Search

The monograph discusses models of synthetic protocells, which are cell-like structures obtained from non-living matter endowed with some rudimentary kind of metabolism and genetics, but much simpler than biological cells. They should grow and proliferate, generating offsprings that resemble in some way the parent protocells with some variation, so that selection may take place. Sustainable protocell populations have not yet been obtained experimentally and mathematical models are therefore extremely important to address key questions concerning their synthesis and behavior. Different protocell “architectures” have been proposed and high-level abstract models like those that are presented in this book are particularly relevant to gain a better understanding of the different properites. These models are able to treat all the major dynamical phenomena in a unified framework, so they can be seen as “virtual laboratories” for protocell research. Particular attention is paid to the problem of synchronization of the fission rate of the whole protocell and the duplication rate of its'protogenetic'material, which is shown to be an emergent property that spontaneously develops in successive generations.The book is of interest for a broad range of scientists working in soft matter physics, chemistry and biology, interested in the role protocells may play on the development of new technologies with medical, environmental and industrial applications as well as scientists interested in the origin of life.

This volume describes our intellectual path from the physics of complex sys­ tems to the science of artificial cognitive systems. It was exciting to discover that many of the concepts and methods which succeed in describing the self­ organizing phenomena of the physical world are relevant also for understand­ ing cognitive processes. Several nonlinear physicists have felt the fascination of such discovery in recent years. In this volume, we will limit our discussion to artificial cognitive systems, without attempting to model either the cognitive behaviour or the nervous structure of humans or animals. On the one hand, such artificial systems are important per se; on the other hand, it can be expected that their study will shed light on some general principles which are relevant also to biological cognitive systems. The main purpose of this volume is to show that nonlinear dynamical systems have several properties which make them particularly attractive for reaching some of the goals of artificial intelligence. The enthusiasm which was mentioned above must however be qualified by a critical consideration of the limitations of the dynamical systems approach. Understanding cognitive processes is a tremendous scientific challenge, and the achievements reached so far allow no single method to claim that it is the only valid one. In particular, the approach based upon nonlinear dynamical systems, which is our main topic, is still in an early stage of development.