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Yoichi Nakayama

Publications and source records attributed to Yoichi Nakayama.

11 recordsLinked to original sources
PubMed2007

A mathematical model for the Kai-protein-based chemical oscillator and clock gene expression rhythms in cyanobacteria.

In the cyanobacterium, Synechococcus elongatus, most promoters are regulated by a circadian clock under continuous light (LL) conditions. Nevertheless, the basic circadian oscillation is primarily generated by alternating KaiC phosphorylation/dephosphorylation reactions at the posttranslational level. Indeed, the KaiC phosphorylation cycle was recently reconstituted in vitro by incubating KaiA, KaiB, and KaiC proteins with ATP. However, the molecular dynamics of this chemical oscillation and the mechanism that drives the circadian transcription/translation rhythms remain unknown. In this report, the KaiC phosphorylation cycle and the gene regulatory network in the cyanobacterial circadian system have been modeled. The model reproduces the robust KaiC phosphorylation cycle in the absence of de novo gene expression as is observed in vitro, as well as its coupling to transcriptional/translational feedback in LL conditions in vivo. Moreover, the model is consistent with most previous experiments, including various combinations of genetic knockout or overexpression of kai genes. It also predicts that multiple KaiC phosphorylation states and dynamic Kai protein interactions may be required for the cyanobacterial circadian system.

Bacterial Proteins↗
PubMed2006

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles.

BACKGROUND: In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems. RESULTS: The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations. CONCLUSION: The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.

Kinetics↗
PubMed2006

Over-representation of Chi sequences caused by di-codon increase in Escherichia coli K-12.

Chi sequences (5'-GCTGGTGG-3') are cis-acting 8 bp sequence elements that enhance homologous recombination promoted by the RecBCD pathway in Escherichia coli. The genome of E. coli K-12 MG1655 contains 1009 Chi sequences and this frequency far exceeds the expected value for occurrence of an 8 bp sequence in a genome of this size. It is generally thought that the over-representation of Chi sequences indicates that they have been selected for during evolution because of their function in recombination. The genes from three E. coli strains (K-12, O157 and CFT) were classified into three categories (island, match to other E. coli, and backbone). Island genes have a different base composition and codon usage in comparison with those in the backbone genes, therefore they were relatively new and not yet adapted to the base composition patterns and codon usage typical of the recipient genome. The over-representation of Chi sequences was examined by comparing Chi frequencies and codon frequencies between island and backbone genes. The difference in the CTGGTG di-codon frequency between the backbone and island genes was correlated with the frequency of Chi sequences which were translated in the Leu-Val (-G/CTG/GTG/G-) reading frame in the K-12 strain. These results suggest that the main reading frame of Chi sequences increased as a result of the di-codon CTG-GTG increasing under a genome-wide pressure for adapting to the codon usage and base composition of the E. coli K-12 strain, and that the RecBCD recombinase might adjust its recognition sequence to a frequently occurring oligomer such as G-CTG-GTG-G.

Base Composition↗
PubMed2006

GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

BACKGROUND: Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. RESULTS: We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. CONCLUSION: The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

Chromosome Mapping↗
PubMed2006

GPAC: benchmarking the sensitivity of genome informatics analysis to genome annotation completeness.

In view of the recent explosion in genome sequence data, and the 200 or more complete genome sequences currently available, the importance of genome-scale bioinformatics analysis is increasing rapidly. However, computational genome informatics analyses often lack a statistical assessment of their sensitivity to the completeness of the functional annotation. Therefore, a pre-analysis method to automatically validate the sensitivity of computational genome analyses with regard to genome annotation completeness is useful for this purpose. In this report we developed the Gene Prediction Accuracy Classification (GPAC) test, which provides statistical evidence of sensitivity by repeating the same analysis for five different gene groups (classified according to annotation accuracy level), and for randomly sampled gene groups, with the same number of genes as each of the five classified groups. Variability in these results is then assessed, and if the results vary significantly with different data subsets, the analysis is considered "sensitive" to annotation completeness, and careful selection of data is advised prior to the actual in silico analysis. The GPAC test has been applied to the analyses of Sakai et al., 2001, and Ohno et al., 2001, and it revealed that the analysis of Ohno et al. was more sensitive to annotation completeness. It showed that GPAC could be employed to ascertain the sensitivity of an analysis. The GPAC bendhmarking software is freely available in the latest G-language Genome Analysis Environment package, at http://www.g-language.org/.

Benchmarking↗
PubMed2005

A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks.

BACKGROUND: Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. RESULTS: We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. CONCLUSION: We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

Amino Acid Motifs↗
PubMed2005

Hybrid dynamic/static method for large-scale simulation of metabolism.

BACKGROUND: Many computer studies have employed either dynamic simulation or metabolic flux analysis (MFA) to predict the behaviour of biochemical pathways. Dynamic simulation determines the time evolution of pathway properties in response to environmental changes, whereas MFA provides only a snapshot of pathway properties within a particular set of environmental conditions. However, owing to the large amount of kinetic data required for dynamic simulation, MFA, which requires less information, has been used to manipulate large-scale pathways to determine metabolic outcomes. RESULTS: Here we describe a simulation method based on cooperation between kinetics-based dynamic models and MFA-based static models. This hybrid method enables quasi-dynamic simulations of large-scale metabolic pathways, while drastically reducing the number of kinetics assays needed for dynamic simulations. The dynamic behaviour of metabolic pathways predicted by our method is almost identical to that determined by dynamic kinetic simulation. CONCLUSION: The discrepancies between the dynamic and the hybrid models were sufficiently small to prove that an MFA-based static module is capable of performing dynamic simulations as accurately as kinetic models. Our hybrid method reduces the number of biochemical experiments required for dynamic models of large-scale metabolic pathways by replacing suitable enzyme reactions with a static module.

Animals↗
PubMed2005

Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition.

BACKGROUND: Cell simulation, which aims to predict the complex and dynamic behavior of living cells, is becoming a valuable tool. In silico models of human red blood cell (RBC) metabolism have been developed by several laboratories. An RBC model using the E-Cell simulation system has been developed. This prototype model consists of three major metabolic pathways, namely, the glycolytic pathway, the pentose phosphate pathway and the nucleotide metabolic pathway. Like the previous model by Joshi and Palsson, it also models physical effects such as osmotic balance. This model was used here to reconstruct the pathology arising from hereditary glucose-6-phosphate dehydrogenase (G6PD) deficiency, which is the most common deficiency in human RBC. RESULTS: Since the prototype model could not reproduce the state of G6PD deficiency, the model was modified to include a pathway for de novo glutathione synthesis and a glutathione disulfide (GSSG) export system. The de novo glutathione (GSH) synthesis pathway was found to compensate partially for the lowered GSH concentrations resulting from G6PD deficiency, with the result that GSSG could be maintained at a very low concentration due to the active export system. CONCLUSION: The results of the simulation were consistent with the estimated situation of real G6PD-deficient cells. These results suggest that the de novo glutathione synthesis pathway and the GSSG export system play an important role in alleviating the consequences of G6PD deficiency.

Computer Simulation↗
PubMed2004

Toward large-scale modeling of the microbial cell for computer simulation.

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

Biotechnology↗
PubMed2003

On dynamics of overlapping genes in bacterial genomes.

A genome-wide computational analysis of overlapping genes in various prokaryotic genomes was conducted to study the evolutionary rates of the formation and degradation of overlapping genes. Overlapping genes are adjacent genes that overlap partially or entirely. Overlapping gene pairs extracted from complete bacterial genome sequences were classified into three directional patterns, namely, 'convergent' (--><--), 'unidirectional' (-->-->), and 'divergent' (<---->). Divergent overlapping genes were rare compared to the other two patterns. We also compared the structures of overlapping genes that are not conserved in the homologous genes of different species. We found that overlapping genes are mainly generated from gene pairs with short intergenic regions. Overlapping genes caused by mutations at the 3'-end of genes occur more frequently than overlapping genes generated by mutations at the 5'-end of genes in closely related species. In contrast, gene overlaps due to mutations at the 5'-ends accumulate in distant species. These differences presumably correlate to the genomic divergence among species. The evolution of these overlapping gene structures is probably related to the evolutionary time scale, which suggests that the evolution of overlapping genes occurs at a universal mutation rate across species.

Bacteria↗