Catálogo de publicaciones - libros

To study how organism responds to periodic stimulation is meaningful since it may be an approach to an elementary capacity of time memory and learning in chronological events. We reported here that the ability of time memory found in true slime mold was also found in a protozoan ciliate, and a green plant . Stimulation of temperature or light was repeated several times in a regular period, and the creature anticipated the next timing of stimulation. After the anticipatory behavior disappeared some time later, another single stimulation triggered recalling of periodicity of the previous stimulation. We discuss that the observed capacity is expected to be common in a range of species as the similar capacity has been reported in true slime mold . The observed responses were, however, dependent of individual of organism and a wide range of different responses was observed. We need an extensive study of both experimental characterization and mathematical modeling of ethological dynamics. environment.

The giant amoeba-like plasmodia of is able to solve the shortest path through a maze and construct near optimal functional networks between multiple, spatially distributed food-sources. These phenomena are interesting as they provide clues to potential biological computational algorithms that operate in a de-centralized, single-celled system. We report here some factors that can affect path-finding through networks. These findings help us to understand more generally how the organism tries to establish an optimal set of paths in more complex environments and how this behaviour can be captured in relatively simple algorithms.

I review recent studies on distributions and fluctuations for personal-income and firm-size in real-economy by using recently available large-data. Specifically explained are Pareto-Zipf laws, Gibrat’s law and detailed-balance, and the fact that they are mutually related in a simple way. These patterns and shapes are not “laws” in physics, but can break down in abnormal situations such as bubble-collapse. These findings provide an important foundation of phenomenology for real-economy. The expression “aggregate calculation” nicely fits into the paradigm of this workshop.

We will begin with a simplified view of systems biology and synthetic biology. Systems biology extracts information from life, while synthetic biology converts information to reality. This cycle allows the co-evolution of life and information, and accelerates the evolution of both. Additionally, the field of molecular robotics has recently emerged. This field is attempting to implement artificial life using biological molecules. We foresee that molecular robots will interface information and life, and the distinction among information, life and artificial life will eventually become a blur. Once molecular robots gain the ability to evolve, then co-evolution of the three will lead to a new stage of intelligence.

investigates and models computational techniques inspired by nature and attempts to understand natural phenomena as information processing. In this position paper, we consider harness the nature for computation, from the perspective of natural computing. We investigated facsimile computational models of self- organization in nature, and identified dissipation of information flow as a common mechanism, where intermediate information is produced through interactions and consumed through evoking novel interactions. Based on this mechanism, we propose the concept of a harness: an indirect controlling method for natural systems. We realize this concept through a computational model, and discuss how this concept has already been successfully applied in medical and ecological science.

In this paper, we first depict a short history of arts and make clear that current critical artworks are not successor to original fine arts. Then, we acknowledge that some key points of fine arts remain in natural computing, especially in the idea of ‘harnessing.’ Finally, we re-evaluate artworks from the point of view of natural computing, especially from the idea of ‘harness’ in natural computing. We further suggest the possibility of making a new lineage, for example, from horticulture in the 18 century through land art to the idea of harnessing in natural computing, and to expect new successors to fine arts in extension of this lineage.

Inference in Gene Regulatory Networks remains an important problem in Molecular Biology. Many models have been proposed to model the relationships within genes in a DNA chain. Many of these models use Evolutionary Techniques to find the best parameters of specific DNA motifs.

In this work, we compare the popular S-System using a powerful evolutionary technique, DPSO, and the novel Recursive Neural Network model, using clustered (PBIL). We will use the SOS network for to do the comparison to finally show how they fare against other techniques in the area of (GRN) inference.

In this work we used the decoupled version of the recurrent neural network (RNN) model for gene network inference from gene expression data. In the decoupled version, the global problem of estimating the full set of parameters for the complete network is divided into several sub-problems each of which corresponds to estimating the parameters associated with a single gene. Thus, the decoupling of the model decreases the problem dimensionality and makes the reconstruction of larger networks more feasible from the point of algorithmic perspective. We applied a well established evolutionary algorithm called differential evolution for inferring the underlying network structure as well as the regulatory parameters. We investigated the effectiveness of the reconstruction mechanism in analyzing the gene expression data collected from both synthetic and real gene networks. The proposed method was successful in inferring important gene interactions from expression profiles.

In the field of synthetic biology, genetic networks are designed by combining well-characterized genetic parts, similar to electronic circuits. Such gene networks are called synthetic genetic circuits. The design approach for synthetic genetic circuits is based on mathematical modeling and numerical simulation. The approach allows the realization of various cellular functions. However, unavoidable differences in the initial states or fluctuations of the gene expression in cells have prevented the precise prediction and control of cellular behavior. Therefore, the design of synthetic genetic circuits is not sufficient, and the dynamic control of the circuits is also required. In this report, we provide examples of synthetic circuit designs and the control of synthetic biological systems, as well as perspectives on design and control.

Computational aesthetics already has had a long history. As early as 1928, G.D. Birkhoff introduced the concept of the aesthetic measure (M) and defined it as the ratio between order (O) and complexity (C): M = O/C. In Japan, in September 1964, art philosopher H. Kawano published the first computer-generated works in (see the Website [] of the exhibition: “Hiroshi Kawano –The Philosopher at the Computer,” 2012, ZKM, Karlsruhe, Germany).