There have been many attempts for realization of emergent computing, but the notion of emergent computing is still ambiguous. In an open system, emergence and an error cannot be specified distinctly, because they are dependent on the dis-equilibration process between local and global behaviors. To manifest such an aspect, we implement a Boolean gate as a biological device made of slime mold Physarum polycephalum. A Physarum (slime mold) Boolean gate could be an internally instable machine, while it has the potential for emergent computing. First, we examined whether Physarum Boolean gate works properly, and then examined its behaviors when the gate is collapsed in terms of hardware. The behavior of Physarum changes and self-repairing computing is achieved as a result. The self-repairing against internal failure is one of attributes of emergent and robust computing.

Robust and emergent Physarum logical-computing

This is a comprehensive (and friendly) introduction to membrane computing (MC), meant to offer both computer scientists and non-computer scientists an up-to-date overview of the field. That is why the set of notions introduced here is rather large, but the presentation is informal, without proofs and with rigorous definitions given only for the basic types of P systems — symbol object P systems with multiset rewriting rules, systems with symport/antiport rules, systems with string objects, tissue-like P systems, and neural-like P systems. Besides a list of (biologically inspired or mathematically motivated) ingredients/features which can be used in systems of these types, we also mention a series of results, as well as a series of research trends and topics.

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Introduction to Membrane Computing

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future.

https://www.embopress.org/doi/pdf/10.1038/msb.2011.78

Algorithms in nature: the convergence of systems biology and computational thinking.

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

https://link.springer.com/content/pdf/10.1186%2F1471-2105-11-307.pdf

Rule-based spatial modeling with diffusing, geometrically constrained molecules

/pdf/molecular-computation-by-dna-hairpin-formation-ih02p4vm80.pdf

康文榊原

Papers

G. Paun, G. Rozenberg and A. Salomaa : "DNA Computing-New Computing Paradigms", Springer-Verlag (1998)

康文 榊原

Papers

G. Paun, G. Rozenberg and A. Salomaa : "DNA Computing-New Computing Paradigms", Springer-Verlag (1998)

康文榊原