Hiroyuki Kurata

Separation of proteins by charged ultrafiltration membranes

Abstract The separation of a protein mixture by charged ultrafiltration membranes was studied. A negatively charged polymer was obtained by sulfonation of polysulfone, and a positively charged polymer was synthesized by chloromethylation of polysulfone and then by quaternization of the amino group. Then, the negatively and positively charged ultrafiltration membranes were cast from solutions of charged polymer/NMP(or DMF)/lithium nitrate. The molecular weight cut-off of the membranes were controlled by the changing casting conditions. Single protein solutions were ultrafiltrated at the isoelectric point and at another pH level by the use of charged membranes. At the isoelectric point, rejection of the protein was low, while it was high at the pH level which gave the protein the same sign of charge as that of the membrane. A protein mixture of myoglobin and cytochrome C was separated by the charged ultrafiltration membranes at the isoelectric point of one of the proteins. At the isoelectric point of cytochrome C, myoglobin has a negative charge. Thus myoglobin was rejected with a rejection of about 80% by the negatively charged membrane. At the same time, cytochrome C permeated completely through the membrane. Conversely, at the isoelectric point of myoglobin, cytochrome C has a positive charge and thus it was rejected with a rejection of about 20% by the positively charged membrane. The rejection of myoglobin here was almost zero.

A grid layout algorithm for automatic drawing of biochemical networks

MOTIVATION Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. RESULTS We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. AVAILABILITY All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ SUPPLEMENTARY INFORMATION http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/

Computer-aided rational design of the phosphotransferase system for enhanced glucose uptake in Escherichia coli

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer‐aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate.

Integration of enzyme activities into metabolic flux distributions by elementary mode analysis

BackgroundIn systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model.ResultsTo link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF.ConclusionThe ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis.

Light irradiation causes physiological and metabolic changes for purine alkaloid production by a Coffea arabica cell suspension culture

Abstract Light irradiation not only enhanced purine alkaloid (caffeine and theobromine) production by a Coffea arabica cell suspension culture, but also caused physiological changes in cell growth, and sugar and oxygen uptake rates. Both sugar and oxygen uptake rates showed maxima between 7 and 10 W/m 2 of light intensity, where the specific production rate reached the highest, although the specific cell growth rate was reduced by more than 50%. Light acts as a stress for enhanced production. The activities of enzymes related to purine alkaloid production were induced by light after a lag-time of 1 day. Light is an inducer for these enzymes. However, the de novo synthesis of purine alkaloids required at least 6 days of light irradiation. This discrepancy was cuased by the insufficient supply of purine rings, the precursors for biosynthesis of purine alkaloids.

Maximum entropy decomposition of flux distribution at steady state to elementary modes

Enzyme Control Flux (ECF) is a method of correlating enzyme activity and flux distribution. The advantage of ECF is that the measurement integrates proteome data with metabolic flux analysis through Elementary Modes (EMs). But there are a few methods of effectively determining the Elementary Mode Coefficient (EMC) in cases where no objective biological function is available. Therefore, we proposed a new algorithm implementing the maximum entropy principle (MEP) as an objective function for estimating the EMC. To demonstrate the feasibility of using the MEP in this way, we compared it with Linear Programming and Quadratic Programming for modeling the metabolic networks of Chinese Hamster Ovary, Escherichia coli, and Saccharomyces cerevisiae cells. The use of the MEP presents the most plausible distribution of EMCs in the absence of any biological hypotheses describing the physiological state of cells, thereby enhancing the prediction accuracy of the flux distribution in various mutants.

Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis

Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents ‘Domain expansion’ for proteins and RNAs, ‘Virtual reaction and nodes’ that are responsible for illustrating domain-based interaction and ‘InnerLink’ that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis.

Intermittent light irradiation with second- or hour-scale periods controls anthocyanin production by strawberry cells☆

Anthocyanin production by strawberry cells depends not only on light intensity but also on the light/dark cycle operation with hour- or second-scale periods. These findings are useful for designing and operating photobioreactors for enhanced anthocyanin production. Intermittent illumination with a second-scale period produces the same amount of anthocyanin as continuous light, suggesting that the light intensity distribution within a photobioreactor does not cause suppressed production. In the hour-scale cycle, continuous light operation enhanced anthocyanin production more than the light/dark cycle process.

Genetic modification of flux for flux prediction of mutants

MOTIVATION Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level. RESULTS To overcome such existing limitations, we develop a novel algorithm that predicts the flux distribution of the mutants with a broad range of genetic modification, based on elementary mode analysis. It is denoted as genetic modification of flux (GMF), which couples two algorithms that we have developed: modified control effective flux (mCEF) and enzyme control flux (ECF). mCEF is proposed based on CEF to estimate the gene expression patterns in genetically modified mutants in terms of specific biological functions. GMF is demonstrated to predict the flux distribution of not only gene deletion mutants, but also the mutants with underexpressed and overexpressed genes in Escherichia coli and Corynebacterium glutamicum. This achieves breakthrough in the a priori flux prediction of a broad range of genetically modified mutants. SUPPLEMENTARY INFORMATION Supplementary file and programs are available at Bioinformatics online or http://www.cadlive.jp.

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

Background A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. Methodology/Principal Findings To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. Conclusions/Significance ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple.