Science and technology that function as knowledge resources have shown a complex relation in the process of innovation, especially in the synthetic biotechnology industry, an industry bridging biology and engineering where its standardized, decoupled and modularized innovation mode has reconstructed rather than simply spanned the institutions of academic and industrial worlds. Multiple knowledge networks open a new avenue for studying the evolution of synthetic biotechnology. This paper first proposes a framework of integrating scientific and technological knowledge networks, then utilizes WOS’s Cross Search function to construct the cross-reference between DII and SCI and SSCI, finally employs the indicators of network structures and nodes to analyze the multiple knowledge networks in the evolution of synthetic biotechnology. Results show that at the emergence stage (2000-2003), scientific and technological knowledge are difficult to integrate with each other; at the exploration stage (2004-2007), there exist significant intersection and symbiosis between scientific and technological knowledge; and at the growth stage (2008-2014), scientific and technological knowledge give rise to independent logics of growth.
Synthetic biotechnology originated from the launch of the human genome project and the rise of system biology in the 1990s. Life scientists use the concept of electronic circuits in physics for reference to construct gene circuits with specific functions and logical relationships, and endow cells with new biological functions at the functional level [
Synthetic biotechnology bridges two major disciplines, life sciences and engineering. The subversive innovations it has achieved over the past decade have been driven by a number of scientific achievements and technological advances [
Multiple networks are dynamic, reflecting the variability of individual networks and the linkage between networks. Over time, the network may be deformed, nested or separated. In some cases, it shows a trend of decline or even disintegration. The existing research mainly focuses on the discussion and analysis of the network evolution model and its motivation [
Building a multiple network is a frontier trend in complex network research, such as building a multiple network of social networks and their sub-networks [
Patent citation information system is an effective way to study knowledge flow problems at present, which can link industrial development with related scientific and technological fields [
The data in this paper comes from the Web of Science (WOS). WOS is an academic index platform with great influence worldwide. It includes the Science Citation Index (SCIE), the Social Science Citation Index (SSCI) and other literature index databases, as well as the Derwent World Patent Index (DII). Compared with the USPTO and the EPO, DII covers patent information from many countries and has more comprehensive data, which has advantageous for examining the overall development of emerging technologies. More importantly, WOS provides CrossSearch functions. It is possible to capture the flow of scientific and technological knowledge at the same time by building cross-reference between DII patent database and SCI, SSCI citation index database with unique citation retrieval mechanism and powerful cross-retrieval function.
Keywords are one of the important tools for searching patents and literature. However, the identification of keyword search strategies requires the participation of experts in various fields of the industry. The scientificalness and timeliness determine the rationality of the research samples. This paper refers to the search strategy of synthetic biology keywords proposed by Shapira, Kwon and Youtie, downloads 1385 synthetic biotechnology patents from 2000 to 2014 as focal patents from WOS, and further downloads 43804 cited literature and 36943 cited patents of these focal patents [
Empirical analysis based on network characteristic index system includes two
Evolution Stage of Synthetic Bio-technology | Focal patents | Cited literature | Cited patents |
---|---|---|---|
Emergence stage (2000-2003) | 456 | 20,467 | 15,891 |
Exploration stage (2004-2007) | 336 | 9077 | 9706 |
Growth stage (2008-2014) | 593 | 14,260 | 11,346 |
Total | 1385 | 43,804 | 36,943 |
parts: 1) Analyze the structural characteristics of multiple knowledge networks by using network structure indicators, including network density, network degree distribution, connectivity of the networks, the pattern of clustering; 2) Analyze the characteristics of multiple knowledge network nodes by using Term Frequency-Inverse Document Frequency (TF-IDF), which is a network node index.
After multiple screening and cleaning of the sample data, we use the visual text analysis tool CiteSpace to generate the keyword co-occurrence map, and then extract the literature and patent title keyword data, and use the complex network analysis integration software Ucinet to draw a clearer large-scale keyword co-occurrence network, thus obtaining the keyword co-occurrence network of focal patents, cited literature and cited patents at different research stages. In the overall network structure, the larger the node, the more the keyword appears in the title of the literature or patents’ report, and the higher the co-occurrence frequency with other keywords. It is not difficult to find that there are many component networks (a group of related knowledge clusters) in the focal patents and cited literature and post-introduction patent keywords in each period. What’s more, there are reachable paths between keywords in the component networks, but there is no connection between different component networks. It can be seen that secondary aggregation is formed within the network, and the evolution of the overall network can be decomposed into the evolution of each secondary network, which implies that even within the same network, there are still multiple technological subject development paths. At the same time, with the continuous development of synthetic biotechnology, the focal patents keyword network has been deepened, and the cited literature and the cited patent keyword network have presented different network characteristics. Therefore, it is helpful to deconstruct the evolution of synthetic biotechnology to analyze the multiple knowledge network from two dimensions: overall network structure and knowledge element network node characteristics.
According to the co-occurrence network of focal patents and its cited literature and patents at the emergence stage (Figures 1-3), we found that, firstly, from the perspective of network density, the network density (0.015) of the cited literature is basically the same as the focal patents network density (0.016), which is lower than the cited patents (0.025). It shows that scientific knowledge is loose and technological knowledge is close at the emergence stage, and the distribution of innovation in synthetic biotechnology has high randomness. Secondly, from the perspective of network degree centrality, there are great differences between different knowledge networks. The degree centrality of the focal patent network, the cited literature and the cited patent network are 0.037, 0.649, and 0.175 respectively. This shows that the core node of scientific knowledge is prominent at the exploration stage, the core node of technological knowledge appears but has not yet been completely formed, and the distribution of synthetic biotechnology innovation is highly discrete. Thirdly, from the point of view of network diameter, the network diameter (3.5) of cited literature is slightly larger than that of cited patents (3.1), and both are much larger than that of focal patents (1.2). This trend is determined by the active knowledge flow of synthetic biotechnology. Fourthly, according to the network cohesive subgroups, the cited network (40) is slightly smaller than the cited patent network (49), which is much larger than the focal patent network (3). This shows that although the degree of differentiation between the post-cited literature network and the post-patent network reaches a certain level, the diversity of synthetic biotechnology innovation is extremely limited. This also implies that there is a certain obstacle in the knowledge fusion between scientific knowledge and technological knowledge at the emergence stage. Professor Endy (2005), the founder of the discipline, classified
it into two major categories: at the scientific level, little is known about biological systems; at the technological level, the engineering level at that time was not sufficient to control the complex microscopic biological system. Although the germination of synthetic biology bridges biology and engineering, the development of the two types of disciplines stems from the independent process of institutional, social structure and background shaping [
According to the co-occurrence network of focal patents and its cited literature and patents at the exploration stage (Figures 4-6), we found that, firstly, from the perspective of network density, the focal patent network density (0.030) is the same as the cited patent network (0.030), which is much larger than the cited literature network (0.004). The network density of scientific knowledge and technological knowledge at the exploration stage maintained the trend of the emergence stage and further deepened. The connection of scientific knowledge becomes scattered and the connection of technological knowledge becomes closed. However, the innovation of synthetic biotechnology was different from the previous stage and shows a higher sustainability. It shows that the synthetic
biotechnology innovation has changed during the exploration period. Secondly, from the perspective of network degree centrality, the degree centrality of the cited literature (0.665) and the cited patents (0.175) both maintain the aggregation degree of the aggregation of knowledge nodes from the absolute number or the relative number, but the degree centrality of the focal patents (0.149) has been greatly improved. This shows that the innovation of synthetic biotechnology was no longer too discrete, the core areas were gradually determined during the exploration stage. Thirdly, from the point of view of network diameter, the network diameter of the focal patent, the cited literature, and the cited patent are 1.4, 3.2, and 3.8 respectively, fluctuating but basically consistent with the previous stage. Fourthly, according to the network cohesive subgroup, compared with the previous stage, the differentiation degree of the focal patent network (20), the cited literature network (194) and the cited patent network (76) is greatly improved. Explain that the scientific knowledge and technical knowledge have merged during the exploration period and jointly promoted the innovation of synthetic biotechnology. Life science and engineering began to merge rapidly. Technological advances such as gene synthesis, deletion and editing have made it possible to connect theoretical research and applied research of synthetic biotechnology. The top-down research method based on the redesign of existing gene sequences intersects with the bottom-up research method based on non-living components to construct life systems. More and more scientific research teams attached importance to the two-way extension of synthetic biology to engineering and molecular biology, not only the molecular components required for complex gene circuits, but also the technical methods for adapting gene circuits.
According to the co-occurrence network of focal patents and its cited literature and patents at the growth stage (Figures 7-9), we found that, firstly, from the perspective of network density, the density of knowledge networks at all levels showed a significant decline. The focal patent network and the cited literature network were highly dispersed: the focal patent network density was 0.002, the cited literature network density was 0.002, and the cited patent network density was 0.015. Further comparing the total number of nodes and the number of connected edges of nodes in the network density index, it is found that the scale of each layer of knowledge network has an exponential transition. Secondly, from the perspective of network degree centrality, compared with the previous stage, the focal patent network degree centrality, (0.125) decreased slightly but remained basically the same, while the cited document network (0.375) and the cited patent network (0.072) decreased significantly. It can be seen that the scientific knowledge and technical knowledge elements at the growth stage were gradually enriched, and the research fields and topics were scattered. However, it has not caused great changes in the level of innovation. Thirdly, from the point
of view of network diameter, the diameter of the cited literature network (2.9) and the cited patent network (3.3) have not changed substantially compared with the exploration stage, but the diameter of the focal patent network has increased greatly, reaching 4.1. Fourthly, according to the network cohesive subgroup, the network cohesive subgroup of the focal patent, the cited literature, and the cited patent are 34, 611, and 117 respectively, all of which are greatly improved, especially the scientific knowledge shows a trend of highly differentiation. After a short period of co-evolution, major common scientific and technological problems hindering industrial innovation have been effectively solved. Science and technology have reached such a level that they can break through the structural dilemma of mutual restriction and basically realize the standardized, decoupled and modularized innovation mode [
At the emergence stage of synthetic biotechnology, the comparison of TF-IDF values of multiple knowledge network keywords shows (
Focal Patent | TF*IDF | Cited Patent | TF*IDF | Cited Literature | TF*IDF |
---|---|---|---|---|---|
Nucleic acid | 108.13 | Nucleic acid | 148.07 | Gene expression | 87.63 |
Amino acids | 96.71 | Gene therapy | 138.43 | Binding protein | 64.27 |
Amino acid sequence | 82 | Nucleic acids | 121.3 | Monoclonal antibody | 62.19 |
TF technology focus | 72.09 | Host cell | 54.77 | Amino acid | 82.92 |
Gene therapy | 56.44 | Nucleic acid sequence | 54.77 | Binding domain | 48.43 |
… | … | … | … | … | … |
Acetyl coenzyme | 14.87 | Antimicrobial agent | 17.85 | Bacillus thuringiensis | 12.78 |
different. In particular, the nucleic acid ranked first in TF-IDF value in the basic patent keyword co-occurrence network is ranked lower in the post-citation keyword co-occurrence network, which shows that it is not the focus of synthetic biotechnology research in the emergence stage. From the core research field and core research themes, the similarity between the cited literature and the focal patents is low in the emergence stage of synthetic biotechnology, but the similarity between the cited patents and the focal patents is high, innovation mainly depends on technical knowledge.
At the exploration stage of synthetic biotechnology, the comparison of TF-IDF values of multiple knowledge network keywords shows (
Focal Patent | TF*IDF | Cited Patent | TF*IDF | Cited Literature | TF*IDF |
---|---|---|---|---|---|
Nucleic acid | 57.15 | Nucleic acid | 203.98 | Amino acids | 60.68 |
Amino acids | 52.78 | New nucleic acid | 78.42 | Nucleic acid | 19.32 |
Amino acid sequence | 48.16 | Nucleic acid sequences | 64.47 | Fusion protein | 13.39 |
Nucleotide sequence | 36.25 | Rheumatoid arthritis | 60.87 | Nucleic acids | 13.43 |
Synthetic gene | 36.25 | DNA sequencing | 60.87 | Gene product | 8.40 |
… | … | … | … | … | … |
Androgen response sequence | 13.79 | Therapeutic agent | 17.05 | Nucleotide sequences | 4.73 |
intermediate TF-IDF value. From the core research field and core research themes, compared with the g emergence stage, a significant feature is that the research field and the core research of the cited literature and the focal patent keyword co-occurrence network have high consistency. It indicates that the similarity among the focal patents and those focal patents’ cited literature/patents at the exploration stage of synthetic biotechnology is high, and innovation depends on new scientific and technological knowledge at the same time.
At the growth stage of synthetic biotechnology, the comparison of TF-IDF values of multiple knowledge network keywords shows (
Focal Patent | TF*IDF | Cited Patent | TF*IDF | Cited Literature | TF*IDF |
---|---|---|---|---|---|
Nucleic acid | 117.43 | Nucleic acids | 183.49 | Gene expression | 133.68 |
Nucleotide sequence | 88.72 | Nucleic acid sequence | 125.68 | Nucleic acid | 78.93 |
Synthetic gene | 85 | Treating cancer | 118.67 | Amino acids | 53.65 |
Amino acids | 79.5 | Gene therapy | 111.47 | Breast cancer | 46.60 |
Amino acid sequence | 62.62 | Host cell | 104 | Tumor cell | 28.94 |
… | … | … | … | … | … |
Artificial cell membrane | 15.49 | Acetic acid | 20.18 | Plant genes | 10.47 |
different. From the point of view of the core research field and themes, compared with the exploration period, the similarity between the later cited literature or the cited patents and the basic patents has been improved, but the similarity between the cited literature and the cited patents shows a decreasing trend. Synthetic biotechnology innovation relies on relatively independent scientific and technological knowledge pathways.
From the perspective of global evolution, after more than ten years of development, synthetic biotechnology has completed the symbiosis of resources at the emergence stage and the exploration stage, and entered a new strategic pattern of radiation to the world centered on the UK and the United States. In order to provide theoretical basis for formulating scientific and technological policies and planning industrial layout, this paper proposes an analysis framework integrating scientific knowledge network and techno-logical knowledge network to deconstruct the heterogeneous role of science and technology in the evolution of synthetic biotechnology. At the same time, this paper also has the following shortcomings: this paper uses coded patents and literature to explain the relationship between science and technology. Although it captures the dominant knowledge flow to a large extent, it ignores the interactive relationship between science and technology as tacit knowledge, for example, the interaction through industry-university-research cooperation, technology licensing, business consulting and cross-border cooperation. The main conclusions of this paper are as follows.
First, scientific knowledge and technological knowledge are difficult to integrate at the emergence stage of synthetic biotechnology. There are obvious intersections and symbiosis at the exploration stage. What’s more, scientific knowledge and technological knowledge differentiate into independent growth logic in the growth period. The analysis of structural characteristics of multiple knowledge networks in the evolution of synthetic biotechnology indicates: 1) At the emergence stage (2000-2003), scientific knowledge is relatively loose, core nodes are prominent. But technical knowledge is relatively close, and there is a certain tendency to focus on core nodes. There are certain obstacles in the integration of knowledge, it is manifested that there is little knowledge of biological systems at the scientific level, and the technical level is not enough to control complex microscopic biological systems. Synthetic biotechnology innovations have high randomness and dispersion. 2) At the exploration stage (2004-2007), technological advances such as gene synthesis, deletion and editing made it possible to connect theoretical research and applied research of synthetic biotechnology. The connection of scientific knowledge is looser and the connection of technical knowledge is closer, but the innovation of synthetic biotechnology is different from the situation of the previous stage, which is too discrete, showing a high degree of continuity. It can be indicated that scientific knowledge and technical knowledge have some form of convergence during the exploration period, and jointly promote the innovation of synthetic bio-technology. 3) At the growth stage (2008-2014), the elements of scientific knowledge and technical knowledge are gradually enriched. In addition, the research fields and themes are scattered, major scientific and technological problems that hindering industrial innovation have been effectively solved, and relatively independent growth logic has been differentiated to promote the exponential transition of network scale.
Second, the emergence stage of synthetic biotechnology evolution mainly depends on technological knowledge. The exploration stage mainly relies on scientific knowledge and technical knowledge with high similarity. The growth stage mainly relies on scientific knowledge and technical knowledge with low similarity. The analysis of the characteristics of multiple knowledge network nodes in the evolution of synthetic biotechnology indicates that: 1) At the emergence stage (2000-2003), the similarity between scientific knowledge and innovation network is low, while the similarity between technological knowledge and innovation network is high. Synthetic biotechnology innovation mainly depends on technical knowledge. 2) At the exploration stage (2004-2007), the similarity among scientific knowledge, technological knowledge and innovation network is at a high level, suggesting that synthetic biotechnology innovation relies on both scientific and technological new knowledge. 3) At the growth stage (2008-2014), Both scientific knowledge and technical knowledge have high similarities with innovation networks, but the similarity between the two types of knowledge networks is low, indicating that synthetic biotechnology innovation relies on relatively independent scientific and technological knowledge paths.
We believe that future research can be further extended from a synthetic biotechnology scenario to a more general emerging technology scenario. In recent years, emerging technologies such as artificial intelligence and digital manufacturing often span the two categories of science and technology, involving multiple research fields. Moreover, disruptive innovation triggered by these emerging technologies is closely related to the interaction between science and technology. So, the research on the evolution of multiple knowledge networks based on the technology life cycle has research prospects and research values. In addition, stage evolution research can be extended to dynamic evolution research. Relevant studies published in top international journals of management science in recent years have proved the dynamic nature of network indicators and the heterogeneity between cooperative networks and knowledge networks. However, insufficient attention has been paid to the dynamic evolution of “core relationships” between multiple networks, especially multiple knowledge networks. The relevant conclusions are likely to provide some important innovation management inspiration for the complex network field.
The authors declare no conflicts of interest regarding the publication of this paper.
Liu, R.F. (2019) Evolution Analysis of Synthetic Biotechnology from the Perspective of Multiple Knowledge Network. American Journal of Industrial and Business Management, 9, 366-384. https://doi.org/10.4236/ajibm.2019.92025