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Filisetti A.,European Center for Living Technology | Graudenzi A.,European Center for Living Technology | Serra R.,European Center for Living Technology | Serra R.,University of Modena and Reggio Emilia | And 7 more authors.
Theory in Biosciences

Autocatalytic cycles are rather widespread in nature and in several theoretical models of catalytic reaction networks their emergence is hypothesized to be inevitable when the network is or becomes sufficiently complex. Nevertheless, the emergence of autocatalytic cycles has been never observed in wet laboratory experiments. Here, we present a novel model of catalytic reaction networks with the explicit goal of filling the gap between theoretical predictions and experimental findings. The model is based on previous study of Kauffman, with new features in the introduction of a stochastic algorithm to describe the dynamics and in the possibility to increase the number of elements and reactions according to the dynamical evolution of the system. Furthermore, the introduction of a temporal threshold allows the detection of cycles even in our context of a stochastic model with asynchronous update. In this study, we describe the model and present results concerning the effect on the overall dynamics of varying (a) the average residence time of the elements in the reactor, (b) both the composition of the firing disk and the concentration of the molecules belonging to it, (c) the composition of the incoming flux. © Springer-Verlag 2011. Source

Filisetti A.,European Center for Living Technology | Graudenzi A.,European Center for Living Technology | Serra R.,European Center for Living Technology | Serra R.,University of Modena and Reggio Emilia | And 10 more authors.
Journal of Systems Chemistry

Autocatalytic cycles are rather common in biological systems and they might have played a major role in the transition from non-living to living systems. Several theoretical models have been proposed to address the experimentalists during the investigation of this issue and most of them describe a phase transition depending upon the level of heterogeneity of the chemical soup. Nevertheless, it is well known that reproducing the emergence of autocatalytic sets in wet laboratories is a hard task. Understanding the rationale at the basis of such a mismatch between theoretical predictions and experimental observations is therefore of fundamental importance. We here introduce a novel stochastic model of catalytic reaction networks, in order to investigate the emergence of autocatalytic cycles, sensibly considering the importance of noise, of small-number effects and the possible growth of the number of different elements in the system. Furthermore, the introduction of a temporal threshold that defines how long a specific reaction is kept in the reaction graph allows to univocally define cycles also within an asynchronous framework. The foremost analyses have been focused on the study of the variation of the composition of the incoming flux. It was possible to show that the activity of the system is enhanced, with particular regard to the emergence of autocatalytic sets, if a larger number of different elements is present in the incoming flux, while the specific length of the species seems to entail minor effects on the overall dynamics. © 2011 Filisetti et al; licensee Chemistry Central Ltd. Source

Buchanan A.,Reed College | Packard N.H.,ProtoLife Inc. | Bedau M.A.,Reed College
Artificial Life

We argue that technology changes over time by an evolutionary process that is similar in important respects to biological evolution. The process is adaptive in the sense that technologies are selected because of their specific adaptive value and not at random, but this adaptive evolutionary process differs from the Darwinian process of random variation followed by natural selection. We find evidence for the adaptive evolution of technology in the US patent record, specifically, the public bibliographic information of all utility patents issued in the United States from 1976 through 2010. Patents record certain innovations in the evolution of technology. The 1976-2010 patent record is huge, containing almost four million patents. We use a patent's incoming citations to measure its impact on subsequent patented innovations. Weighting innovative impact by the dissimilarity between parent and child technologies reveals that many of the most fecund inventions are door-opening technologies that spawn innovations in widely diverse categories. © 2011 Massachusetts Institute of Technology. Source

Shaw R.S.,ProtoLife Inc. | Shaw R.S.,Max Planck Institute for Dynamics and Self-Organization | Packard N.H.,ProtoLife Inc. | Packard N.H.,European Center for Living Technology | Packard N.H.,Santa Fe Institute
Physical Review Letters

A concentration difference of particles across a membrane perforated by pores will induce a diffusive flux. If the diffusing objects are of the same length scale as the pores, diffusion may not be simple; objects can move into the pore in a configuration that requires them to back up in order to continue forward. A configuration that blocks flow through the pore may be statistically preferred, an attracting metastable state of the system. This effect is purely kinetic, and not dependent on potentials, friction, or dissipation. We discuss several geometries which generate this effect, and introduce a heuristic model which captures the qualitative features. © 2010 The American Physical Society. Source

Cawse J.N.,ProtoLife Inc. | Cawse J.N.,Cawse and Effect LLC | Gazzola G.,ProtoLife Inc. | Gazzola G.,European Center for Living Technology | Packard N.,ProtoLife Inc.
Catalysis Today

As the pace of experimentation in materials science and catalysis has increased, experimental tactics and strategies have had to adapt to meet the demands of goals of experimentalists, and the spaces they explore. This pace has increased from runs/year to runs/day and sometimes to runs/minute in high-throughput experimentation. Although much of this capacity is used to simply speed up conventional experimental designs, the leading-edge application is discovery of low-probability, high-value occurrences (hits) by searching extensive, complex experimental spaces. Conventional design of experiments (DoE) is not capable of dealing with these issues. Instead, more advanced experimental tactics and strategies must be implemented. After introducing the elements that make an experimental campaign complex, here we present a novel statistical model-based evolutionary experimental strategy and apply it to the optimization of a family of artificial complex systems. With our experiments, we show that such a strategy may significantly reduce the experimental effort required for finding the optima compared to other state-of-the-art evolutionary strategies. © 2010 Elsevier B.V. Source

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