Pre-existing technological core and roots for the CRISPR breakthrough

This paper applies objective methods to explore the technological origins of the widely acclaimed CRISPR breakthrough in the technological domain of genome engineering. Previously developed patent search techniques are first used to recover a set of patents that well-represent the genome editing domain before CRISPR. Main paths are then determined from the citation network associated with this patent set allowing identification of the three major knowledge trajectories. The most significant of these trajectories for CRISPR involves the core of genome editing with less significant trajectories involving cloning and endonuclease specific developments. The major patents on the core trajectory are consistent with qualitative expert knowledge of the topical area. A second set of patents that we call the CRISPR roots are obtained by finding the patents directly cited by the recent CRISPR patents along with patents cited by that set of patents. We find that the CRISPR roots contain 8 key patents from the genome engineering main path associated with restriction endonucleases and the expected strong connection of CRISPR to prior genome editing technology such as Zn finger nucleases. Nonetheless, analysis of the full CRISPR roots shows that a very wide array of technological knowledge beyond genome engineering has contributed to achieving the CRISPR breakthrough. Such breadth in origins is not surprising since “spillover” is generally perceived as important and previous qualitative studies of CRISPR have shown not only technological breadth in origins but scientific breadth as well. In addition, we find that the estimated rate of functional performance improvement of the CRISPR roots set is about 9% per year compared to the genome engineering set (~4% per year). These estimates indicate below average rates of improvement and may indicate that CRISPR (and perhaps yet undiscovered) genome engineering developments could evolve in effectiveness over an upcoming long rather than short time period.

[1]  F. Rodríguez-Valera,et al.  Long stretches of short tandem repeats are present in the largest replicons of the Archaea Haloferax mediterranei and Haloferax volcanii and could be involved in replicon partitioning , 1995, Molecular microbiology.

[2]  Christopher L. Magee,et al.  Estimating technology performance improvement rates by mining patent data , 2020 .

[3]  Christopher L. Magee,et al.  Quantitative Identification of Technological Discontinuities , 2018, IEEE Access.

[4]  Christopher L. Magee,et al.  Quantitative empirical trends in technical performance , 2016 .

[5]  D. Carroll Genome Engineering With Zinc-Finger Nucleases , 2011, Genetics.

[6]  Christopher L. Magee,et al.  Technology structural implications from the extension of a patent search method , 2014, Scientometrics.

[7]  Christopher L. Magee,et al.  A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field , 2013, Scientometrics.

[8]  Giorgio Triulzi,et al.  Food Productivity Trends from Hybrid Corn: Statistical Analysis of Patents and Field-test data , 2017 .

[9]  L BensonChristopher,et al.  Is There a Moore's Law for 3D Printing? , 2018 .

[10]  Arianna Martinelli,et al.  An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry , 2012 .

[11]  F. Quétier,et al.  The CRISPR-Cas9 technology: Closer to the ultimate toolkit for targeted genome editing. , 2016, Plant science : an international journal of experimental plant biology.

[12]  Önder Nomaler,et al.  Measuring knowledge persistence: a genetic approach to patent citation networks , 2014 .

[13]  Joseph Martino Examples of technological trend forecasting for research and development planning , 1971 .

[14]  Stephen Berard,et al.  Implications of Historical Trends in the Electrical Efficiency of Computing , 2011, IEEE Annals of the History of Computing.

[15]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[16]  W. Nordhaus Two Centuries of Productivity Growth in Computing , 2007, The Journal of Economic History.

[17]  Christopher L. Magee,et al.  A functional approach for studying technological progress: Application to information technology ☆ , 2006 .

[18]  W. Nordhaus Do Real Output and Real Wage Measures Capture Reality? The History of Lighting Suggests Not , 1996 .

[19]  Norman P. Hummon,et al.  Connectivity in a citation network: The development of DNA theory☆ , 1989 .

[20]  R. Barrangou,et al.  CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes , 2007, Science.

[21]  J. Trancik,et al.  Statistical Basis for Predicting Technological Progress , 2012, PloS one.

[22]  C. Barbas,et al.  ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. , 2013, Trends in biotechnology.

[23]  D UrnovFyodor,et al.  Genome Editing B.C. (Before CRISPR): Lasting Lessons from the “Old Testament” , 2018 .

[24]  Christopher L. Magee,et al.  Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach , 2016, PloS one.

[25]  Eric S Lander,et al.  The Heroes of CRISPR , 2016, Cell.

[26]  Jennifer A. Doudna,et al.  The new frontier of genome engineering with CRISPR-Cas 9 GENOME , 2014 .

[27]  Natalie de Souza Zinc-finger nucleases , 2011, Nature Methods.

[28]  J. Doudna,et al.  The new frontier of genome engineering with CRISPR-Cas9 , 2014, Science.

[29]  David T. F. Dryden,et al.  Highlights of the DNA cutters: a short history of the restriction enzymes , 2014, Nucleic acids research.

[30]  Fyodor D Urnov,et al.  Genome Editing B.C. (Before CRISPR): Lasting Lessons from the "Old Testament". , 2018, The CRISPR journal.

[31]  John Metcalfe,et al.  Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge , 2007 .

[32]  J. García-Martínez,et al.  Intervening Sequences of Regularly Spaced Prokaryotic Repeats Derive from Foreign Genetic Elements , 2005, Journal of Molecular Evolution.

[33]  G Vergnaud,et al.  CRISPR elements in Yersinia pestis acquire new repeats by preferential uptake of bacteriophage DNA, and provide additional tools for evolutionary studies. , 2005, Microbiology.

[34]  Christopher L. Magee,et al.  A functional approach for studying technological progress: Extension to energy technology , 2008 .

[35]  Bart Verspagen,et al.  Mapping Technological Trajectories as Patent citation Networks: a Study on the History of Fuel Cell Research , 2007, Adv. Complex Syst..

[36]  F. Rodríguez-Valera,et al.  Transcription at different salinities of Haloferax mediterranei sequences adjacent to partially modified PstI sites , 1993, Molecular microbiology.

[37]  Christopher L. Benson,et al.  Correction: Quantitative Determination of Technological Improvement from Patent Data , 2016, PloS one.

[38]  J. Farmer,et al.  How Predictable is Technological Progress? , 2015, 1502.05274.