Funding CRISPR: Understanding the role of government and private sector actors in transformative innovation systems

David Fajardo-Ortiz, Stephan Hornbostel, Maywa Montenegro-de-Wit, Annie Shattuck

 

CRISPR/Cas has the potential to revolutionize medicine, agriculture, and the way we understand life itself. Understanding the trajectory of innovation, how it is influenced and who pays for it, is essential for such a transformative technology. The University of California and the Broad/Harvard/MIT systems are the two most prominent academic institutions involved in the research and development of CRISPR/Cas. Here we present a model of co-funding networks for CRISPR/Cas research at these institutions, using funding acknowledgments to build connections. We map papers representing 95% of citations on CRISPR/Cas from these institutions grouped by the stage each represents in the research translation process (as a biological phenomenon, as a research tool, as a set of technologies, and applications of that technology), and use a novel technique to analyse the relationships between the structures of the co-funding networks, the phase of research, and funding sources. The co-funding subnetworks were similar in that US government research funding played the decisive role in early stage research. Research at Broad/Harvard/MIT is also strongly supported by philanthropic/charitable organizations in later stages of the translation process, clustered around certain topics. Applications for CRISPR technologies were underrepresented, which bolsters findings on the preponderance of the US private sector in developing applications, and the disproportionate number of Chinese institutions filing patents for industrial and food systems applications. These network models raise fundamental questions about the role of the state in supporting breakthrough innovations, risk, reward, and the influence of the private sector and philanthropy over the trajectory of transformative technologies.

Source: arxiv.org

Is the cultural evolution of technology cumulative or combinatorial?

Explanations of human technology often point to both its cumulative and combinatorial character. Using a novel computational framework, where individual agents attempt to solve problems by modifying, combining and transmitting technologies in an open-ended search space, this paper re-evaluates two prominent explanations for the cultural evolution of technology: that humans are equipped with (i) social learning mechanisms for minimizing information loss during transmission, and (ii) creative mechanisms for generating novel technologies via combinatorial innovation. Here, both information loss and combinatorial innovation are introduced as parameters in the model, and then manipulated to approximate situations where technological evolution is either more cumulative or combinatorial. Compared to existing models, which tend to marginalize the role of purposeful problem-solving, this approach allows for indefinite growth in complexity while directly simulating constraints from history and computation. The findings show that minimizing information loss is only required when the dynamics are strongly cumulative and characterised by incremental innovation. Contrary to previous findings, when agents are equipped with a capacity for combinatorial innovation, low levels of information loss are neither necessary nor sufficient for populations to solve increasingly complex problems. Instead, higher levels of information loss are advantageous for unmasking the potential for combinatorial innovation. This points to a parsimonious explanation for the cultural evolution of technology without invoking separate mechanisms of stability and creativity.

Source: osf.io

Entropy | Special Issue : Complexity and Evolution

The understanding of evolutionary processes is one the most important issues of scientific enquiry of this century. Scientific thinking in twentieth century witnessed the overwhelming power of the evolutionary paradigm. It not only solidified the foundations of diverse areas such as cell biology, ecology, and economics, but also fostered the development of several mathematical and computational tools to model and simulate how evolutionary processes take place.

Besides the application of the evolutionary paradigm and the discovery of the evolutionary features for diverse processes, there is another interesting aspect which touches upon the emergence of novel evolutionary processes. Generally, the emergence of an evolutionary process requires a complex transition between a prior form where no evolutionary process is undergoing and a posterior form where the evolutionary process has been triggered. Most advanced methods to understand the emergence of evolutionary processes require the consideration of systemic features such as self-organization, resilience, and contextuality, among others.

Source: www.mdpi.com