Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IOP Publishing  

Abstract

In recent years, with the widespread dissemination of the concept of anti-racial discrimination, various countries have introduced a series of welfare policies for ethnic minorities, trying to control population apartheid to alleviate the generation of racial conflicts, and many achievements have been made. However, apartheid seems to have rebounded recently. This paper proposes the Schelling model with welfare policies to analyze the reasons for the rebound phenomenon and propose possible solutions.

DOI： 10.1088/1755-1315/608/1/012018

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Other Link： https://iopscience.iop.org/article/10.1088/1755-1315/608/1/012018

Language：English   Publisher：Chem-Bio Informatics Society  

<p>There are limitations in interactions with molecular objects in laboratory experiments due to the very small size of the objects. Common media to show the experimental results of molecular objects is still lack of observer interaction to understand it intuitively. In order to overcome this lack of interaction, this research takes tensegrity representation of molecular objects reproducing experimental results and creates interactive 3D objects to be presented in a virtual reality (VR) environment. The tensegrity representation enables us to enhance the interaction experience with the natural user interface with haptic technology and hand tracking controller. A particle simulation system that utilizes multiple GPUs resources is used to fulfill haptic VR requirements. We developed a unified particle object model using springs and particles which we call anchors which act as tensegrity structure of the object to support conformation of filament-type objects such as microtubules. Some object parameters can be set to match the flexural rigidity of the object with some experimental results. The bending shape of the object is evaluated using the classic bending equation and the results show high compatibility. Viscoelastic behavior also shows similarities with the viscosity reported in other studies. The object's flexural rigidity can be adjusted to match the target value with the direction of the prediction equation. The object model provides a better insight about molecular objects with natural and real-time interactions to provide a more intuitive understanding with the molecular objects presented. The results show that this model can also be applied to any filament-type or rod-like molecular object.</p>

DOI： 10.1273/cbij.20.19

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(1) Background: The folate receptor (FR) is a target for cancer treatment and detection. Expression of the FR is restricted in normal cells but overexpressed in many types of tumors. Folate was conjugated with peptides for enhancing binding affinity to the FR. (2) Materials and Methods: For conjugation, folate was coupled with propargyl or dibenzocyclooctyne, and 4-azidophenylalanine was introduced in peptides for "click" reactions. We measured binding kinetics including the rate constants of association (ka) and dissociation (kd) of folate-peptide conjugates with purified FR by biolayer interferometry. After optimization of the conditions for the click reaction, we successfully conjugated folate with designed peptides. (3) Results: The binding affinity, indicated by the equilibrium dissociation constant (KD), of folate toward the FR was enhanced by peptide conjugation. The enhanced FR binding affinity by peptide conjugation is a result of an increase in the number of interaction sites. (4) Conclusion: Such peptide-ligand conjugates will be important in the design of ligands with higher affinity. These high affinity ligands can be useful for targeted drug delivery system.

DOI： 10.3390/ijms20092152

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Language：English   Publishing type：Research paper (scientific journal)  

Reporter genes have contributed to advancements in molecular biology. Binding of an upstream regulatory protein to a downstream reporter promoter allows quantification of the activity of the upstream protein produced from the corresponding gene. In studies of synthetic biology, analyses of reporter gene activities ensure control of the cell with synthetic genetic circuits, as achieved using a combination of in silico and in vivo experiments. However, unexpected effects of downstream reporter genes on upstream regulatory genes may interfere with in vivo observations. This phenomenon is termed as retroactivity. Using in silico and in vivo experiments, we found that a different copy number of regulatory protein-binding sites in a downstream gene altered the upstream dynamics, suggesting retroactivity of reporters in this synthetic genetic oscillator. Furthermore, by separating the two sources of retroactivity (titration of the component and competition for degradation), we showed that, in the dual-feedback oscillator, the level of the fluorescent protein reporter competing for degradation with the circuits' components is important for the stability of the oscillations. Altogether, our results indicate that the selection of reporter promoters using a combination of in silico and in vivo experiments is essential for the advanced design of genetic circuits.

DOI： 10.3390/life9010030

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Language：English   Publishing type：Research paper (scientific journal)  

Although conjugation with polyethylene glycol (PEGylation) improves the pharmacokinetics of therapeutic proteins, it drastically decreases their bioactivity. Site-specific PEGylation counters the reduction in bioactivity, but developing PEGylated proteins with equivalent bioactivity to that of their unmodified counterparts remains challenging. This study aimed to generate PEGylated proteins with equivalent bioactivity to that of unmodified counterparts. Using interferon (IFN) as a model protein, a highly bioactive Lys-deficient protein variant generated using our unique directed evolution methods enables the design of a site-specific di-PEGylated protein. Antiviral activity of our di-PEGylated IFN was similar to that of unmodified IFN-α2b. The di-PEGylated IFN exhibited 3.0-fold greater antiviral activity than that of a commercial PEGylated IFN. Moreover, our di-PEGylated IFN showed higher in vitro and in vivo stability than those of unmodified IFN-α2b. Hence, we propose that highly bioactive Lys-deficient proteins solve the limitation of conventional PEGylation with respect to the reduction in bioactivity of PEGylated proteins.

DOI： 10.1021/acssynbio.8b00188

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Language：English   Publishing type：Research paper (scientific journal)  

Human folate receptors (hFRα and hFRβ) are membrane proteins anchored to the cell surface by glycosylphosphatidylinositol. They play an important role in cell growth by taking up folate for de novo synthesis of purines and methylation of DNA, lipids, and proteins. Thus, controlling folate uptake through hFRs may lead to the development of anti-cancer drugs. Development of hFRs-targeting drug requires a large amount of hFRs. However, it is difficult to prepare active forms of hFRs from prokaryotic cells because of their high content of cysteine residues that form disulfide bonds. Here, we prepared active forms of hFRα and hFRβ from inclusion bodies of Escherichia coli. The crucial steps in our preparation were intensive washing of the inclusion bodies to remove impurities derived from E. coli and gradual dropping of solubilized hFRs into refolding buffers to correctly reform disulfide bonds. The binding activity of prepared hFRs to folate was confirmed by biolayer interferometry measurements. Finally, we successfully prepared the active form of 2.52 mg hFRα and 2.4 mg hFRβ from 10 g of E. coli cell bodies.

DOI： 10.1016/j.pep.2018.04.006

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Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

DOI： 10.1371/journal.pone.0191650

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Association for Computing Machinery  

Among the various biological devices developed and characterized in synthetic biology, light-sensing biological devices can serve as an input-output system owing to their light modulation property. The well-characterized devices in living systems are useful for modulating cellular sensing and transducing information. In this study, we examined short pulse responsiveness of a light-sensing two-component system (TCS), Cph8-OmpR, which was generated by replacing the sensor domain of the EnvZ-OmpR osmoregulatory system with the light sensor Cph1. We varied the input pulse width of the Cph8-OmpR system and found that an input width of &lt
1 s was sufficient to alter the accumulation of a reporter gene upregulated by Cph8 phosphorylation of OmpR. Based on this result and the mathematical model showing that the timescale for the upstream Cph8-activity transition was much faster than that of downstream gene expression, we evaluated the merit of a TCS with such an unbalanced cascade. Our mathematical simulation of a cascade TCS suggests that high-frequency noise arising from fast transitions in kinase activity was attenuated throughout the cascade reaction. In terms of noise attenuation, these results can contribute to analyze biological cascade systems with the balance of reaction rates in each process.

DOI： 10.1145/3029375.3029377

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

We developed a reaction system to generate multiple single-stranded DNA species at a physiological temperature for controlling the operation of DNA nanomachines. In this reaction system, cascading DNA generation is arbitrarily programmed by permutation and altering the combinations of template DNA sequences in a modular fashion. Because the dissociation of generated DNA strands from their templates is fully dependent on the strand displacement activity of DNA polymerase, generation and subsequent hybridization of DNA strands can be implemented in a one-pot reaction at the reaction temperature. We experimentally confirmed the generation and hybridization of DNA strands at a temperature remarkably lower than the melting temperature by monitoring the fluorescence change caused by the structural transition of molecular beacons as a simple DNA nanomachine operation. Then, we demonstrated the versatility and programmability of the cascading DNA generation up to three layers. By integrating the proposed DNA generation reaction with various types of DNA nanomachines, an intelligent molecular robotic system is expected to be achieved.

DOI： 10.1007/s00354-015-0304-5

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SOC BIOSCIENCE BIOENGINEERING JAPAN  

Photoresponsive peptide aptamer to glutathione-immobilized microbeads was in vitro selected using ribosome display incorporated with tRNA carrying an amino acid coupled with an azobenzene. (C) 2014, The Society for Biotechnology, Japan. All rights reserved.

DOI： 10.1016/j.jbiosc.2014.06.018

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ROYAL SOC CHEMISTRY  

Polyethylene glycol (PEG) of different lengths was genetically incorporated into the backbone of a polypeptide using stop-anticodon and frameshift anticodon-containing tRNAs, which were acylated with PEG-containing amino acids.

DOI： 10.1039/c5cc04486c

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

Background: In order to understand and regulate complex genetic networks in living cells, it is important to build simple and well-defined genetic circuits. We designed such circuits using a synthetic biology approach that included mathematical modeling and simulation, with a focus on the effects by which downstream reporter genes are involved in the regulation of synthetic genetic circuits.
Results: Our results indicated that downstream genes exert two main effects on genes involved in the regulation of synthetic genetic circuits: (1) competition for regulatory proteins and (2) protein degradation in the cell.
Conclusions: Our findings regarding the effects of downstream genes on regulatory genes and the role of impedance in driving large-scale and complex genetic circuits may facilitate the design of more accurate genetic circuits. This design will have wide applications in future studies of systems and synthetic biology.

DOI： 10.1186/1752-0509-8-S4-S4

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER CHEMICAL SOC  

Control of the cell-type ratio in multistable systems requires wide-range control of the initial states of cells. Here, using a synthetic circuit in E. coli, we describe the use of a simple gene-overexpression system combined with a bistable toggle switch, for the purposes of enabling the wide-range control of cellular states and thus generating arbitrary cell-type ratios. Theoretically, overexpression induction temporarily alters the bistable system to a monostable system, in which the location of the single steady state of cells can be manipulated over a wide range by regulating the overexpression levels. This induced cellular state becomes the initial state of the basal bistable system upon overexpression cessation, which restores the original bistable system. We experimentally demonstrated that the overexpression induced a monomodal cell distribution, and subsequent overexpression withdrawal generated a bimodal distribution. Furthermore, as designed theoretically, regulating the overexpression levels by adjusting the concentrations of small molecules generated arbitrary cell-type ratios.

DOI： 10.1021/sb400102w

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Language：English   Publisher：The Biophysical Society of Japan General Incorporated Association  

DOI： 10.2142/biophys.54.S241_4

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Language：English   Publishing type：Research paper (scientific journal)  

The autonomous generation of phenotypic diversity in embryonic cell populations can be explained by Waddington's landscape. The landscape proposes that intra- and inter-cellular interactions mediate the generation of cellular diversity. Recently, we implemented, in a population of Escherichia coli, a synthetic diversification, which is governed by inter-cellular signaling mediated by acyl-homoserine lactone (AHL). The cells with the diversity generator diversified into two distinct cell states, "high" and "low," if all of the cells started from the low state. The ratio of the states after the diversification was affected by the velocity of autonomous signal accumulation, which depends on the cell density and the AHL production rate of individual cells. The dependency of the ratio on the initial cell density is reminiscent of the community effect, which is observed in animal development and is important for ES-cell differentiation. Therefore, it is worthwhile reviewing the roles of natural animal gene networks with similar topologies to the diversity generator design. The diversity generator design will also be the basis for a tool to direct cell fates on the population level in tissue engineering. Here, we discuss the tunability of the ratio of cell states by our synthetic circuit design.

DOI： 10.4161/cib.20310

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Language：Japanese   Publisher：The Institute of Electrical Engineers of Japan  

This article has no abstract.

DOI： 10.1541/ieejjournal.132.221

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Other Link： https://jlc.jst.go.jp/DN/JALC/10000032345?from=CiNii

Satoru Akama, Masayuki Yamamura, Takanori Kigawa, 2012, &#039;A Multiphysics Model of In Vitro Transcription Coupling Enzymatic Reaction and Precipitation Formation&#039;, &lt;i&gt;Biophysical Journal&lt;/i&gt;, vol. 102, no. 2

DOI： 10.1016/j.bpj.2011.12.014

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Language：English   Publisher：Chem-Bio Informatics Society  

Initial state-independent phenotypic diversification will be a powerful tool for directing cells to multiple phenotypes in practical situation, in which initial cellular states are unknown. In this study, we designed Symmetric Diversity Generator (SDG) for the initial state-independent phenotypic diversification, in which homogenous cells diversify into two phenotypes and the ratio of the phenotypes do not depend on the initial cellular state. The SDG consists of two mechanisms: an intracellular mutual inhibition by repressors and an intercellular activation of the repressor productions by intercellular activators that are expected to compensate imbalance of repressor concentrations and of intercellular activator concentrations. We computationally evaluated the initial state dependence of the SDG in terms of the ratio of the two phenotypes after the diversification, and found the SDG still has initial state dependence. For lower dependence, we designed two kinds of symmetric diversity generator focusing on degradation rate of activators and responsiveness of repressor productions to transcription factors, activators and repressors. Our computational evaluation suggests that the latter approach is much more promising than the former one because the intercellular activators can compensate the imbalance of the transcription factors in advance of response of repressor productions. The former approach would be used for improvement of robustness of other synthetic genetic circuits already designed.

DOI： 10.1273/cbij.12.39

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATL ACAD SCIENCES  

Phenotypic diversification of cells is crucial for developmental and regenerative processes in multicellular organisms. The diversification concept is described as the motion of marbles rolling down Waddington&apos;s landscape, in which the number of stable states changes as development proceeds. In contrast to this simple concept, the complexity of natural biomolecular processes prevents comprehension of their design principles. We have constructed, in Escherichia coli, a synthetic circuit with just four genes, which programs cells to autonomously diversify as the motion on the landscape through cell-cell communication. The circuit design was based on the combination of a bistable toggle switch with an intercellular signaling system. The cells with the circuit diversified into two distinct cell states, "high" and "low," in vivo and in silico, when all of the cells started from the low state. The synthetic diversification was affected by not only the shape of the landscape determined by the circuit design, which includes the synthesis rate of the signaling molecule, but also the number of cells in the experiments. This cell-number dependency is reminiscent of the " community effect": The fates of developing cells are determined by their number. Our synthetic circuit could be a model system for studying diversification and differentiation in higher organisms. Prospectively, further integrations of our circuit with different cellular functions will provide unique tools for directing cell fates on the population level in tissue engineering.

DOI： 10.1073/pnas.1105901108

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Language：English   Publisher：The Biophysical Society of Japan General Incorporated Association  

DOI： 10.2142/biophys.51.S25_1

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Language：English   Publishing type：Research paper (international conference proceedings)  

Optimization of reaction conditions for biocatalytic synthesis has been extensively studied as single-objective optimization (SOO) or multi-objective optimization (MOO). Most of the studies have focused only on maximization of single objectives such as the synthesis rate. In biocatalytic synthesis, however, it is highly probable that component concentrations selected as design variables contain large errors. For example, there can be a gap between the true and the assumed concentrations of enzymes because of their easy inactivation, leading to unexpected decrease in yield. Therefore, it is important to focus on the robustness of the synthesis as well as the enhancement of other objectives. In this paper, we apply an MOO method considering robustness for finding optimal reaction conditions of an in vitro RNA synthesis. This multi-objective robust optimization was performed by considering both the mean value and standard deviation of the yield as objective functions in the MOO problem and solving the problem with NSGA-II, a genetic algorithm. We first verified the effectiveness of our method and then conducted yield and cost optimization considering robustness. Thus, optimal reaction conditions with increased robustness could be successfully obtained.

DOI： 10.2316/P.2011.753-017

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

Background: Appropriate regulation of respective gene expressions is a bottleneck for the realization of artificial biological systems inside living cells. The modification of several promoter sequences is required to achieve appropriate regulation of the systems. However, a time-consuming process is required for the insertion of an operator, a binding site of a protein for gene expression, to the gene regulatory region of a plasmid. Thus, a standardized method for integrating operator sequences to the regulatory region of a plasmid is required.
Results: We developed a standardized method for integrating operator sequences to the regulatory region of a plasmid and constructed a synthetic promoter that functions as a genetic AND gate. By standardizing the regulatory region of a plasmid and the operator parts, we established a platform for modular assembly of the operator parts. Moreover, by assembling two different operator parts on the regulatory region, we constructed a regulatory device with an AND gate function.
Conclusions: We implemented a new standard to assemble operator parts for construction of functional genetic logic gates. The logic gates at the molecular scale have important implications for reprogramming cellular behavior.

DOI： 10.1186/1471-2164-11-S4-S16

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

In this work, we report the development and experimental validation of a coupled statistical thermodynamic model allowing prediction of the structural transitions executed by a novel DNA nanodevice, for quantitative operational design. The efficiency of target structure formation by this nanodevice, implemented with a bistable DNA molecule designed to transform between three distinct structures, is modeled by coupling the isolated equilibrium models for the individual structures. A peculiar behavior is predicted for this nanodevice, which forms the target structure within a limited temperature range by sensing thermal variations. The predicted thermal response is then validated via fluorescence measurements to quantitatively assess whether the nanodevice performs as designed. Agreement between predictions and experiment was substantial, with a 0.95 correlation for overall curve shape over a wide temperature range, from 30 http://www.w3.org/1999C. The obtained accuracy, which is comparable to that of conventional melting behavior prediction for DNA duplexes in isolation, ensures the applicability of the coupled model for illustrating general DNA reaction systems involving competitive duplex formation. Finally, tuning of the nanodevice using the current model towards design of a thermal band pass filter to control chemical circuits, as a novel function of DNA nanodevices is proposed.

DOI： 10.1093/nar/gkq250

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DOI： 10.1109/BIBE.2010.15

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Other Link： http://doi.ieeecomputersociety.org/10.1109/BIBE.2010.15

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER  

Whiplash PCR (WPCR), which implements self-directed operation, programmed within a single DNA molecule, is a potential candidate for both mathematical and biological applications. However, WPCR-based methods are known to suffer from a serious efficiency problem called back-hybridization (BH). Previously, we proposed and partially validated a new rule-protect operation to abolish BH. In this work, we experimentally demonstrate the ability of rule-protect to drive multi-step WPCR. Successful implementation of isothermal operation at physiological temperatures is an essential benchmark for biological applications. We also propose the use of rule-protect for external signalling to control computational operation. Consequently, signal-dependent self-directed operation, which is conceptually new to DNA computing, is achieved. The present architecture, provided with sensing ability, allows a composite system design layering computational reactions, and would be suitable for functioning as the central processing unit of this system.

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Language：English   Publishing type：Research paper (international conference proceedings)  

Protein engineering, developing novel proteins with a desired activity, has become increasingly important in many fields. This paper presents two studies in protein engineering: (i) a biological implementation of a genetic algorithm, with an observed in vitro evolution, and (ii) its preliminary computer simulation using a prototypical probabilistic model based on a random walk. The steady evolution of the fitness distribution of the mutant proteins that appeared in the biological experiments has provided some convincing evidence about the search behavior and the fitness landscape. The computer simulation and the simple probabilistic model have indicated their future potential for providing a practical alternative to the time-consuming manual operations in the biological experiments. Successful experimental results in the two studies have raised expectations of their further development and mutually beneficial interactions. Copyright 2009 ACM.

DOI： 10.1145/1569901.1569934

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

Recent progress in various related fields has engendered a new style of biology, named Synthetic Biology, which utilizes concepts from modern engineering to emulate specific cellular functions and their functional combinations. This paper presents an introduction to Synthetic Biology from various viewpoints. First, we survey the concepts and tools from Systems Science along with several issues on social impact. Then, we discuss the recent progress in Molecular Biology that supports Synthetic Biology.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

In this paper, we propose an autonomous molecular walking machine using DNA. This molecular machine follows a track of DNA equipped with many single-strand DNA stators arranged in a certain pattern. The molecular machine achieves autonomous walk by using a restriction enzyme as source of power. With a proposed machine we can control its moving direction and we can easily extend walking patterns in two or three dimensions. Combination of multiple legs and ssDNA stators can control the walking pattern. We designed and per-formed a series of feasibility study with molecular biology experiments.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

We developed an interface to enable feedback control for a methylationbased computing model, in which a bit string is represented by the methylated and unmethylated status of the specific locations on a DNA molecule. On construction of a reaction system for the computational purpose, it is problematic that an open loop system without feedback control is easy to lose the molecular variety required for computation. It is, thus, important for the methylation-based computing to achieve quantitative sensing for feedback control. Difference in methylation status can be converted into the sequence variation by the bisulfite reaction. As a consequence, distribution between methylated and unmethylated DNA molecules could be quantitatively monitored by combining the polymerase chain reaction (PCR) using methylation specific primers with quantitative PCR. In the present study, we experimentally investigated the feasibility of the proposed interface for controlling the population of a library of DNA registers that have distinct methylated patterns representing different bits. Result indicated that, quantitative measurement of population was successfully performed by discriminative amplification using the methylation-specific primer. This interface, which allows us to generate a homogenous or biased library as expectedly, would be useful for molecular evolutionary computation and molecular learning.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Early explosion of DNA computing to solve combinatorial problems is now shrinking by three hardness; (1) code set design, (2) scalability and (3) speed and reliability. This paper proposes an implementation of Aqueous memory molecules by using light responsive modification of DNAs and show a series of feasibility experiments. We expect to overcome three difficulties since Aqueous computing is code design free, DNA sequence provides arbitrary size of address space, and light responsive reaction is fast and reliable.

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

In this paper we address a new problem in reinforcement learning. Here we consider an agent that faces multiple learning tasks within its lifetime. The agent's objective is to maximize its total reward in the lifetime as well as a conventional return in each task. To realize this, it has to be endowed an important ability to keep its past learning experiences and utilize them for improving future learning performance. This time we try to phrase this problem formally. The central idea is to introduce an environmental class, BV-MDPs that is defined with the distribution of MDPs. As an approach to exploiting past learning experiences, we focus on statistics (mean and deviation) about the agent's value tables. The mean can be used as initial values of the table when a new task is presented. The deviation can be viewed as measuring reliability of the mean, and we utilize it in calculating priority of simulated backups. We conduct experiments in computer simulation to evaluate the effectiveness.

DOI： 10.1109/CIRA.2003.1222152

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Verlag  

DOI： 10.1007/3-540-45110-2_45

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Language：English   Publishing type：Research paper (international conference proceedings)  

Extracting biological importance from protein structures is extremely helpful to understand the molecular nature. Although methods for protein structure-based alignment have been hitherto proposed in a number of ways, each method focuses on a part of alignment possibility. We have developed a generic method for pairwise structure-based alignment utilizing the population search ability of a Real-coded Genetic Algorithm. Our method simultaneously optimizes vector-expressed local fragment posture and global atomic superposition. Here, we report comparative results derived from the proposed method and existing methods. The experiments use three protein pairs well studied and a number of pairs derived from diverse protein families. The results show that our method provides useful two-layer similarity and statistical significance at a time to be able to capture not only the remarkable difference between local alignment and global alignment but also biologically meaningful common folds and motifs. Interestingly, we unveiled a vague region in protein structure-function relationships. It may indicate the limit of using alpha-carbon backbones.

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Language：English   Publishing type：Research paper (international conference proceedings)  

In this paper, we try to consider interaction design for adaptation from the viewpoint of transfer of knowledge. Recent advancements in robotics are amazing, and their interaction processes with outside world (including human) are getting to be longer in time scale. We will investigate these matters in an abstract agent that faces multiple learning tasks within its lifetime, transferring past learning experiences to improve its performance. We formulize the multitask reinforcement learning problem at first, and then we present two ways of incorporating past learning experiences into the agent's learning algorithm. © 2003 IEEE.

DOI： 10.1109/ROMAN.2003.1251857

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE Computer Society  

The evolution of biological functions in protein molecules may take place on two layers that are local fragment and global domain conservations/mutations. Based on the fact that the three-dimensional structure of a protein activates its native function, this paper discusses acquiring such biological importance from comparing protein structures. Unlike the one-point search of existing methods based on various ideas, our approach utilizes the population search ability of real-coded genetic algorithm that is asynchronously parallelized. It may be useful to optimize this issue using a multiple objective evolutionary approach. In this work, we focus on maximizing two fitness functions because of the obvious trade-off. Our method as a generic structure-based alignment tool can compare all types of proteins on the two layers at a time. As the most advantageous fact, the genetic algorithm preserves local alignments as building blocks and reuses them for finding global alignments. This feature gives information on the connectivity of local fragments that often involve biological important parts, such as binding sites, active sites, etc. Robust optimization of our approach appears from experiment of protein pairs that are functionally and structurally similar/distinct. The results show that the proposed method is able to pull out the significant consideration to biological analyses. © 2003 IEEE.

DOI： 10.1109/CEC.2003.1299854

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In this paper, we propose a novel method of self-organizing formation. It is assumed that elements are not connected to each other, and they can move in continuous space. The objective is to arrange elements in certain spatial pattern like a crystal, and to make the outline of the group in desired shape. For this purpose, we proposed a method by using virtual springs among the elements. In this algorithm, an element generates virtual springs between neighbor element based on information how many other elements exist in neighborhood with a certain radius. Although the elements interact locally only by virtual springs, and they don&#039;t have global information at all, they form a shape much larger than the sensory radius. By simulation study, we confirmed convergence to a target shape from a random state in very high probability. This kind of algorithm gives a new principle of self-organizing formation, and its simplicity will be useful for design of self-assembling nano machines in future.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

In this paper, we introduce a novel signal element by using bacterial pheromones. In multicellular organism, every cell can communicate and exchange information with other cells. Bacteria also have such mechanisms. Enterococcus faecalis, one of the gram-positive bacteria, has a unique pheromone system. Male cells are stimulated by pheromones from female cells, and they give their plasmid to female cells through conjugation phenomenon. The variety of pheromones and their inducible activities of plasmid transfer inspire us that Enterococcus faecalis can serve as a pheromone-dependant DNA transporter. We show a design to realize logically controllable Information Gates by using Enterococcus faecalis and show an experimental plan. It is still on going project, but we can show the feasibility that bacterial pheromone system would provide alternative methodologies in molecular computing research.

DOI： 10.1007/3-540-48017-x_25

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

Reinforcement learning aims to adapt an agent to an unknown environment according to rewards, There are two issues to handle delayed reward and uncertainty, Q-learning is a representative reinforcement learning method. It is used in many works since it can learn an optimum policy. However, Q-learning needs numerous trials to converge to an optimum policy. If the target environments can be described in Markov decision processes, we can identify them from statistics of sensor-action pairs. When we build the correct environment model, vie can derive an optimum policy with the Policy Iteration Algorithm. Therefore, we can construct an optimum policy through identifying environments efficiently.
We separate the learning process into two phases: identifying an environment and determining an optimum policy. We propose the k-Certainty Exploration Method for identifying an environment, After that, an optimum policy is determined by the Policy Iteration Algorithm. We call a rule k-certainty if and only if it has been selected k times or more. The k-Certainty Exploration Method excepts any loop of rules that already achieve k-certainty. We show its effectiveness by comparing it with Q-learning in two experiments. One is Sutton's maze-like environment, the other is an original environment where an optimum policy varies according to a parameter. (C) 1997 Elsevier Science B.V.

DOI： 10.1016/S0004-3702(96)00062-8

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：社団法人人工知能学会  

Reinforcement learning is a kind of machine learning. It aims to adapt an agent to a given environment with a clue to rewards. Profit sharing (PS) can get rewards efficiently at an initial learning phase. However, it can not always learn an optimum policy that maximizes rewards per an action. Though Q-learning is guaranteed to obtain an optimum policy, it needs numerous trials to learn it. On Markov decision processes (MDPs), if a correct environment model is identified, we can derive an optimum policy by applying Policy Iteration Algorithm (PIA). As an efficient method for identifying MDPs, k-Certainty Exploration Method has been proposed. We consider that ideal reinforcement learning systems are to get some rewards even at an initial learning phase and to get mere rewards as the identification of environments proceeds. In this paper, we propose a unified learning system : MarcoPolo which considers both getting rewards by PS or PIA and identifying the environment by k-Certainty Exploration Method. MarcoPolo can realize any tradeoff between exploitation and exploration through the whole learning process. By applying MarcoPolo to an example, its basic performance is shown. Moreover, by applying it to Sutton&#039;s maze problem and its modified version, its feasibility on more realistic domains is shown.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：社団法人人工知能学会  

k-Certainty Exploration Method gives top priority for selection to an action whose number of selection is the fewest. However it doesn&#039;t consider any state-transition probability. Therefore, though it guarantees the rationality and the efficiency under deterministic Markov decision processes (MDPs), it doesn&#039;t always guarantee the rationality nor the efficiency under stochastic MDPs. In this paper, we propose l-Certainty Exploration Method which is an extension of k-Certainty Exploration Method to stochastic MDPs. We define reachability as a difference between the sampled number of any rule and the sampling number necessary to identify its rule structure by error e with confidence 1. l-Certainty Exploration Method realizes efficient identification of the environment through selecting prior to a rule whose reachability is the lowest. We show the superiority of l-Certainty Exploration Method compared with the other methods through an numerical example.

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Language：Japanese   Publisher：The Society of Instrument and Control Engineers  

DOI： 10.11499/sicejl1962.35.7_557

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Other Link： https://jlc.jst.go.jp/DN/JALC/00040862811?from=CiNii

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

In this paper, we propose a new genetic algorithm for job-shop scheduling problems. The proposed method uses a job sequence matrix. This paper introduces a new crossover, the job-based order crossover (JOX), which can preserve characteristics very well. JOX preserves the order of each job on all machines between parents and their children, taking account of the dependency among machines. Since the children generated by JOX are not always feasible, we propose a technique to transform them into active schedules by using the Giffler and Thompson method. Furthermore, we introduce a mutation for maintaining a diversity of population without disrupting characteristics. By applying the proposed method to Fisher's and Thompson's 10×10 and 20×5 problems, we show its usefulness.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：社団法人人工知能学会  

Reinforcement learning aims to adapt a system to an unkown environment according to rewards. There are two issues to handle delayed reward and uncertainty. Q-learning is a representative reinforcement learning method. It is used by many works since it can learn the optimum policy. However, Q-learning needs numerous trials to converge to the optimum policy. If target environments can be described in a Markov decision process, we can identify them from statistics of sensor-action pairs. When we build the correct environment model, we can derive the optimum policy with policy Iteration Algorithm. Therefore, we can construct the optimum policy through identifying environments efficiently. In this paper, we separate learning process into two phases ; identifying an environment and determining the optimum policy. We propose k-Certainty Exploration Method for identifying an environment. After that, the optimum policy is determined by Policy Iteration Algorithm. We call a rule is k-Certainty if and only if the number of selecting it is larger than k. k-Certainty Explolation Method suppresses any loop of rules that already achieve k-Ceratinty. We show its effect by comparing with Q-learning in two experiments. 0ne is under maze environment of Dyna, the other is the environment where the optimum policy varies according to a parameter.

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Language：Japanese   Publisher：The Robotics Society of Japan  

DOI： 10.7210/jrsj.13.57

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Power plant start-up scheduling is aimed mainly at minimizing the start-up time of both boiler and turbine, while limiting turbine rotor stresses to acceptable values. This problem has a number of local optima. In order to find the optimal start-up schedule efficiently, we apply population-based evolutionary optimization techniques - Genetic Algorithms (GA) - with our proposed 'enforcement operator.' The purpose of this enforcement operator is to limit the search space to those promising areas where the optimal solution is supposed to exist. As shown in this paper, aided with this enforcement operator, the search efficiency improves significantly as compared to a GA-based search without such operator. In addition, an optimal solution can be achieved, which reduces the start-up time by approximately 10%, or 20 minutes, relative to conventional methods.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：社団法人人工知能学会  

Reinforcement learning is a kind of machine learning. It aims to adapt a system to a given environment according to rewards. We consider profit sharing that is a representative reinforcement learning method. A rule sequence applied between reward and reward is called an episode. Profit sharing reinforce rules at each episode. A function that shares the reward between rules of an episode is called a reinforcement function. Conventional work has used ad hoc functions. This paper analyzes reinforcement functions theoretically. First, we examine what a reinforcement function is locally reasonable. We call a rule is ineffective if and only if it is on a detour for any episodes. It is locally reasonable that ineffective rules are suppressed than any effective rules. We have derived the necessary and sufficient condition to suppress any ineffective rules as following inequality ; &lt;LΣ&gt;^^^w___&lt;j=i&gt;&lt;f_&lt;i-1&gt; (i=1,…,W). where, L is the maximum number of conflicting effective rules, W is the maximum length of episodes, and f_j is the value of reinforcement for the j-th previous rule applied before the reward. We call this as the ineffective rule suppression theorem. We demonstrate that a profit sharing can learn ineffective rules when the condition is violated. Second, we examine whether reinforcement functions satisfying the condition are globally reasonable. We call a collection of effective rules as a rule selection plan if and only if it selects at most one effective rule per one state. It is globally reasonable that a plan gains reward continuously. We show that the condition is also necessary and sufficient to learn a rewardfull plan. We call this as the rewardfull plan acquisition theorem. We also demonstrate that a profit sharing can learn rewardless plans when the condition is violated.

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Though EBL (explanation based learning) is a useful framework for generating operational knowledge from an example, we must assume perfect domain theory and explicitly defined operationality criterion. This assumption is too severe in practical situation. This paper presents a topdown search over generalization hierarchy of common explanation structure for solving the imperfect theory problem in EBL, forcusing on an inconsistent problem. Common explanation structures are generated from prural primitive explanation structures and they are organized as a generalization hierarchy based on a concept of maximal covering which reflects on similarity among them. By topdown search over the generalization hierarchy, the best set of macro rules, which includes all positive examples and excludes all negatives, can be found. This method has been implemented on Prolog. Some experiments show its usefulness for valid macro rules under the imperfect domain theory.

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Other Link： http://id.nii.ac.jp/1001/00062137/

Language：English   Publisher：OXFORD UNIV PRESS  

Motivation: Large-scale biological analyses produce huge amounts of data. As a consequence, automation in the data analysis process is needed. Sample screening problems in NMR high-throughput protein structure analysis are the typical examples. Especially, screening by protein (1)H(15)N heteronuclear single quantum coherence (HSQC) spectra must be done quantitatively by a human expert. One popular solution for this problem is data mining. Machine learning methods can automatically extract rules and achieve high accuracy in prediction when a good quality training dataset is prepared. However, they tend to be a black box and the learned machines suffer the risk of overfitting to the dataset.
Results: We propose a model which evaluates HSQC spectra for feature construction. The model calculates similarity between the measured chemical shifts and those of a random coil peak model. We applied our feature construction method for the machine learning discrimination of folded protein HSQC spectra from unfolded ones, and compared our model-based features with those of conventional sequence-based features and image recognition features. The results revealed that our method has sufficient discrimination power and less overfits on training data, as compared to the other methods. In addition, our method succeeded reduction of input data complexity towards further investigation.

DOI： 10.1093/bioinformatics/btn345

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In this paper, we propose an autonomous molecular walking machine using DNA. This molecular machine follows a track of DNA equipped with many single-strand DNA stators arranged in a certain pattern. The molecular machine achieves autonomous walk by using a restriction enzyme as source of power. With a proposed machine we can control its moving direction and we can easily extend walking patterns in two or three dimensions. Combination of multiple legs and ssDNA stators can control the walking pattern. We designed and performed a series of feasibility study with computer simulation and molecular biology experiments. © Springer Science+Business Media B.V. 2008.

DOI： 10.1007/s11047-008-9077-9

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Recent progress in various related fields has engendered a new style of biology, named Synthetic Biology, which utilizes concepts from modern engineering to emulate specific cellular functions and their functional combinations. This paper presents an introduction to Synthetic Biology from various viewpoints. First, we survey the concepts and tools from Systems Science along with several issues on social impact. Then, we discuss the recent progress in Molecular Biology that supports Synthetic Biology. © 2008 Ohmsha, Ltd.

DOI： 10.1007/s00354-008-0050-z

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DOI： 10.1007/s00354-008-0050-z

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Language：Japanese   Publisher：Japanese Society for Artificial Intelligence  

This paper discusses DNA-based stochastic optimizations under the constraint that the search starts from a given point in a search space. Generally speaking, a stochastic optimization method explores a search space and finds out the optimum or a sub-optimum after many cycles of trials and errors. This search process could be implemented efficiently by "molecular computing", which processes DNA molecules by the techniques of molecular biology to generate and evaluate a vast number of solution candidates at a time. We assume the exploration starting from a single point, and propose a method to embody DNA-based optimization under this constraint, because this method has a promising application in the research field of protein engineering. In this application, a string of nucleotide bases (a base sequence) encodes a protein possessing a specific activity, which could be given as a value of an objective function. Thus, a problem of obtaining a protein with the optimum or a sub-optimum about the desired activity corresponds to a combinatorial problem of obtaining a base sequence giving the optimum or a sub-optimum in the sequence space. Biologists usually modify a base sequence corresponding to a naturally occurring protein into another sequence giving a desired activity. In other words, they explore the space in the proximity of a natural protein as a start point. We first examined if the optimization methods that involve a single start point, such as simulated annealing, Gibbs sampler, and MH algorithms, can be implemented by DNA-based operations. Then, we proposed an application of genetic algorithm, and examined the performance of this application on a model fitness landscape by computer experiments. These experiments gave helpful guidelines in the embodiments of DNA-based stochastic optimization, including a better design of crossover operator.

DOI： 10.1527/tjsai.22.405

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We analyzed the expressivity of recombinant proteins by using data mining methods. The expression technique of recombinant protein is a key step towards elucidating the functions of genes discovered through genomic sequence projects. We have studied the productive efficiency of recombinant proteins in fission yeast, Schizosaccharomyces pombe (S.pombe), by mining the expression results. We gathered 57 proteins whose expression levels were known roughly in the host. Correlation analysis, principal component analysis and decision tree analysis were applied to these expression data. Analysis featuring codon usage and amino acid composition clarified that the amino acid composition affected to the expression levels of a recombinant protein strongly than the effect of codon usage. Furthermore, analysis of amino acid composition showed that protein solubility and the metabolism cost of amino acids correlated with a protein expressivity. Codon usage was often interesting in the field of recombinant expressions. However, our analysis found the weak correlation codon features with expressivities. These results indicated that ready-made indices of codon bias were irrelevant ones for modeling the expressivities of recombinant proteins. Our data driven approach was an easy and powerful method to improve recombinant protein expression, and this approach should be concentrated attention with the huge amount of expression data accumulating through the post-genome era.

DOI： 10.1527/tjsai.21.9

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In this paper, we propose a general idea of Cellular Evolutionary Computation (CEC). CEC is Evolutionary Computation that solves the optimization problems with real DNA molecules and cells. The easiest means of cellular evolution is achieved by adding some genes to the main frame of gene network in the cell. However, in some cases it is necessary to optimize the gene parameters to achieve a desirable gene network output. We are working toward a realization of Evolutionary Computation algorithm to deal with the network optimization problems. We also suggest a novel method to realize a crossover operator for CEC via homologous recombination system within bacterial cells. Our ultimate objective of this study is the achievement of gene network evolution of the cell. We suggest an idea of cell-based computing that the cell-related problems are addressed by their related cells. © Springer 2005.

DOI： 10.1007/s11047-004-5236-9

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

Comparing protein tertiary structures that are explosively increasing is indispensable to the investigation into protein structure-function relationship. Methods hitherto published, however, treat proteins as rigid-bodies and thus are not able to capture the function-related structural variability of proteins, which appear through evolution. In this paper, we develop a protein structure comparison tool that emphasizes physico-chemical rearrangement of local fragments as building blocks of global structures. The proposed tool optimizes local fragment alignment and global superposition simultaneously. Using the population search ability of Genetic Algorithm, this tool shows the protein flexibility. We describe first the approach and the implementation. To address the large-scaled analysis of protein structure-function relationship, we show the effectiveness of the global search ability and fitness functions.

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Other Link： http://id.nii.ac.jp/1001/00010690/

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Language：Japanese   Publisher：Information Processing Society of Japan (IPSJ)  

The proteins fold into the native structures that express biological functions, and therefore comparing three-dimensional protein structures and classifying them are extremely important to understand the nature of protein molecules. Generally, the local structure and global structure that will be related to the survival of the fitness are strongly conserved in the process of molecular evolution. In here, we suggest an approach to lifting the veil of the relationship between local structure and global structure on the basis of assumption that local structures play a crucial role in assembling the global topology. The idea, two-layer comparison, proposed in this study is based on a Real-coded GA asynchronously parallelized.

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Other Link： http://id.nii.ac.jp/1001/00033311/

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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To use AntNet-FA globally, the ability of routing algorithms must be clear. The Internet has special topology and a hierarchy (AS and router). The topology have power-laws or scale-free property in other words. In this paper, we focused on the network topology and we applied AntNet algorithm to the network such as the Internet. We examined a node should use either a Loop-Free algorithm or a non-Loop-Free algorithm depending on its degree in heavy traffic condition. The Loop-Free feature means that when an ant decides to visit an adjacent node, then the ant selects the next node from its unvisited node. The non-Loop-Free algorithm is the same to the original AntNet. As a result, we found that network topology affects the ability of AntNet algorithms.

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Language：English   Publisher：OXFORD UNIV PRESS  

Motivation: The ultimate goal of bioinformatics is to reconstruct biological systems in the computer. Since biological systems have many levels, it is important to focus on an appropriate level. In our first application of computer modeling to the early development of the nematode Caenorhabditis elegans, we focus on the cellular arrangement in early embryos. This plays a very important role in cell fate determination by cell-cell interaction, and is regarded as a system, one level higher than the system of gene regulation within cells. It is largely restricted by physical conditions that seemed feasible to model by computer.
Results: We constructed a computer model of the C.elegans embryo, currently up to the 4-cell stage, using a deformable and dividable triangulated network. The model is based solely on cellular-level dynamics. We found that the optimal ranges of three parameters that affect the elongation of dividing cells led, in computer simulations, to almost the same cellular arrangements as in real embryos. The nature of the model and the relationship with real embryos are discussed.

DOI： 10.1093/bioinformatics/btg069

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Language：English   Publisher：SPRINGER-VERLAG BERLIN  

Whiplash PCR has been proposed as a unique mechanism realizing autonomous inference machines and used in various applications of DNA coumputing. However, it is not easy to increase step sizes within a single molecule because of back annealing. This paper proposes a sheme to cascade results of WPCR from molecules to molecules by using a nicking enzyme. We also show preliminary experiments to produce output fragments continuously from WPCR.

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Language：Japanese   Publisher：The Institute of Electrical Engineers of Japan  

In this paper we address a new problem in reinforcement learning. Here we consider an agent that faces multiple learning tasks within its lifetime. The agent's objective is to maximize its total reward in the lifetime as well as a conventional return in each task. To realize this, it has to be endowed an important ability to keep its past learning experiences and utilize them for improving future learning performance. This time we try to phrase this problem formally. The central idea is to introduce an environmental class, BV-MDPs that is defined with the distribution of MDPs. As an approach to exploiting past learning experiences, we focus on statistical information (mean and deviation) about the agent's value tables. The mean can be used as initial values of the table when a new task is presented. The deviation can be viewed as measuring reliability of the mean, and we utilize it in calculating priority of simulated backups. We conduct experiments in computer simulation to evaluate the effectiveness.

DOI： 10.1541/ieejeiss.123.1004

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Other Link： https://jlc.jst.go.jp/DN/JALC/00208623353?from=CiNii

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Language：English   Publisher：SCIENCE PRESS  

The concept of aqueous computing is presented here, first in full generality, and afterward, using an implementation in a specific enzymatic technology. Aqueous computing arose in the context of biomolecular (DNA) computing, but the concept is independent of the specifics of its biochemical origin. Alternate technologies for realizing aqueous computing are being considered for future implementation. A solution of an instance of the Boolean satisfiability problem, (SAT), is reported here that provides a new example of an aqueous computation that has been carried out successfully. This small instance of the SAT problem is sufficiently complex to allow our current enzymatic technology to be illustrated in detail. The reader is invited to participate in the rich interdisciplinary activity required by wet lab computing. A project is suggested to the reader for determining the three-colorings of a graph. The basic operations required for this project are exhibited in the solution of the SAT example reported here.

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Language：English   Publisher：Japanese Society for Bioinformatics  

The ultimate goal of bioinformatics is to reconstruct biological systems in a computer. Biological systems have a multi-scale and multi-level biological hierarchy. The cellular level of the hierarchy is appropriate and practicable for reconstructing biological systems by computer modeling. In our first application of computer modeling to development of the nematode C. elegans, we focus on the cellular arrangement in early embryos. This plays a very important role in cell fate determination by cell-cell interaction, which is largely restricted by physical conditions. We have already constructed a computer model of a C. elegans embryo, currently up to the 4-cell stage, using deformable and dividable geometric graphics. Modeling components of the embryo are based solely on cellular-level dynamics. Here, we modeled new physical phenomena of cell division, cell rounding and stiffening; we then combined them with already modeled phenomena, contractile ring contraction and cell elongation. We investigated effectiveness of the new model on cellular arrangement by computer simulations. We found that cell rounding and stiffening only during the period of cell division were effective to generate almost identical cellular arrangements to in real embryos. Since cells could be soft during the period between cell divisions, implementation of the new model resulted in cell shapes similar to real embryos. The nature of the model and its relationship to real embryos arediscussed.

DOI： 10.11234/gi1990.13.224

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Language：Japanese   Publisher：The Institute of Electrical Engineers of Japan  

DOI： 10.1541/ieejjournal.122.156

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DOI： 10.11234/gi1990.13.224

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DOI： 10.1002/ecja.1123

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Language：English   Publisher：Japanese Society for Bioinformatics  

The ultimate goal of bioinformatics is to reconstruct biological systems in a computer. Biological systems have a multi-scale and multi-level biological hierarchy. The cellular level of the hierarchy is appropriate and practicable for reconstructing biological systems by computer modeling. In our first application of computer modeling to development of the nematode C. elegans, we focus on the cellular arrangement in early embryos. This plays a very important role in cell fate determination by cell-cell interaction, which is largely restricted by physical conditions. We have already constructed a computer model of a C. elegans embryo, currently up to the 4-cell stage, using deformable and dividable geometric graphics. Modeling components of the embryo are based solely on cellular-level dynamics. Here, we modeled new physical phenomena of cell division, cell rounding and stiffening; we then combined them with already modeled phenomena, contractile ring contraction and cell elongation. We investigated effectiveness of the new model on cellular arrangement by computer simulations. We found that cell rounding and stiffening only during the period of cell division were effective to generate almost identical cellular arrangements to in real embryos. Since cells could be soft during the period between cell divisions, implementation of the new model resulted in cell shapes similar to real embryos. The nature of the model and its relationship to real embryos arediscussed.

DOI： 10.11234/gi1990.13.224

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DOI： 10.1002/ecja.1123

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Language：English   Publisher：SPRINGER-VERLAG  

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This paper presents a new method that improves robustness of real-coded Genetic Algorithm (GA) for function optimization. It is reported that most of crossover operators for real-coded GA have sampling bias, which prevents to find the optimum when it is near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of the search space. Although several methods have been proposed to relax this sampling bias, they could not cancel whole bias. In this paper, we propose a new method, Toroidal Search Space Conversion (TSC), to remove this sampling bias. TSC converts bounded search space into toroidal one without any parameter. Experimental results show that a GA with TSC has higher performance to find the optimum near the boundary of search space and the GA has more robustness concerning the relative position of the optimum.

DOI： 10.1527/tjsai.16.333

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In this paper, we perform theoretical analysis and experiments on the Simplex Crossover (SPX), which we have proposed. Real-coded GAs are expected to be a powerful function optimization technique for real-world applications where it is often hard to formulate the objective function. However, we believe there are two problems which will make such applications difficult
1) performance of real-coded GAs depends on the coordinate system used to express the objective function, and 2) it costs much labor to adjust parameters so that the GAs always find an optimum point efficiently. The result of our theoretical analysis and experiments shows that a performance of SPX is independent of linear coordinate transformation and that SPX always optimizes various test function efficiently when theoretical value for expansion rate, which is a parameter of SPX, is applied. We also show that BLX-α is equivalent to degenerate form of SPX. Experiments show that we have something misunderstood effect of epistasis on performance degradation of real-coded GAs.

DOI： 10.1527/tjsai.16.147

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In this paper, we perform theoretical analysis and experiments on the Simplex Crossover (SPX), which we have proposed. Real-coded GAs are expected to be a powerful function optimization technique for real-world applications where it is often hard to formulate the objective function. However, we believe there are two problems which will make such applications difficult
1) performance of real-coded GAs depends on the coordinate system used to express the objective function, and 2) it costs much labor to adjust parameters so that the GAs always find an optimum point efficiently. The result of our theoretical analysis and experiments shows that a performance of SPX is independent of linear coordinate transformation and that SPX always optimizes various test function efficiently when theoretical value for expansion rate, which is a parameter of SPX, is applied. We also show that BLX-α is equivalent to degenerate form of SPX. Experiments show that we have something misunderstood effect of epistasis on performance degradation of real-coded GAs.

DOI： 10.1527/tjsai.16.147

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Language：English   Publisher：IEEE  

This paper applies a method, Genetic algorithm with Search area Adaptation (GSA), to the function optimization. In previous study, GSA has proposed for the floorplan design problem and it has shown better performance than several existing methods. We believe that investigation of the searching behavior of the algorithm is important. However, since the floorplan design problem is combinatorial optimization problem, we do not know in detail why GSA works well. Thus, in this paper, we apply GSA to the function optimization in order to study the searching behavior in detail. In the function optimization, several benchmarks have been proposed, and their optima and landscapes are known. There is another purpose to apply GSA to the function optimization. We would like to propose a superior method for the function optimization. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance than one of existing methods.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publisher：I O S PRESS  

For a large scale evolutionary multi-agent systems, such as social or economic systems, we cannot design and control each agent directly. This paper shows a case study of an indirect control on such a system using a simple evolutionary baggage transportation system. We propose a two levels control mechanism, then show how that improve throughput and how high selection pressure cause premature convergence.

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DOI： 10.11499/sicejl1962.38.612

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Language：English   Publisher：MORGAN KAUFMANN PUB INC  

Many algorithms and protocols for DNA computing have been proposed so far, but most of them remain mere proposals and their feasibility has not yet been verified. Even in cases when in vitro experiments are possible, it is desirable to verify the feasibility in advance. We developed a simulator to aid those who design algorithms and protocols for DNA computing. Tn this simulator, abstract sequences instead of real DNA sequences are used to represent molecules in order to increase efficiency of simulations. It consists of two main. parts, one for finding reactions among existing molecules and generating new ones, and the other for numerically solving differential equations to calculate the concentration of each molecule. The two parts rely on each other. In particular, the former avoids a combinatorial explosion by setting a threshold on concentrations of molecules that can take part in reactions. Some simulation results are also presented: computation of Boolean circuits, formation of DNA tiles and simulation of polymerase chain reaction (PCR). As for PCR, we also tried to find good protocols for PCR amplification using Genetic Algorithm (GA).

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DOI： 10.1109/CEC.1999.782533

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Language：English   Publisher：MORGAN KAUFMANN PUB INC  

Genetic Algorithm (GA) has been applied to many difficult optimization problems. It is known that GA can find the globally optimum solution rapidly if the population holds both varieties and concentration sufficiently. However, it is difficult to satisfy both requirements at the same time, because they are often in the relation of tradeoff each other. Although existing methods have several parameters to control this tradeoff balance, tuning them before search is also difficult. In this paper, we propose GSA, Genetic algorithm with Search area Adaptation, which controls the tradeoff balance dynamically. We have applied GSA to the floorplan design problem. The experimental results have shown the effectiveness of this approach.

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DOI： 10.1109/CEC.1999.782533

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Other Link： http://id.nii.ac.jp/1004/00004163/

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Language：English   Publisher：The Institute of Electronics, Information and Communication Engineers  

In this paper we present an efficient method to solve reachability problems for Petri nets based on genetic algorithms and a kind of random search which is called postpone search. Genetic algorithm is one of algorithms developed for solving several problems of optimization. We apply GAs and postpone search to approximately solving reachability problems. This approach can not determine exact solutions, however, from applicability points of view, does not directly face state space explosion problems and can extend class of Petri nets to deal with very large state space in reasonable time. First we describe how to represent reachability problems on each of GAs and postpone search. We suppose the existence of a nonnegative parickh vector which satisfies the necessary reachability condition. Possible firing sequences of transitions induced by the parickh vector is encoded on GAs. We also define fitness function to solve reachability problems. Reachability problems can be interpreted as an optimization ones on GAs. Next we introduce random reachability problems which are capable of handling state space and the number of firing sequences which enable to reach a target marking from an initial marking. State space and the number of firing sequences are considered as factors which effect on the hardness of reachability problems to solve with stochastic methods. Furthermore,by using those random reachability problems and well known dining philosophers problems as benchmark problems, we compare GAs'performance with the performance of postpone search. Finally we present empirical results that GAs is more useful method than postpone search for solving more harder reachabi1ity problems from the both points of view; "reliability " and "efficiency."

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Language：Japanese  

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Other Link： http://id.nii.ac.jp/1004/00004093/

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Other Link： http://id.nii.ac.jp/1004/00004095/

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Language：English   Publisher：IEEE  

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Language：Japanese  

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Language：Japanese   Publisher：Japan Society for Simmulation Technology  

Genetic Algorithms (GA) is a kind of optimization method which models naive evolutional process. GA has number of design elements which have great affect to its performance. This paper tries to put them in order under a context of combinatorial optimization. We classify design elements into three levels ; coding/crossover design, fitness design, and parameter tuning. We also classify performance of GA into four levels ; random search, local search, global search, and well-balanced search. We show some typical trial and error examples for each design levels with scheduling problems. For coding/crossover design, we show GA remains random search unless a crossover preserves characters in traveling salesman problems. For fitness design, we show GA remains local search when deceptive fitness prevents to grow building blocks in nurse scheduling problems. For parameter tuning, we show higher crossover rate accelerates early convergence in jobshop scheduling problems.

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Language：Japanese   Publisher：公益社団法人日本オペレーションズ・リサーチ学会  

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Presentation type：Poster presentation  

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Presentation type：Poster presentation  

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