications versus random publications. For each keyword k, we estimated the share of
benchmark papers (HitRatiob,k) and of randomly selected papers (HitRatior,k) identified by
searching using this keyword. The noise ratio of keyword k (NRk) was then defined as
(HitRatior,k/HitRatiob,k). Keywords with low NRk ratios are more effective than those with
higher NRk ratios in distinguishing benchmark records from random records. In a face
validity test, we screened all keywords and manually checked identified publication
records. This led us to drop 117 keywords where NRk was higher than 0.1 (10% threshold).
This judgment was founded on a determination that keywords above this threshold brought
in too many records that were extraneous to synthetic biology. We also tested specific
exclusion terms. We pursued an approach similar to that adopted by
Porter et al. (2008)
formulate exclusion terms that would remove extraneous records associated with selected
keywords. For example, in connection with the keyword ‘‘synthetic cell’’ we added
exclusion terms such as ‘‘cell* phone’’ and ‘‘battery cell*’’ to avoid capturing records
association with communications technology and electronics. We added terms related to
‘‘BioBricks’’ (sets of biological parts used to engineer biological devices and systems). We
tested the term ‘‘iGEM’’—the International Genetically Engineered Machine synthetic
biology competition. However, we found this brought in too many extraneous records
because of similar acronyms or initial characters in a range of fields. The result from these
tests was a set of 21 candidate keyword terms. We excluded six of these keyword terms as
they were found to add no additional publication records. These processes resulted in 15
keyword terms (14 extracted terms plus terms related to ‘‘BioBricks’’) and 18 associated
exclusion terms used in various combinations. Table 2 presents this keyword list,
summarizes the noise test results, indicates keyword sources, and lists specific exclusion terms.
Of the final set of terms, 14 present with a zero noise ratio, while ‘‘synthetic biolog*’’ (with
the exclusion of ‘‘photosynthe*’’) has a very low (0.001) noise ratio. We then transformed
the keywords and exclusion terms into a consolidated search approach (Table 3). This
search approach includes, but goes beyond, approaches that define synthetic biology just by
that term alone or by reference to standardized parts.
VD = suggested by van Doren et al. (2013), OD = suggested by
Oldham et al. (2012)
, New = newly
added keywords, Expert = suggested by expert, Comm. = common keywords shared by VD, OD, and New
Synthetic biology journal and special issue inclusions
In a further refinement step, we also included papers published in curated synthetic biology
journals and special synthetic biology issues of general journals (see Table 3). We added
this step to ensure the inclusion of papers that editors have determined are within the
synthetic biology domain. This set included PLoS ONE curated synthetic biology articles
from http://collections.plos.org/s/synbio; this source yielded 185 publication records. We
Search strategy—synthetic biology inclusion and exclusion terms
(((TS = (‘‘synthetic biolog*’’ OR ‘‘synthetic dna’’ OR ‘‘synthetic genom*’’ OR ‘‘synthetic *nucleotide’’ OR
‘‘synthetic promoter’’ OR ‘‘synthetic gene* cluster’’) NOT TS = (‘‘photosynthe*’’)) OR
(TS = (‘‘synthetic mammalian gene*’’ AND ‘‘mammalian cell’’) NOT TS = ‘‘photosynthe*’’) OR
(TS = ‘‘synthetic gene*’’ NOT TS = (‘‘synthetic gener*’’ OR ‘‘photosynthe*’’)) OR (TS = (‘‘artificial
gene* network’’ OR (‘‘artificial gene* circuit*’’ AND ‘‘biological system’’)) NOT TS = ‘‘gener*’’) OR
(TS = (‘‘artificial cell’’) NOT TS = (‘‘cell* telephone’’ OR ‘‘cell* phone’’ OR ‘‘cell* culture’’ OR ‘‘logic
cell*’’ or ‘‘fuel cell*’’ or ‘‘battery cell*’’ or ‘‘load-cell*’’ or ‘‘geo-synthetic cell*’’ or ‘‘memory cell*’’ or
‘‘cellular network’’ or ‘‘ram cell*’’ or ‘‘rom cell*’’ or ‘‘maximum cell*’’ OR ‘‘electrochemical cell*’’ OR
‘‘solar cell*’’)) OR (TS = (‘‘synthetic cell’’) NOT TS = (‘‘cell* telephone’’ OR ‘‘cell* phone’’ OR ‘‘cell*
culture’’ OR ‘‘logic cell*’’ or ‘‘fuel cell*’’ or ‘‘battery cell*’’ or ‘‘load-cell*’’ or ‘‘geo-synthetic cell*’’ or
‘‘memory cell*’’ or ‘‘cellular network’’ or ‘‘ram cell*’’ or ‘‘rom cell*’’ or ‘‘maximum cell*’’ OR
‘‘electrochemical cell*’’ OR ‘‘solar cell*’’ OR ‘‘photosynthe*’’)) OR (TS = (‘‘artificial nucleic acid*’’ OR
‘‘artificial *nucleotide’’)) OR (TS = (‘‘bio brick’’ or ‘‘biobrick’’ or ‘‘bio-brick’’))))
This definition is applied directly into the advanced search feature of the Web of Science. It does not
incorporate the additional synthetic biology journal search strategies described in the paper (see also
added all publication records from the synthetic biology specialty journal ACS Synthetic
Biology. We also added publication records in special issues of general journals with a
focus on synthetic biology, including Trends in Biotechnology 33(2), ACM Journal of
Emerging Technologies in Computing Systems 11(3), Biochimica et BioPhusica Acta—
Gene Regulatory Mechanisms 1839(10), Biochimica et Biophysica Acta—Bioenergetics
1837(9), Natural Computing 12(4), Chemical Engineering Science 103, FEBS Letters
586(15), and Acta Biotheoretica 58(4). These journals and special issues yielded 558
records (Table 4).
Web of Science search and data cleaning
We applied the consolidated search approach (including exclusion terms) and the journal
search strategy to publications recorded in the Web of Science (WoS) for the period
2000–2015 in Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation
Index (SSCI), Conference Proceedings Citation Index-Science (CPCI-S), and Conference
Proceedings Citation Index-Social Science and Humanities (CCPI-SSH). The gross
worldwide number of records obtained by applying this search strategy
(in May 2016)
8412. VantagePoint text mining software was then used for record cleaning. This software
is customized to enable the data cleaning, text mining, and analysis of bibliometric
publication records (https://www.thevantagepoint.com/). After removing duplicate records,
including early conference papers subsequently published with the same title, abstract and
authors as articles, our synthetic biology publication dataset comprised 8064 publication
Comparison with other bibliometric definitions of synthetic biology
As indicated in the introduction, there is debate about the operational definition of
synthetic biology, i.e. what should be included (and excluded). Given our focus on the
bibliometric definition of synthetic biology, we assessed our search strategy against three
other comparable studies. The first of these studies, by
Oldham et al. (2012)
PLOSONE curated synthetic biology articles from http://collections.plos.org/s/synbio
ACS Synthetic Biology
Trends in Biotechnology volume 33(2)
ACM Journal on Emerging Technologies in Computing Systems volume 11(3)
Biochimica et Biophysica Acta-Gene Regulatory Mechanisms volume 1839(10)
Biochimica et Biophysica Acta-Bioenergetics volume 1837(9)
Natural Computing volume 12(4)
Chemical Engineering Science volume 103
FEBS Letters volume 586(15)
Acta Biotheoretica volume 58(4)
Where applicable, journal issue number is in parenthesis
acknowledging the complexity of defining synthetic biology, explicitly eschewed a
multifaceted approach, choosing instead to use a simple and restricted search strategy. This
restricted strategy used only four terms to bibliometrically define synthetic biology:
‘‘synthetic biology’’, ‘‘synthetic genomics’’, ‘‘synthetic genome’’, and ‘‘synthetic
genomes’’. A subsequent study, by
Raimbault et al. (2016)
identified a set of core articles using
a topic search for synthetic biology, examined relevant keywords, and using expert review
added 11 additional search terms. This approach added refinements to the Oldham et al.
strategy, for example by adding terms such as ‘‘synthetic gene network’’ or ‘‘synthetic gene
circuits.’’ Raimbault et al. also added several other terms, including ‘‘standard biological
parts’’ and ‘‘minimal cells’’.
In a third study,
Hu and Rousseau (2015)
, from WoS articles using the term ‘‘synthetic
biology’’, extracted two types of keywords, analyzed the frequencies of those key words,
correlated the most common terms with MeSH subject definitions and Wikipedia
descriptions, and verified their list of most-used terms with a field expert. The outcome
added a further 23 search terms to the core term of ‘‘synthetic biology’’. There is
commonality between all three definitions in the use of term variations related to ‘‘synthetic
gene*’’ and ‘‘synthetic genomics.’’ Hu and Rousseau’s insertion of ‘‘biobrick*’’ also has a
correspondence (see Shetty et al. 2008) with the inclusion of ‘‘standard biological parts’’ by
Raimbault and colleagues. However, there are differences. For example, Hu and Rousseau
explicitly include ‘‘DNA nanotechnology’’—a topic that tends to focus more on DNA’s
physical and chemical features than on its genetic aspects
. Hu and Rousseau
also include the terms ‘‘protein engineering’’ and ‘‘metabolic engineering’’. Research in
these two established fields has typically been oriented towards enhancing existing
biological mechanisms, for instance to develop proteins and enzymes, while synthetic biology
arguably has an orientation to adding new biological components to make mechanisms
work differently. While there are emerging overlaps between these fields and synthetic
(Jungmann et al. 2008; Keasling 2012; Li 2012; Yadav et al. 2012)
, the specific
inclusion of these terms (and others, such as ‘‘protein design’’) means that Hu and
Rousseau’s bibliometric definition of synthetic biology extends rather broadly.
The three comparison synthetic biology search strategies discussed here, plus our own,
each use the WoS as the source of publication records. However, different publication
periods and end dates are used and there are also variations in the WoS databases searched
and types of publications extracted. Hence, to compare these studies, we applied the
respective bibliometric search strategies to identify articles published in WoS
SCI-Expanded and SSCI between the years 2000 and 2015. The search results were organized and
cleaned in VantagePoint, using common methods (for example, to eliminate duplicates and
to ascertain keywords). This standardized comparison yielded significant differences in the
number of records captured by each search (see Table 5). The ‘‘minimalist’’ approach of
Oldham and colleagues to defining synthetic biology returned nearly 2400 articles. The
synthetic biology search strategy of Raimbault and colleagues garnered only 380 more
articles, notwithstanding the additional keywords added. In contrast, Hu and Rousseau’s
broad definition returned a much larger set comprising over 18,100 articles. The synthetic
biology search strategy that we put forward in this paper (see prior section) yielded about
For a systematic comparison of the four approaches, we profiled the top author
keywords for the articles returned by each definition. Overall, our definition has a profile for
the highest ranked author keywords comparable to that found in the Oldham and Raimbault
definitions. ‘‘Synthetic biology’’—the most frequent keyword captured by all three
definitions—was about 6.5 times more likely to appear than the next term, which was
metabolic engineering. ‘‘System biology’’ was third ranked in the Oldham and Raimbault
definitions, with ‘‘Synthetic gene’’ and ‘‘Escherichia coli’’ ranked above ‘‘System biology’’
in our definition. The search strategy that we put forward yields more articles than the two
other definitions, yet still returns records that capture features of synthetic biology. The Hu
and Rousseau definition yields a noticeably different ordering of top keywords. In their
definition, ‘‘Engineered proteins’’ is the most frequently keyword, appearing about 1.9
times more frequently than third-ranked ‘‘Synthetic biology.’’ ‘‘Metabolic engineering’’
ranks second in Hu and Rousseau, appearing 1.5 times more frequently than ‘‘Synthetic
biology.’’ Other keywords containing the term ‘‘Protein’’ appear more than in the other
three definitions. Also, the Hu and Rousseau definition yields a rather longer tail
distribution of the keywords than the three other definitions (including our definition).
As with other emerging technologies where boundaries are blurry, there is not
necessarily a single way to bibliometrically define synthetic biology. Each method has its own
mix of strengths and weaknesses. The Oldham ‘‘minimalist’’ definition, with the use of just
four search terms, appears to have relatively high precision in terms of the limited set of
papers retrieved. However, Oldham also performs weakly in terms of recall in that it fails
to capture synthetic biology papers whose authors do not explicitly use the term ‘‘synthetic
biology’’ or refer to ‘‘synthetic genome’’ or one of its variations. While Oldham and
colleagues succeed in avoiding false positives, arguably they significantly undercount the
breadth and scope of the synthetic biology domain. Raimbault et al. seek to address the
recall problem by introducing additional keywords, but those added terms lead to only a
small recall increment in part because most of the new terms are modest variants on
Oldham’s. Conversely, the Hu and Rousseau strategy captures a much wider range of
topics. Recall is significantly increased, but at the expense of precision as this definition
raises concerns that papers are being captured that are more related to general or
predecessor biological concepts rather than to topics that are more directly connected with the
contemporary thrust of synthetic biology. The analysis and comparison indicates that our
definition is focused on the synthetic biology domain while capturing more features of
synthetic biology than ‘‘minimalist’’ definitions. This is not a ‘‘golden mean’’ compromise,
i.e. we do not arbitrarily split the difference between two boundary positions. Rather, we
derive our definition through a process that builds on the terms and associations evident
among those who publish and curate in the synthetic biology domain as well as reviewing
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terms suggested by those who have previously formulated bibliometric synthetic biology
field definitions. The process considers candidate keywords identified in recognized
synthetic biology publications, through co-occurrence analysis, and in prior studies; screens
and tests keywords before acceptance, incorporating exclusion terms; and then merges
externally curated publication records. While this search process identified a significant
number of potential new keywords, the subsequent screening and exclusion process
ensured that only those new terms that measured up well in terms of precision were
retained. Recall and precision also received reinforcement by the hybrid addition of
curated synthetic biology journals and collections. Overall, the method put forward in the
paper has reasonable recall and precision, avoiding the potential under- and over-counts of
other available approaches: this is important in appropriately assessing the scale and scope
of the emergent synthetic biology domain—the topic that we now turn to in the next part of
Analysis of results
The purpose of our synthetic biology bibliometric definition and search approach is to
provide a tool that can be used to track developments and patterns in this research domain.
In this section, we present the results of analyses using the publications dataset derived
from the search. We look at the growth of synthetic biology publications over time and by
leading countries and explore international author collaborations. We then explore the
scientific disciplines that underpin the emergence of synthetic biology, including analysis
of the profile of synthetic biology on the map of science. Finally, we examine the
emergence of research sponsorship in the development of synthetic biology and investigate
insights provided by funding acknowledgements information.
Synthetic biology publications and collaborations by author countries
The synthetic biology search strategy that we presented in the prior section finds that
annual worldwide synthetic biology publication output grew from an average of about 170
publications per year from 2000 to 2005 to well over 1200 publications in 2015. Here, we
use the full cleaned dataset of WoS publication records, including articles, reviews,
editorials, proceedings, conference papers, and book chapters. Synthetic biology publication
output noticeably accelerated from 2008 (Fig. 1). Our synthetic biology publication dataset
includes publications by more than 25,000 authors at 3700 organizations located in 79
countries. Yet, while researchers from many countries are now involved in publishing work
on synthetic biology, it is evident that the bulk of research output derives from a sub-set of
countries. The US is the leading country by number of synthetic biology publications, with
US-based authors contributing to 42.4% of identified worldwide 2000–2015 publications.
Authors based in the UK contributed to 10.1% of synthetic biology publications over the
same period, followed by Germany (8.9%), China (7.0%) and Japan (6.8%). (Percentage
totals calculated by authors add up to more than 100% if all countries are included, as some
publications have authors from two or more countries.) Publications with US, UK,
German, and Japanese authors more than tripled from 2008 to 2015 while publications with at
least one author based in China grew by almost a factor of eight over this period (Fig. 1).
The rapid rise of China, joining US, European and Japanese researchers engaged in
synthetic biology research, is notable. This reflects the massive expansion of research
Germany USA 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
investment, scientific human capital, and publication output in China in recent years
(Bound et al. 2013; Zhou 2015)
and its policy focus to develop leading-edge capabilities in
emerging fields, including in synthetic biology
(Pei et al. 2011; National Academy of
Sciences 2013, 17–19; Li and Shapira 2015)
While we observe the growth of national synthetic biology research publication outputs
in key countries, it is also apparent that the performance of science intrinsically crosses
(Leydesdorff et al. 2013)
. This is evident in the international
coauthorship patterns associated with synthetic biology research. However, there are
variations in international co-authorship patterns. For the US, 29% of synthetic biology
publications with US authors also have authors from other countries, while for China and
Japan the comparable percentages are 36 and 27% respectively. International co-authorship
levels are higher for European authors, with 55% of UK synthetic biology publications
coauthored with researchers from other countries, while for Germany the corresponding
percentage is 61%. For US authors of synthetic biology publications, international
coauthors are most commonly located in the UK, China and Canada (with authors from each
of these countries co-authoring more than 100 papers with US counterparts). UK
international synthetic biology co-authorships are most frequently with US colleagues, then
with researchers from Germany, Italy, Canada, and France. German international synthetic
biology co-authorships are also most commonly with the US, followed by Switzerland, the
UK, the Netherlands and France. Chinese international synthetic biology co-authorships
are dominated by collaborations with the US, with Japan and the UK representing
somewhat fewer international co-authorships. We depict these and other collaboration
patterns among countries through an international co-author network visualization (Fig. 2)
where node size is proportional to country output and linkage thickness is proportional to
the number of co-authorships between country pairs. The major countries that produce
synthetic biology publications can be clustered into three groups: US–UK–Japan–Canada–
China (colored in yellow); Germany–France–Switzerland (green); and Australia and
Finland (blue). Articles authored by researchers in these three clusters comprise 81% of our
global synthetic biology publication dataset.
Scientific disciplines of synthetic biology
Synthetic biology is frequently described as an interdisciplinary research domain with
contributions from biology, engineering, chemistry, computer science and other disciplines
(National Academy of Sciences 2010; Cheng and Lu 2012)
. Yet, as broached in the
opening part of the paper, there are also debates about the fields and specialties that
underpin synthetic biology. Such discussions are important not only for definitional
purposes but also because they suggest different trajectories for the emergence of synthetic
biology (Vincent 2013).
To offer further insights on the nature of the disciplines that are contributing to synthetic
biology, we draw on our synthetic biology publications dataset to analyze the subject
categories associated with these records. Each publication is assigned to at least one of the
more than 250 subject categories designated by the Web of Science based on citation
patterns and judgement. The assignment of subject categories is based on the source
journal or book where the publication is placed. One or more subject categories can be
allocated: of the more than 1800 publication sources in our synthetic biology dataset, 44%
are assigned one subject category, 36% have two subject categories, 18% have three or
four subject categories, and a handful (about 1%) have five or more subject categories. The
assignment of subject categories to a publication record does not necessarily represent the
research disciplines of all the individual authors. However, when aggregated for many
publications, the distribution of subject categories, and changes in the distribution of time,
can signal broad research field trends. For example, analysis of our synthetic biology
publications dataset indicates that the publications in the domain were distributed over
about 60 subject categories in the first part of the 2000s. While there is change in WoS
subject categories from time to time, and new journals enter with different subject category
distributions, it is noticeable that by 2015, publications in the synthetic biology domain
were distributed across 109 subject categories, suggesting disciplinary dispersion.
Yet, while there is a broad spread, it is also apparent that the bulk of synthetic biology
publications are assigned to sources within a rather smaller number of subject categories.
An analysis of the top ten WoS subject categories in the synthetic biology publication
dataset shows the dominance of three categories: ‘‘biochemistry and molecular biology’’,
‘‘biotechnology and applied microbiology’’, and ‘‘biochemical research methods’’
(Table 6). Since 2010, publications assigned to ‘‘biochemical research methods’’ have
more than doubled in their share, with a major contributor to growth in this category being
the journal ACS Synthetic Biology (430 records) which first started publishing in 2012. In
the category multi-disciplinary science, a boost is given by the entry of PlosOne, where the
first publications in our dataset are recorded in 2006. We note that while the established
biology fields of ‘‘cell biology,’’ ‘‘microbiology,’’ and ‘‘genetics and heredity’’ are in the
top ten subject categories, they take a smaller position in the composition of synthetic
biology research fields. ‘‘Mathematical and computational biology’’ increased its role from
the first part to the second part of the 2000s, but then saw a more recent downward trend in
share, possibly because synthetic biology papers with mathematical modeling and
computer simulation can now also find outlets in multidisciplinary and other research methods
In an extension of the analysis, we layer the synthetic biology publication dataset onto a
base map of science
(Porter and Rafols 2009; Rafols et al. 2010)
. The base map is a
representation of the underlying structure of science, based on the analysis of co-citation
patterns among subject categories of all WoS-indexed journals. We draw on the enhanced
overlay science base map and the clustering of subject categories into 18 macro-disciplines
constructed with 2015 WoS journal data by Carley et al. (2016) and visualized with
VOSviewer (van Eck and Waltman 2016). The VOSviewer clustering method and
algorithm is used (van Eck and Waltman 2010). The map visualizes the distance and intensity
of co-citations between corresponding subject categories. Each node represents individual
WoS subject categories, with colors used to depict subject categories within the same
macro-discipline group. The subject categories that are assigned to synthetic biology
articles in our publication dataset are matched to the 18 macro-disciplines and displayed on
the base map (Fig. 3). We use articles
(published 2000–2015, N = 5806)
in the synthetic
biology publication dataset as this matches the journal article and subject category analysis
method used by Carley and colleagues. In our array of synthetic biology research on the
map of science, the node size is proportional to the number of articles in that subject
category. While synthetic biology spreads broadly across the macro-disciplines and subject
categories in natural sciences and engineering, with representation in the humanities and
social science, we observe concentrations in three clusters. The largest cluster
papers for the 2000 to 2015 period)
is ‘‘biochemistry, and molecular and cell biology.’’
This is followed by ‘‘chemistry’’ (which includes the ‘‘biochemical research methods’’
category) and ‘‘biotechnology’’ (which includes ‘‘plant sciences’’). A parallel analysis of
publications in the leading macro-disciplines of synthetic biology over time (see Table 7)
Table 6 Top 10 Web of Science subject categories, synthetic biology publications. Source: Synthetic
biology publications, 2000–2015 (N = 8064)
Total number of Web of Science subject categories in synthetic biology publications = 178. Percentages for
all subject categories sum to more than 100% as publications may be associated with more than one journal
or book subject category
indicates a redistribution of relative emphasis among the top three clusters. In the context
of absolute increases in paper outputs for all three clusters from 2000 through to 2015, the
‘‘chemistry’’ macro-discipline (driven particularly by growth in ‘‘biochemical research
methods’’) took a larger relative share of publications over the recent 5 year period
(2011–2015). Relative growth in this most recent period was also seen in publications in
the ‘‘biotechnology’’ cluster.
The profile of synthetic biology research on the map of science also depicts groupings of
synthetic biology papers that are found among the clusters of ‘‘multidisciplinary sciences’’
(including ‘‘research and experimental medicine’’), ‘‘genetics and computational biology,’’
‘‘materials science’’ (which includes ‘‘nanoscience and nanotechnology’’ and ‘‘biomedical
engineering’’), and ‘‘microbiology and immunology.’’ Subjects in the ‘‘computer science
and engineering’’ cluster (including ‘‘computer science, interdisciplinary applications’’ and
‘‘statistics and probability’’) are noticeably linked to subjects in the core synthetic biology
clusters, including ‘‘biochemistry and molecular biology,’’ ‘‘biochemical research
methods,’’ and ‘‘genetics and heredity.’’ Just over 280 papers (or 4.9% of our dataset of
synthetic biology papers) are in subject categories with explicit reference to ‘‘engineering’’,
led by the three largest categories of biomedical, chemical, and electrical and electronic
engineering. If papers that use engineering (or a variations such as metabolic or genome
engineering) are added to the papers in engineering subject categories (with duplicates
removed), we find about 625 papers (10.8% of the dataset). Papers in engineering subject
classifications or which use engineering as a key word are arrayed across multiple
macrodisciplines on the profile map of synthetic biology. If papers that use terms such as design
or assembly were also identified, the number of synthetic biology papers explicitly
referring to engineering concepts would be higher and new variations in the distribution
might be observed—although another study would be required to fully elucidate, test, and
analyze this proposition. About 165, or 2.9 percent, of the articles in our dataset that raise
HUMANITIES, PHILOSOPHY & SOCIAL SCIENCE
COMPUTER SCIENCE &
ETHICS, POLICY &
Table 7 Top 10 macro-discipline of synthetic biology Source: Synthetic biology publications, 2000–2015
(N = 8064)
Biochemistry; Molecular and Cell Biology
Multidisciplinary Sciences; Medical Research
Genetics; Computational Biology
Computer Science and Engineering
Biology and Ecology
Chemical Engineering; Energy and Fuels
Percent of total publications
SCs = Number of Web of Science subject categories in macro-discipline. Method of assignment of subject
categories to macro-disciplines based on
Carley et al. (2016)
. Percentages for all macro-disciplines sum to
more than 100% as publications may be associated with more than one journal or book subject category
synthetic biology topics are papers with subject categories in the humanities and social
sciences. The overlap map shows visible nodes in the ‘‘history and philosophy of science,’’
‘‘social science, biomedical,’’ ‘‘ethics,’’ ‘‘medical ethics,’’ ‘‘social issues,’’ and ‘‘law.’’
Multiple factors, structures, and dynamics underpin the practices and trajectories of science
and, in particular, the emergence of new scientific domains
(Merz and Sormani 2015)
of the key enabling elements, especially in the contemporary practice of both public and
private science and its growth, is the availability of funding to support researcher time,
laboratories, and the various other costs and overheads associated with scientific research
(Shrum et al. 2007; Kennedy 2012)
. Individual researchers and teams can (and do) pioneer
new scientific approaches and methods, by using already available resources and, where
necessary, seeking added funding in researcher-initiated open requests for proposals.
However, an uptick in support from the sponsors of research is generally indispensable for
an emerging field of scientific inquiry to speedily ‘‘take-off’’, to initiate new research
projects, acquire recognition, attract and train new researchers, and build a critical mass of
interdisciplinary and institutional collaborators. Increased support for an emerging field
can draw on existing funding sources and programs or, as is often the case, through the
introduction of dedicated funding calls and programs. There is debate about the
relationships between additional tranches of research funding for science and the utility of the
knowledge that results (Sarewitz 2016). Nonetheless, it is apparent that whether and how
an emerging domain attracts support, including what kind of research is performed and its
scale and scope, is intrinsically intertwined with the priorities and policies of the bodies
that support research and the organizational and governance landscapes within which these
research sponsors operate (Stefan 2012). This includes extramural scientific research
funded by research councils, government agencies, foundations, and corporations, as well
as internal or core research support from within research-performing organizations.
In tracing the precursors of synthetic biology, pointers can be directed to the early 1960s
and early work on molecular network cell regulation, molecular cloning in the 1970s and
1980s, and the growth of genomics and systems biology in the 1990s
Cameron et al. 2014)
. From 2000 to 2003, denoted by Cameron and colleagues as synthetic
biology’s ‘‘foundational years’’, engineering concepts of genetic circuits, networks, and
switches advanced significantly. The key funders of this early millennium synthetic
biology research included the US Defense Advanced Research Projects Agency (DARPA),
the US Office of Naval Research (ONR), the US National Science Foundation (NSF), and
the US National Institutes of Health (NIH). Early research funding was also provided by
US and European foundations, and Canadian, European and other US research funders.
Interest in synthetic biology, both from researchers and from funding bodies, took off from
around 2004. That year saw the first major international synthetic biology conference, held
in the US
(MIT 2004; Campos 2009)
, and the founding of the synthetic biology student
competition that is now the International Genetically Engineered Machine (iGEM)
competition (Smolke 2009). The concept of open-access standardized biological parts was
advanced in this period, with the establishment of the BioBricks Foundation in 2006
(Minssen and Wested 2015)
. From 2004 to 2006 NSF ramped up funding for synthetic
biology research, leading to the formation of the multi-partner Synthetic Biology
Engineering Research Center (SynBERC) which was awarded more than $37 million in 2006
under an NSF cooperative agreement with the University of California, Berkeley
2006; Si and Zhao 2016)
. In 2005, the Alfred P. Sloan Foundation sponsored an initiative
to examine the risk, societal, ethical and governance implications of synthetic biology,
awarding nearly $10 million through to 2014 (Sloan Foundation 2016). In 2008, NSF
awarded nearly $34 m to a second Engineering Research Center, this to the Center for
Biorenewable Chemicals (CBiRC) at Iowa State University (NSF 2008). Overall, from
2008 through to 2014, the Wilson Center (2015) identifies about $820 million of US public
research funding as allocated to synthetic biology; of this, three-fifths was sponsored by
DARPA, with funding from NSF, NIH, the Department of Energy (DOE), and other
Department of Defense (DOD) agencies comprising much of the balance. The Wilson
Center estimates less than two percent of this funding has gone to research on risk, ethical,
legal, and societal issues.
In Europe, research support explicitly for the development of synthetic biology began to
expand in the mid-2000s. In the UK, the Biotechnology and Biological Sciences Research
Council (BBSRC) sponsored multi-university Networks in Synthetic Biology beginning in
2007, while the Engineering and Physical Sciences Research Council (EPSRC) awarded
£4.7 m in 2009 to a Centre for Synthetic Biology and Innovation (CSynBi) at Imperial
College London. UK research sponsorship of synthetic biology grew to upwards of £200
million by 2015, with a further six university BBSRC ‘‘Synthetic Biology Research
Centres (SBRCs)’’ established in 2013 and 2014, along with support for synthetic biology
doctoral training, academic-industry networks, commercialization, and related research
and translational projects
(Shapira and Go¨ k 2015; Clarke and Kitney 2016)
. In the
mid-tolate 2000s, a 10-year €60 million university research investment in synthetic biology was
reported in the Netherlands, the Swiss National Science Foundation (SNSF) expanded
synthetic biology research funding, and smaller synthetic biology research programs (in
addition to general research funding) were initiated in Germany, France, and some other
(Wilson Center 2010; Meyer 2013; Pei et al. 2012)
. The European
Union enlarged its funding of synthetic biology research from 2005 onwards, awarding an
estimated €86 million to synthetic biology under its 7th Framework Programme
(2007–2013), with support continuing under the Horizon 2020 (2014–2020) Research and
Innovation Programme (ERASynbio 2014). There are no updated measures of European
research funding for risk, ethical, legal and societal implications of synthetic biology
(beyond a 2010 estimate of about 2% by the Wilson Center)
, although some support for
responsible research and innovation has been embedded into the UK SBRCs as well as in
projects sponsored by the European Union
(Stemerding and Rerimassie 2013; Hagen
. Among other countries
(see also OECD 2014)
, synthetic biology research funding
in China began to pick up towards the end of the 2000s, with funding from the National
Natural Science Foundation of China (NSFC) and other governmental R&D and
(Yang 2009; Pei et al. 2011; Chen and Feng 2016)
With the global expansion of research activities and outputs in synthetic biology in
recent years, a systematic approach to tracking the linkages between research funders and
research outputs is needed. This can now be done at a large scale by data mining funding
acknowledgments information that, since mid-2008, is available in the records of journal
publications in the WoS. The analysis of funding acknowledgements information offers
broad insights about who is sponsoring research, what research gets funded and how it is
(Shapira and Wang 2010)
. There are caveats. For example, not all awards are
acknowledged (although sponsors are increasingly making this a requirement), the
recording of social science and humanities awards is lower than for natural sciences and
engineering, there are variations in the reporting of the names and programs of funding
agencies, and work supported through ongoing institutional resources may not be
acknowledged as there is no dedicated funding award (Wang and Shapira 2011; Tang et al.
2016). That said, of the 4250 journal articles in our synthetic biology database published
between 2009 and 2015, a relatively high share—3405 or 80.1% of these
papers—acknowledge one or more funders. The percentage of articles with funding
acknowledgements in our synthetic biology publications dataset rose from 68% in 2009
(soon after the WoS started to report acknowledgements) to 85% in 2015. In short, in the
7 year time period that we analyze, four in five synthetic biology papers report funding
acknowledgements, and the proportion is higher for more recent papers.
For the synthetic biology papers with funding information, more than 5000 variations in
sponsor names are listed. The types of sponsor acknowledged included research councils,
national and regional government agencies and programs, universities and research centers,
foundations, corporations, international agencies and international research partnerships,
and career advancement, fellowship, and mobility awards. We undertook an iterative
cleaning process, using VantagePoint to develop a master thesaurus of variants of sponsor
names, abbreviations, and acronyms and coupled this with manual review and verification.
Judgement was exercised in how to combine (or separate) certain sponsors. For example,
we separated out the European Research Council which has a distinct operational mode in
selecting research for funding from the European Union with its multiple framework,
collaborative research, and developmental programs. We also distinguished individual key
mission funding agencies within the US Department of Defense, and disaggregated
prominent Chinese government funding programs. After three rounds of cleaning,
reaggregation, and review, just under 2800 cleaned sponsor names emerged. On average,
each funded paper acknowledged 2.6 sponsors, with 60% of the papers acknowledging
between two to four sponsors. Multiple research sponsorship can reflect acknowledgement
to a research line, team or center that has attracted more than one research sponsor and,
since synthetic biology papers are typically multi-authored, acknowledgement to the mix
of author funding sources.
Although there is a wide global spread of research sponsors of synthetic biology, there
is also concentration among a subset of sponsors. While the available data does not provide
funding amounts, it does allow analysis of the distribution of acknowledgements to
sponsors. A cluster of 30 research sponsors is acknowledged in 74.7% of the funded
articles, within which a top group of 20 research sponsors is acknowledged in 70.6% of the
funded synthetic biology articles. By the number of WoS synthetic biology articles
published between 2009 and 2005 with funding acknowledgment information, two leading
sponsors stand out (Fig. 4). NIH is acknowledged in about 750 articles, with NSF
acknowledged in just over 615 articles. These two US sponsors are followed by the
European Union, NSFC (China), and the US DOE. Overall, the US has six funders among
the top 20 global sponsors of synthetic biology research, China has three, Canada,
Germany, Japan, the UK, and the European Union each have two, while South Korea has one.
By supranational regional bloc, eight funders among the top 20 global sponsors of
synthetic biology research are located in North America, and six each in Europe and Asia,
although the top US research sponsors have supported more synthetic biology articles to
date than the combined total of the 14 top-twenty sponsors in other countries. Represented
among the leading US synthetic biology research sponsors are DARPA, ONR, and DOD.
For China, in addition to NSFC, the top sponsor group includes two programs of the
Ministry of Science and Technology: the 973 National Basic Research Program
(multidisciplinary basic research) and the 863 National High-Tech R&D Program (research on
potentially commercializable technologies). Two interrelated European supra-national
bodies are in the top group: the European Union’s research and innovation programs and
the European Research Council. With the exception of Japan’s Ministry of Education,
Culture and Sport (MEXT) and the Canadian Institutes of Health Research (CIHR), all
other sponsors in the top twenty group funded more papers in the most recent 3-year period
(2013–2015) than in the prior 4 year period (2009–2012). Comparing the most recent with
Fig. 4 Top 20 research sponsors of synthetic biology articles. Source: Synthetic biology articles,
2009–2015, with one or more funding acknowledgements (N = 3392). Y-axis depicts number of papers
acknowledging research sponsor. A paper may have more than one funding acknowledgement. Italicized
number to right of each bar reports average WoS citations per paper to papers acknowledging that research
the prior period, the greatest relative growth in funded articles was supported by DARPA,
the European Research Council, and China’s 863 R&D Program.
Looking deeper into the ranks of the sponsors of synthetic biology research (by numbers
of papers with funding acknowledgements), the great majority of funders in the top 50 are
public research councils or government agencies. Just outside the top twenty group is the
Human Frontier Science Program, an international partnership of 14 countries (including
the US and six European countries) plus the European Union, which funds research into
complex biological systems. Among foundations, the Howard Hughes Medical Institute,
the Wellcome Trust, and the American Heart Association are among the fifty top research
sponsors of synthetic biology papers, while the Welch Gates, and Packard foundations are
just outside the top 50. The Council of Scientific and Industrial Research (India) and the
Russia Foundation for Basic Research are the only other emerging economy sponsors
among the top 50 research sponsors, with each at sponsorships levels that are about
onetwentieth of the number of papers acknowledged by Chinese research funders.
Among the leading research sponsors of synthetic biology papers, there are noticeable
differences in macro-disciplines of focus. For example, 44% of papers with
acknowledgements to MEXT are in ‘‘chemistry and biochemical research’’ methods, while about
40% respectively of synthetic biology papers with acknowledgements to the UK EPSRC
and the US DOD are also in this macro-discipline. For CIHR, NIH and DARPA, there is a
relatively greater focus on ‘‘biochemistry and molecular and cell biology’’, with over 30%
of the synthetic biology papers funded by each of these sponsors appearing in this
macrodiscipline. In China, ‘‘biotechnology’’ is the prioritized macro-discipline for the 863 and
973 Programs, acknowledged by 54 and 41% respectively of synthetic biology papers
supported by these two funding sources.
In addition to influencing research themes, research sponsorship also has a relationship
on the likelihood of research impacts. In particular, synthetic biology papers that
acknowledge funding sources are more likely to be cited than papers which do not report
any funding information. Synthetic biology papers published between 2009 and 2015
garnered an average of 16.1 citations per paper (as of May 2016); those with funding
acknowledgements (more than four-fifths of the papers) averaged 18.0 citations per paper
compared with 8.4 citations for those without funding acknowledgements. This is
consistent with findings previously reported that funded papers garner higher citation counts
than papers which do not reveal any funding for reasons which can include higher
motivation and access to resources for funded researchers and the role of review and selection
processes in filtering out weaker research and rewarding promising topics, as well as
potential reputational biases associated with funding awards and paper citations
. We note other interaction effects. For example, we observe significant field
differences in citation propensities. It is well established that citations to research journals
vary significantly by fields, with research in fields such as cell biology, multidisciplinary
chemistry, nanoscience, and multidisciplinary science more likely to attract higher
citations (on average) than research in mathematics and statistics, branches of engineering,
computer science, and social science (Thomson Reuters 2016). These field differences are
influenced by factors that include the size of the field, numbers of authors, and
fieldspecific citation practices, while within fields only a minority of papers and journals
become very highly-cited
(Garfield 1996; Aksnes 2003; Ioannidis 2006)
. We observe such
variations in our data set. For synthetic biology papers published between 2009 and 2015,
the mean citation rate per paper for research in the WoS macro-discipline of ‘‘biochemistry
and molecular and cell biology’’ was 17.8, with papers in ‘‘chemistry and biochemical
research’’ and in ‘‘biotechnology’’ each attracting around 13 cites per paper on average.
Synthetic biology papers in ‘‘genetics; computational biology’’ and in ‘‘computer science
and engineering’’ respectively averaged 10.6 and 6.3 citations per paper. Most noticeably,
synthetic biology papers in the macro-discipline that covers ‘‘multidisciplinary sciences’’
garnered 33.4 cites per paper on average. Indeed, 28 of the top 50 cited synthetic biology
papers published between 2009 and 2015 were in ‘‘multidisciplinary sciences’’ including
11 papers in Nature and 10 papers in Science—two journals with among the highest
journal impact factors of all publications in the WoS (Thomson Reuters 2016). Five other
macro-disciplines were represented among this group of 50 most cited synthetic biology
papers: ‘‘biochemistry; molecular and cell biology’’ (with 14 highly-cited papers),
‘‘biotechnology’’ (5 highly-cited papers), ‘‘chemistry and biochemical research methods’’
(3 highly-cited papers), and ‘‘biology and ecology’’ and ‘‘material sciences’’ (each with one
highly-cited paper). (Total adds to more than 50 due as two journals are classed in more
than one macro-disciplinary category.)
Among the sponsors of research, we also observe differences in citations accrued by
synthetic biology papers published between 2009 and 2015 and which acknowledge
specific sponsors (Fig. 4). It should be noted that papers published in this time period could
have been sponsored prior to the start of the time period, citations tend to increase over
time (meaning that papers published towards the start of the period have more time to
accrue citations), and that papers typically acknowledge more than one funding sponsor.
Additionally, variances in citation propensities by field (as noted above, given our results
on how research sponsors vary in their fields of supported research), and in citation
practices by and of authors from different countries
(Tang et al. 2015; Albarra´n et al.
, should be kept in mind. NIH and NSF—the two research sponsors with the greatest
number of synthetic biology papers published between 2009 and 2015 that acknowledge
their support—also have accrued a disproportionately high number of citations (about
20,500 and 16,000 respectively). The papers associated with these two sponsors have
relatively high average citation rates—about 29 and 26 cites per paper respectively for
those sponsored by NIH and NSF. Synthetic biology papers which acknowledge DOD,
CIHR, and DOE also attract a comparably high number of average citations per paper.
Synthetic biology papers acknowledging the US ONR attract the greatest number of
average citations per paper (more than 40). Among the top 50 most cited synthetic biology
papers, 30 acknowledge NIH while 26 acknowledge NSF. (Again, totals exceed 50 as
papers acknowledge multiple research sponsors). Just over 4% of all synthetic biology
papers sponsored by NIH and NSF (and published between 2009 and 2015) are included in
the top 50 most cited group. ONR sponsorship is associated with the third highest number
of top cited papers, although it is 13th in total number of papers sponsored. Over 8% of all
ONR sponsored papers are included in the top 50 most cited group. All papers that
acknowledge ONR also acknowledge other sponsors, most commonly NIH and NSF.
Lower average citation levels per paper are seen in synthetic biology papers sponsored by
China’s 873 and 973 programs, and by the Korean National Research Foundation. Neither
of the two Chinese governmental programs had any sponsored papers in the top fifty most
cited papers, although three papers sponsored by NSFC of China were in this top group
although none were in Nature or Science. Synthetic biology papers sponsored by European
funders generally had average citation impacts at levels lower than for US sponsors,
although there was some placement of European-sponsored synthetic biology papers in
high impact multidisciplinary and field journals.
Discussion and conclusions
This paper has put forward a systematic approach to bibliometrically defining the research
domain of synthetic biology. We constructed a keyword based search strategy using
keyword co-occurrence analysis, combined this keyword-based search strategy with a
targeted synthetic biology journal search, and undertook iterative refinement processes. We
identified and cleaned a dataset of synthetic biology publications published between 2000
and 2015. Our approach, when compared with other narrower and broader synthetic
biology search strategies, resulted in reasonably robust balances of precision and recall.
The search strategy described in this paper is positioned as a public tool that is available
for use and refinement by the research community. Researchers, technology managers,
research sponsors, policy analysts and others who seek information on scientific and
technological development in the synthetic biology field can use the search strategy. A
database of synthetic biology papers can be generated by applying the search strategy
(comprising the collated key word terms in Table 3 plus the journals listed in Table 4) to
the Web of Science. The search strategy can be applied to other databases of publications
(such as PubMed or Scopus) and to patents, bearing in mind variations in how search
algorithms and key terms are entered into these databases. The limited search features of
Google Scholar makes it less amenable to complex bibliometric searches.
Given the continued pace of growth and development of research in synthetic biology,
the search approach and analysis contained in the paper inevitably represents an ex post
view. This retrospective limitation should be kept in mind when using and interpreting the
results. Our initial set of candidate keywords was garnered from papers captured by the
MeSH term for synthetic biology in PubMed for records published from 2011 to 2014; and,
after the further steps detailed earlier in this work, we applied our approach to papers
published in the period from 2000 to 2015. Over time, it is likely that scientific advances
will generate new synthetic biology terms that were not evident or captured when we
developed the search approach. As an update option, users can add new synthetic biology
journals or curated collections to the journal search list. At a future point, it could also be
useful to revisit the keywords captured by the MeSH synthetic biology classification and in
papers included in synthetic biology journals or curated collections.
In the paper, we applied our approach to track developments in synthetic biology
research and publication, including analyses of the key scientific disciplines and
knowledge sources contributing to this emerging field, national outputs and international
collaborations, the distribution of synthetic biology on the map of science, and its profile of
research sponsorship. These are selected examples of the multiple-kinds of analyses that
can be undertaken with the approach. There are many opportunities to undertake further
analyses, with controls and drawing on added data sources, to probe issues related to
research and governance in the synthetic biology domain. As noted, there are also
opportunities to further refine our search strategy, especially as synthetic biology and its
research community evolves, and to apply the search strategy principles to develop
bibliometric search approaches in related as well as non-related emerging technology
The analysis in this paper details the rapid growth and international spread of research
in synthetic biology in recent years, with an expansion of research nodes in the US and
Canada, Europe, and East Asia. We show that diverse research disciplines are contributing
to the global growth of synthetic biology research outputs in recent years. These include
clusters in biochemistry, molecular and cell biology, and chemistry and biochemical
research methods, but also engagement from multidisciplinary sciences, genetics and
computational biology, microbiology, and energy and fuels. Engineering disciplines and
approaches are distributed across in multiple sub-fields in the development of synthetic
biology. Most significantly, the range of research fields involved in synthetic biology
implies that it is emerging as an assembly of platform technologies that is likely to lead to
multiple applications in biochemical, medical and health, energy, materials, agricultural,
and other markets. We also observe small streams of research outputs that probe ethical,
legal, societal, and governance aspects of synthetic biology, although it is uncertain
whether this work is yet of sufficient scale, embeddedness, and impact in terms of its
contribution to responsible research and innovation in synthetic biology.
Inherently, further questions arise from the analyses reported in the paper. For example,
we highlight the key roles played by a relatively concentrated set of about 20 research
sponsors in funding the growth and trajectories of synthetic biology. It would be
appropriate, in follow-on research, to probe in detail the policies and procedures adopted by this
dominant grouping of research sponsors, who are located in North America, Europe, and
Asia, and how these will impact the further development of the synthetic biology domain
and its governance. At a program evaluation level, we can see cases where research
sponsorship (with significant funding levels) has been explicitly targeted to stimulate
synthetic biology research, including large-scale synthetic biology research centers in the
US, and synthetic biology networks and research centers in the UK and elsewhere. In other
cases synthetic biology research has been sponsored through existing funding
arrangements. It seems that the clustering of synthetic biology research in dedicated centers has
broadly contributed to growth and recognition of the domain, but many questions have yet
to be addressed about the performance of individual centers, their levels of
interdisciplinary, and their approaches to responsible governance, training, and commercialization.
Our search approach operationalizes a feasible and replicable bibliometric method to
capture research outputs in the synthetic biology domain. We acknowledge the various
limitations of the approach. These include the comparative limitations of the Web of
(Falagas et al. 2008; Leydesdorff et al. 2016)
including weaknesses in terms of the
scope of publications and disciplines that are indexed and incompleteness in some fields
(for example, funding acknowledgements). We also recognize the imperfections in using
publications and citations as measures of research performance, quality, and impact.
Importantly, while we appreciate, and attempt to address, the ambiguities that currently
exist in the understanding of what synthetic biology is, we cannot resolve all uncertainties
about the delineation of synthetic biology. There are underlying differences in terms of
expert and governance interpretations as to what constitutes the field, and advancement in
the field itself, including convergence with other technologies and disciplines, which will
further confound definitional approaches. Our operationalization of a bibliometric
definition of synthetic biology is situated within this disputed terrain. Although we have
carefully deliberated upon and tested our approach, probably our search strategy does not
capture every relevant keyword that can be representative of synthetic biology research
(although it is always important to check whether other included terms are already
capturing anything that is felt to be missing), and some may judge that certain terms are too
expansive. Moreover, with further iterations of the search strategy, additional exclusion
terms might be appropriate. It will thus be useful to continue to probe our approach, to test
and refine it through further studies, and also to explore how other bibliometric approaches,
including those that use enhanced machine learning algorithms to discern what is (and is
not) in the domain, compare with the keyword (plus key journal) synthetic biology search
strategy that we have put forward in this paper.
Acknowledgements This work was supported by the Biotechnology and Biological Sciences Research
Council (Grant Number BB/M017702/1) (Manchester Synthetic Biology Research Centre for Fine and
Speciality Chemicals) and by the National Science Foundation (Grant No. 0937591) (Center for
Nanotechnology in Society CNS-ASU). The authors gratefully acknowledge comments from Abdullah Go¨k and
guidance from Stephen Carley on science overlay mapping. Any opinions, findings, and conclusions or
recommendations expressed in this material are those of the authors and do not necessarily reflect the views
of the Biotechnology and Biological Sciences Research Council or the National Science Foundation.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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