Yoichi Nakayama

The systems biology markup language (SBML) : a medium for representation and exchange of biochemical network models

MOTIVATION Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY The specification of SBML Level 1 is freely available from http://www.sbml.org/

E-Cell 2: multi-platform E-Cell simulation system.

A new version of the E-Cell simulation system,which runs on Windows as well as Linux, has been released as free software under the terms of the GNU General Public License.

Roles of Hemoglobin Allostery in Hypoxia-induced Metabolic Alterations in Erythrocytes SIMULATION AND ITS VERIFICATION BY METABOLOME ANALYSIS

When erythrocytes are exposed to hypoxia, hemoglobin (Hb) stabilizes in the T-state by capturing 2,3-bisphosphoglycerate. This process could reduce the intracellular pool of glycolytic substrates, jeopardizing cellular energetics. Recent observations suggest that hypoxia-induced activation of glycolytic enzymes is correlated with their release from Band III (BIII) on the cell membrane. Based on these data, we developed a mathematical model of erythrocyte metabolism and compared hypoxia-induced differences in predicted activities of the enzymes, their products, and cellular energetics between models with and without the interaction of Hb with BIII. The models predicted that the allostery-dependent Hb interaction with BIII accelerates consumption of upstream glycolytic substrates such as glucose 6-phosphate and increases downstream products such as phosphoenolpyruvate. This prediction was consistent with metabolomic data from capillary electrophoresis mass spectrometry. The hypoxia-induced alterations in the metabolites resulted from acceleration of glycolysis, as judged by increased conversion of [13C]glucose to [13C]lactate. The allostery-dependent interaction of Hb with BIII appeared to contribute not only to maintenance of energy charge but also to further synthesis of 2,3-bisphosphoglycerate, which could help sustain stabilization of T-state Hb during hypoxia. Furthermore, such an activation of glycolysis was not observed when Hb was stabilized in R-state by treating the cells with CO. These results suggest that Hb allostery in erythrocytes serves as an O2-sensing trigger that drives glycolytic acceleration to stabilize intracellular energetics and promote the ability to release O2 from the cells.

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.

The E-CELL project: Towards integrative simulation of cellular processes

The E-CELL project was launched in 1996 at Keio University in order to model and simulate various cellular processes with the ultimate goal of simulating the cell as a whole. The first version of the E-CELL simulation system, which is a generic software package for cell modeling, was completed in 1997. The E-CELL system enables us to model not only metabolic pathways but also other higher-order cellular processes such as protein synthesis and membrane transport within the same framework. These various processes can then be integrated into a single simulation model.Using the E-CELL system, we have successfully constructed a virtual cell with 127 genes sufficient for “self-support”. The gene set was selected from the genome of Mycoplasma genitalium the organism having the smallest known genome. The set includes genes for transcription, translation, the glycolysis pathway for energy production, membrane transport, and the phospholipid biosynthesis pathway for membrane structure.The E-CELL system has been made available for beta testing from our website (http: //www.e-cell.org).

Simulation study of methemoglobin reduction in erythrocytes Differential contributions of two pathways to tolerance to oxidative stress

Methemoglobin (metHb), an oxidized form of hemoglobin, is unable to bind and carry oxygen. Erythrocytes are continuously subjected to oxidative stress and nitrite exposure, which results in the spontaneous formation of metHb. To avoid the accumulation of metHb, reductive pathways mediated by cytochrome b5 or flavin, coupled with NADH‐dependent or NADPH‐dependent metHb reductases, respectively, keep the level of metHb in erythrocytes at less than 1% of the total hemoglobin under normal conditions. In this work, a mathematical model has been developed to quantitatively assess the relative contributions of the two major metHb‐reducing pathways, taking into consideration the supply of NADH and NADPH from central energy metabolism. The results of the simulation experiments suggest that these pathways have different roles in the reduction of metHb; one has a high response rate to hemoglobin oxidation with a limited reducing flux, and the other has a low response rate with a high capacity flux. On the basis of the results of our model, under normal oxidative conditions, the NADPH‐dependent system, the physiological role of which to date has been unclear, is predicted to be responsible for most of the reduction of metHb. In contrast, the cytochrome b5–NADH pathway becomes dominant under conditions of excess metHb accumulation, only after the capacity of the flavin–NADPH pathway has reached its limit. We discuss the potential implications of a system designed with two metHb‐reducing pathways in human erythrocytes.

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

BackgroundCell 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.ResultsSince 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.ConclusionThe 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.

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

BackgroundMany 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.ResultsHere 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.ConclusionThe 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.

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

BackgroundIn 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.ResultsThe 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.ConclusionThe 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.

Distinguishing enzymes using metabolome data for the hybrid dynamic/static method

BackgroundIn the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.ResultsIn this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.ConclusionA protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.