Norman H. Packard

Topology and evolution of technology innovation networks.

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the U.S. Patent and Trademark Office. We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such an attachment kernel is shared by scientific citation networks, thus indicating a universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.

Living technology: Exploiting life's principles in technology

The concept of living technologythat is, technology that is based on the powerful core features of lifeis explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems. We define primary (secondary) living technology according as key material components and core systems are not (are) derived from living organisms. Primary living technology is currently emerging, distinctive, and potentially powerful, motivating this review. We trace living technologys connections with artificial life (soft, hard, and wet), synthetic biology (top-down and bottom-up), and the convergence of nano-, bio-, information, and cognitive (NBIC) technologies. We end with a brief look at the social and ethical questions generated by the prospect of living technology.

Automated Discovery of Novel Drug Formulations Using Predictive Iterated High Throughput Experimentation

Background We consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library) that interact nonlinearly and synergistically in ways that cannot be predicted from first principles. Methodology/Principal Findings The optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE), that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop. Conclusions We demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.

Coping with complexity: Machine learning optimization of cell‐free protein synthesis

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell‐free protein synthesis may be improved through a series of iterated high‐throughput experiments guided by a machine‐learning algorithm implementing a form of evolutionary design of experiments (Evo‐DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement. Biotechnol. Bioeng. 2011;108:2218–2228.

Introduction to recent developments in living technology

When the scientific and technological fruits of artificial life are embodied in technology with real practical use, sometimes the result can properly be called living technology [6]. Technology today is becoming increasingly lifelike, and there has recently been increasing foundational discussion of the broader scientific, socioeconomic, cultural, and ethical implications of living technology (e.g., [5, 7]). This special issue of Artificial Life describes recent developments in living technology, and samples current progress and applications in the works. The scientific core of the volume consists of seven articles describing new advances toward living technology. The volume also contains four articles about living technologyʼs broader social, ethical, and political implications. Living technology is most simply defined as technology that is alive, but it is convenient to require that such technology furthermore be useful because of being lifelike [6] and not be a simple variant of existing life. We will call something lifelike if it has one or more of lifeʼs characteristic properties. Although there is controversy about the nature of life [4], there is a rough consensus about the characteristic properties exhibited by all typical living beings. Many also agree that a subset