Katsuyuki Yugi

E-CELL: software environment for whole-cell simulation.

MOTIVATION Genome sequencing projects and further systematic functional analyses of complete gene sets are producing an unprecedented mass of molecular information for a wide range of model organisms. This provides us with a detailed account of the cell with which we may begin to build models for simulating intracellular molecular processes to predict the dynamic behavior of living cells. Previous work in biochemical and genetic simulation has isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established. We, therefore, were motivated to develop a software environment for building such integrative models based on gene sets, and running simulations to conduct experiments in silico. RESULTS E-CELL, a modeling and simulation environment for biochemical and genetic processes, has been developed. The E-CELL system allows a user to define functions of proteins, protein-protein interactions, protein-DNA interactions, regulation of gene expression and other features of cellular metabolism, as a set of reaction rules. E-CELL simulates cell behavior by numerically integrating the differential equations described implicitly in these reaction rules. The user can observe, through a computer display, dynamic changes in concentrations of proteins, protein complexes and other chemical compounds in the cell. Using this software, we constructed a model of a hypothetical cell with only 127 genes sufficient for transcription, translation, energy production and phospholipid synthesis. Most of the genes are taken from Mycoplasma genitalium, the organism having the smallest known chromosome, whose complete 580 kb genome sequence was determined at TIGR in 1995. We discuss future applications of the E-CELL system with special respect to genome engineering. AVAILABILITY The E-CELL software is available upon request. SUPPLEMENTARY INFORMATION The complete list of rules of the developed cell model with kinetic parameters can be obtained via our web site at: http://e-cell.org/.

Computational challenges in cell simulation: a software engineering approach

Molecular biologys advent in the 20th century has exponentially increased our knowledge about the inner workings of life. We have dozens of completed genomes and an array of high-throughput methods to characterize gene encodings and gene product operation. The question now is how we will assemble the various pieces. In other words, given sufficient information about a living cells molecular components, can we predict its behavior? We introduce the major classes of cellular processes relevant to modeling, discuss software engineerings role in cell simulation, and identify cell simulation requirements. Our E-Cell project aims to develop the theories, techniques, and software platforms necessary for whole-cell-scale modeling, simulation, and analysis. Since the projects launch in 1996, we have built a variety of cell models, and we are currently developing new models that vary with respect to species, target subsystem, and overall scale.

A general computational model of mitochondrial metabolism in a whole organelle scale

UNLABELLED A computational tool for mitochondrial systems biology has been developed as a simulation model of E-Cell2, a publicly available simulation system. The general model consists of 58 enzymatic reactions and 117 metabolites, representing the respiratory chain, the TCA cycle, the fatty acid beta-oxidation and the inner-membrane transport system. It is based on previously published enzyme kinetics studies in the literature; we have successfully integrated and packaged them into a single large model. The model can be easily extended and modified so that mitochondrial biologists/physiologists can integrate their own models and evaluate them in the context of the whole organelle metabolism. AVAILABILITY The mitochondrial model is bundled up with E-Cell2 simulation system, which can be downloaded from http://www.e-cell.org. CD-ROMs are also available and are distributed at major conferences. SUPPLEMENTARY INFORMATION All the kinetic data are available via http://www.e-cell.org

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).

The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns

Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin‐signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLCex). Based on the experimental results, we constructed a simple computational model that characterises response of insulin‐signalling‐dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLCex was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin.

Reconstruction of Insulin Signal Flow from Phosphoproteome and Metabolome Data

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

The scaffold protein Shoc2/SUR-8 accelerates the interaction of Ras and Raf

Shoc2/SUR-8 positively regulates Ras/ERK MAP kinase signaling by serving as a scaffold for Ras and Raf. Here, we examined the role of Shoc2 in the spatio-temporal regulation of Ras by using a fluorescence resonance energy transfer (FRET)-based biosensor, together with computational modeling. In epidermal growth factor-stimulated HeLa cells, RNA-mediated Shoc2 knockdown reduced the phosphorylation of MEK and ERK with half-maximal inhibition, but not the activation of Ras. For the live monitoring of Ras binding to Raf, we utilized a FRET biosensor wherein Ras and the Ras-binding domain of Raf were connected tandemly and sandwiched with acceptor and donor fluorescent proteins for the FRET measurement. With this biosensor, we found that Shoc2 was required for the rapid interaction of Ras with Raf upon epidermal growth factor stimulation. To decipher the molecular mechanisms underlying the kinetics, we developed two computational models that might account for the action of Shoc2 in the Ras-ERK signaling. One of these models, the Shoc2 accelerator model, provided a reasonable explanation of the experimental observations. In this Shoc2 accelerator model, Shoc2 accelerated both the association and dissociation of Ras-Raf interaction. We propose that Shoc2 regulates the spatio-temporal patterns of the Ras-ERK signaling pathway primarily by accelerating the Ras-Raf interaction.

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.

Latent process genes for cell differentiation are common decoders of neurite extension length

A latent process involving signal transduction and gene expression is needed as a preparation step for cellular function. We previously found that nerve growth factor (NGF)-induced cell differentiation has a latent process, which is dependent on ERK activity and gene expression and required for subsequent neurite extension. A latent process can be considered as a preparation step that decodes extracellular stimulus information into cellular functions; however, molecular mechanisms of this process remain unknown. We identified Metrnl, Dclk1 and Serpinb1a as genes that are induced during the latent process (LP) with distinct temporal expression profiles and are required for subsequent neurite extension in PC12 cells. The LP genes showed distinct dependency on the duration of ERK activity, and they were also induced during the latent process of PACAP- and forskolin-induced cell differentiation. Regardless of neurotrophic factors, expression levels of the LP genes during the latent process (0–12 hours), but not phosphorylation levels of ERK, always correlated with subsequent neurite extension length (12–24 hours). Overexpression of all LP genes together, but not of each gene separately, enhanced NGF-induced neurite extension. The LP gene products showed distinct spatial localization. Thus, the LP genes appear to be the common decoders for neurite extension length regardless of neurotrophic factors, and they might function in distinct temporal and spatial manners during the latent process. Our findings provide molecular insight into the physiological meaning of the latent process as the preparation step for decoding information for future phenotypic change.

Construction of a genetic AND gate under a new standard for assembly of genetic parts

BackgroundAppropriate regulation of respective gene expressions is a bottleneck for the realization of artificial biological systems inside living cells. The modification of several promoter sequences is required to achieve appropriate regulation of the systems. However, a time-consuming process is required for the insertion of an operator, a binding site of a protein for gene expression, to the gene regulatory region of a plasmid. Thus, a standardized method for integrating operator sequences to the regulatory region of a plasmid is required.ResultsWe developed a standardized method for integrating operator sequences to the regulatory region of a plasmid and constructed a synthetic promoter that functions as a genetic AND gate. By standardizing the regulatory region of a plasmid and the operator parts, we established a platform for modular assembly of the operator parts. Moreover, by assembling two different operator parts on the regulatory region, we constructed a regulatory device with an AND gate function.ConclusionsWe implemented a new standard to assemble operator parts for construction of functional genetic logic gates. The logic gates at the molecular scale have important implications for reprogramming cellular behavior.