Miki Hirabayashi

A new lattice Boltzmann approach to the fractal structure in magnetic fluids

We present the result of a lattice Boltzmann simulation of the two-dimensional fractal structure in magnetic fluids in an alternating current magnetic field using a new model that we have developed. This result belongs to the class of self-affine fractal and we measure its fractal dimension. The lattice Boltzmann procedure is convenient to study the mechanical properties of complex fluids. Our simple new model is introduced by the appropriate extension of the model for magnetohydrodynamics and can deal with the rotation of the magnetic moment efficiently. This scheme is suitable for simulating various behaviors of magnetic fluids under the influence of the internal angular momentum.

Programmed regulation of conformational switching in diagnostic biomolecular automata using error control molecules

We aim to present novel biomolecular automata for transcriptome diagnosis. We already proposed several biomolecular probes for the prediction of oral squamous cell carcinoma as a model case and evaluated the system noise and sensitivity. Here we introduce an error control element and discuss the specificity of the system.

Experimental analysis of the basic idea on the transcription-based diagnostic automata controlled by programmed molecules

Biomolecular computing using the artificial nucleic acid technology is expected to bring new solutions to various health problems. We focus on the noninvasive transcriptome diagnosis by salivary mRNAs and present the novel concept of transcription-based diagnostic automata that are constructed by programmed DNA modules. The main computational element has a stem shaped promoter region and a pseudo-loop shaped read-only memory region for transcription regulation through the conformation change caused by targets. Our system quantifies targets by transcription of malachite green aptamer sequence triggered by the target recognition. This algorithm makes it possible to realize the cost-effective and sequence-specific real-time target detection. Moreover, in the in-vivo therapeutic use, this transcription-based system can release RNA-aptamer drugs multiply at the transcription stage, different from the digestion-based systems by the restriction enzyme which was proposed previously. We verified the sensitivity, the selectivity and the quantitative stability of the diagnostic automata in basic conditions. Our approach will provide promising applications of autonomous intelligent systems using programmed molecules.

Regulation Effects by Programmed Molecules for Transcription-Based Diagnostic Automata towards Therapeutic Use

We have presented experimental analysis on the controllability of our transcription-based diagnostic biomolecular automata by programmed molecules. Focusing on the noninvasive transcriptome diagnosis by salivary mRNAs, we already proposed the novel concept of diagnostic device using DNA computation. This system consists of the main computational element which has a stem shaped promoter region and a pseudo-loop shaped read-only memory region for transcription regulation through the conformation change caused by the recognition of disease-related biomarkers. We utilize the transcription of malachite green aptamer sequence triggered by the target recognition for observation of detection. This algorithm makes it possible to release RNA-aptamer drugs multiply, different from the digestion-based systems by the restriction enzyme which was proposed previously, for the in-vivo use, however, the controllability of aptamer release is not enough at the previous stage. In this paper, we verified the regulation effect on aptamer transcription by programmed molecules in basic conditions towards the developm! ent of therapeutic automata. These results would bring us one step closer to the realization of new intelligent diagnostic and therapeutic automata based on molecular circuits.

Experimental validation of the transcription-based diagnostic automata with quantitative control by programmed molecules

Biomolecular computing using the artificial nucleic acid technology is expected to bring new solutions to various health problems. We focus on the noninvasive transcriptome diagnosis by salivary mRNAs and present the novel concept of transcription-based diagnostic automata that are constructed by programmed DNA modules. The main computational element has a stem shaped promoter region and a pseudo-loop shaped read-only memory region for transcription regulation through the conformation change caused by targets. Our system quantifies targets by transcription of malachite green aptamer sequence triggered by the target recognition. This algorithm makes it possible to realize the cost-effective and sequence-specific real-time target detection. Moreover, in the in-vivo therapeutic use, this transcription-based system can release RNA-aptamer drugs multiply at the transcription stage, different from the digestion-based systems by the restriction enzyme which was proposed previously. We verified the sensitivity, the selectivity and the quantitative stability of the diagnostic automata in basic conditions. Our approach will provide promising applications of autonomous intelligent systems using programmed molecules.

A lattice Boltzmann analysis of the effects of the internal angular momentum on rheology of magnetic fluids

We study the rheological properties of the magnetic fluids with the internal angular momentum, using a new lattice Boltzmann model. By the appropriate extension of the lattice Boltzmann scheme for magnetohydrodynamics, it is possible to deal with the internal angular momentum caused by the magnetic dipole moment efficiently. This model is suitable for a wide use for simulating various behaviors of magnetic fluids with the internal angular momentum. We present here a two-dimensional lattice Boltzmann analysis of the effects of internal angular momentum in magnetic fluids in order to understand the rheological properties of the magnetic colloid driven by the external magnetic field.

Effect of bio-inspired multi-stage regulations for diagnostic molecular automata

Bio-inspired natural computing is expected to bring new solutions to various problems confronting us. We already presented a novel concept for transcriptome diagnosis using transcription-based molecular automata. Here we introduce new multi-stage regulations to this diagnostic system in order to reduce the background noise and increase the sensitivity. We use two explicit detector probes with a stem and a memory loop at the pre-transcriptional stage, and one implicit binary aptameric sensor at the post-transcriptional stage. In addition to this we adopt the error control element to improve the detection ability. Consequently we found that the noise was reduced to one-forty-fifth and the sensitivity increased five-fold by the regulation using the combination of several strategies. Basically the living organisms control themselves by complex and dexterous manners. The incorporation of bio-inspired redundant strategies bring the all-too-simple and unstable artificial systems close to the robust and reliable ones.