For the experimental construction of artificial cell, it is a challenge to simultaneously supply the nutrients and lipids required for protein synthesis, gene replication, membrane growth, and fission. Inner reactions of liposomes are not permanent because of nutrient exhaustion since liposomes do not have pores or channels on their membrane for acquisition of nutrients. In this study, we demonstrated that the liposome containing in vitro translation system was fused with liposome encapsulating RNA by freeze and thaw for GFP synthesis. The fusion mixed lipid molecules on the two kinds of liposomes, followed by the fission. Consequently, we observed GFP synthesis inside the liposomes after liposome fusion and fission. This freeze and thaw method can be repeated, for the sustainable supplement of nutrients with liposome growth. We hope this method would achieve the ultimate goal of establishing the artificial cells that can acquire nutrients sustainably and proliferate by coupling protein synthesis and gene...

This paper proposes a novel design of reliable end-to-end molecular communication. In molecular communication proposed in this paper, source and destination bio-nanomachines exchange molecular packets through intermediate bio-nanomachines. A source bio-nanomachine forms a molecular packet and transmits the molecular packet into the environment. An intermediate bio-nanomachine detects a molecular packet and produces its copies. A destination bio-nanomachine, upon reception of a molecular packet, produces an acknowledgment molecular packet and transmits back to the source bio-nanomachine. Further, a source bio-nanomachine retransmits a molecular packet if no acknowledgment molecular packet is returned within a time-out period. In this paper, we develop an analytical framework to study propagation delay in the reliable end-to-end molecular communication proposed in this paper. We also show how the proposed molecular communication is biochemically implemented using ribonucleic acid (RNA) molecules.

Reducing native complexity from living organisms has significance in several aspects such as academic application as model organism and industrial application as factory of useful material. In particular, reduced complexity is also interesting in the field investigating the minimal form of “life-as-we-knowit.” However, subtracting or inactivating genes from the genome without growth defects is difficult due to the complexity of gene network and error proofing functions. In this study, using model bacteria Escherichia coli (E. coli), we designed a culture system using ultraviolet as a mutagen in order to select possible mutants growing rapidly with genomic inactivation. Here, we demonstrated that the culture system could accumulation of many mutations and preservation of growth ability. These results suggest that our method is effective to obtain functionally reduced genome of E. coli.

As key technologies for future information networks, many researchers have focused on self-organized network controls. In the process of their ordering, their robustness against environmental changes decreases while their performance increases. Therefore, their behavior in dynamic environment should retain appropriate amount of disorder. In this paper, we conduct simulation experiments and show that higher entropy leads to higher robustness against node failures.

MOTIVATION: High-density DNA microarrays provide us with useful tools for analyzing DNA and RNA comprehensively. However, the background signal caused by the non-specific binding (NSB) between probe and target makes it difficult to obtain accurate measurements. To remove the background signal, there is a set of background probes on Affymetrix Exon arrays to represent the amount of non-specific signals, and an accurate estimation of non-specific signals using these background probes is desirable for improvement of microarray analyses. RESULTS: We developed a thermodynamic model of NSB on short nucleotide microarrays in which the NSBs are modeled by duplex formation of probes and multiple hypothetical targets. We fitted the observed signal intensities of the background probes with those expected by the model to obtain the model parameters. As a result, we found that the presented model can improve the accuracy of prediction of non-specific signals in comparison with previously proposed methods. This result will provide a useful method to correct for the background signal in oligonucleotide microarray analysis. AVAILABILITY: The software is implemented in the R language and can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/MSNS/).

MOTIVATION: High-density DNA microarrays provide useful tools to analyze gene expression comprehensively. However, it is still difficult to obtain accurate expression levels from the observed microarray data because the signal intensity is affected by complicated factors involving probe-target hybridization, such as non-linear behavior of hybridization, non-specific hybridization, and folding of probe and target oligonucleotides. Various methods for microarray data analysis have been proposed to address this problem. In our previous report, we presented a benchmark analysis of probe-target hybridization using artificially synthesized oligonucleotides as targets, in which the effect of non-specific hybridization was negligible. The results showed that the preceding models explained the behavior of probe-target hybridization only within a narrow range of target concentrations. More accurate models are required for quantitative expression analysis. RESULTS: The experiments showed that finiteness of both probe and target molecules should be considered to explain the hybridization behavior. In this article, we present an extension of the Langmuir model that reproduces the experimental results consistently. In this model, we introduced the effects of secondary structure formation, and dissociation of the probe-target duplex during washing after hybridization. The results will provide useful methods for the understanding and analysis of microarray experiments. AVAILABILITY: The method was implemented for the R software and can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/FHarray/).

Evolution of genetic codes is studied as change in the choice of enzymes that are used to synthesize amino acids from the genetic information of nucleic acids. We propose the following theory: the differentiation of physiological states of a cell allows for a choice of enzymes, and this choice is later fixed genetically through evolution. To demonstrate this theory, a dynamical systems model consisting of the concentrations of metabolites, enzymes, amino acyl tRNA synthetase, and tRNA - amino acid complexes in a cell is introduced and studied numerically. It is shown that the biochemical states of cells are differentiated by cell-cell interactions, and each differentiated type starts to use a different synthetase. Through the mutation of genes, this difference in the genetic code is amplified and stabilized. The relevance of this theory to the evolution of non-universal genetic code in mitochondria is suggested. The present theory is based on our recent theory of isologous symbiotic speciation, which is briefly reviewed. According to the theory, phenotypes of organisms are first differentiated into distinct types through the interaction and developmental dynamics, even though they have identical genotypes; later, with mutation in the genotype, the genotype also differentiates into discrete types, while maintaining the "symbiotic" relationship between the types. Relevance of the theory to natural as well as artificial evolution is discussed.