Narrow Results

In this paper we analyze the replication properties of additive or parity Cellular Automata (CA). The objective of the paper is twofold. Firstly, to review and extend the existing results in the cases of one- and two-dimensional CA. Secondly, to report a general result that states the replication properties of an n-dimensional CA, whose evolution law depends on an arbitrarym number of neighbor cells.

Graph automata define symbol dynamics of graph structures, which are capable of generating structures in addition to describing state transition. Rules of the graph automata are uniform and are therefore suitable for evolutionary computation. This paper shows that various self-replications are possible in graph automata, and that evolutionary computation is applicable to automatic rule generation to obtain self-replicating patterns of graph automata.

Among reaction–diffusion systems showing Turing patterns, the diffusive Gray–Scott model [Pearson, 1993], stands out by showing self-replicating patterns (spots), which makes it the ideal simple model for developmental research. A first study of the influence of noise in the Gray–Scott model was performed by Lesmes et al. [2003] concluding that there exists an optimal noise intensity for which spot multiplication is maximal. Here we show in detail the transition from nonspotlike to spotlike pattern, with the identification of a wide range of noise intensities instead of an optimal value for which this transition occurs. Additional studies also reveal that noise produces a shift and a shrinkage of the regions of spatial patterns in the parameters space, without introducing qualitative changes to the diagram.

A particular type of localized structure in a prototypical model for population dynamics interaction is studied. The model considers cooperative and competitive interaction among the individuals. Interaction at distance (or nonlocal interaction) and a simple random walk for the motion of the individuals are included. The system exhibits the formation of a periodic cellular pattern in some region of its parameter space. Inside this parameter region, it is possible to observe the localization of a single cell from the cellular pattern into an unpopulated background. The stability of this localized structure is discussed, as well as the destabilization process that gives rise to its own self-replication, inducing the propagation of the cellular pattern. The long distance interaction between these localized structures is also studied which results in a mutual repulsion.

Template-dependent replication at the molecular level is the basis of reproduction in nature. A detailed understanding of the peculiarities of the chemical reaction kinetics associated with replication processes is therefore an indispensible prerequisite for any understanding of evolution at the molecular level. Networks of interacting self-replicating species can give rise to a wealth of different dynamical phenomena, from competitive exclusion to permanent coexistence, from global stability to multi-stability and chaotic dynamics. Nevertheless, there are some general principles that govern their overall behavior. We focus on the question to what extent the dynamics of replication can explain the accumulation of genetic information that eventually leads to the emergence of the first cell and hence the origin of life as we know it. A large class of ligation-based replication systems, which includes the experimentally available model systems for template directed self-replication, is of particular interest because its dynamics bridges the gap between the survival of a single fittest species to the global coexistence of everthing. In this intermediate regime the selection is weak enough to allow the coexistence of genetically unrelated replicators and strong enough to limit the accumulation of disfunctional mutants.

We recently formulated an approach to representing structures in cellular automata (CA) spaces, and the rules that govern cell state changes, that is amenable to manipulation by genetic programming (GP). Using this approach, it is possible to efficiently generate self-replicating configurations for fairly arbitrary initial structures. Here, we investigate the properties of self-replicating systems produced using GP in this fashion as the initial configuration's size, shape, symmetry, allowable states, and other factors are systematically varied. We find that the number of GP generations, computation time, and number of resulting rules required by an arbitrary structure to self-replicate are positively and jointly correlated with the number of components, configuration shape, and allowable states in the initial configuration, but inversely correlated with the presence of repeated components, repeated sub-structures, and/or symmetric sub-structures. We conclude that GP can be used as a "replicator factory" to produce a wide range of self-replicating CA configurations, and that the properties of the resulting replicators can be predicted in part a priori. The rules controlling self-replication that are created by GP generally differ from those created manually in past CA studies.

This work aims to consider simplified models of protocells in order to describe their general behaviors. The advantage of the modelling approach is that the early protocells of life-forms on Earth are not reproduced in the present time. However, the problem is considered as a right track to understand the origin of life as well as to work with more objective synthesis of new drugs.

Knot and link diagrams are used to represent nonstandard sets, and to represent the formalism of combinatory logic (lambda calculus). These diagrammatics create a two-way street between the topology of knots and links in three dimensional space and key considerations in the foundations of mathematics.

A self-replicating modular robot has been developed that could operate with white noise input. The robot moves randomly around producing a Brownian style motion. We examine the influence of the initial conditions to the results of self-replication. We train a Feed-Forward Neural Network using back propagation to simulate the process results. We verify the approximation capabilities of the method using experimental data.