Showing papers by "Ines Thiele published in 2009"

Reconstruction of biochemical networks in microorganisms

Abstract: Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This Review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization.

Abstract: Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship.

Three-Dimensional Structural View of the Central Metabolic Network of Thermotoga maritima

Abstract: Metabolic pathways have traditionally been described in terms of biochemical reactions and metabolites. With the use of structural genomics and systems biology, we generated a three-dimensional reconstruction of the central metabolic network of the bacterium Thermotoga maritima. The network encompassed 478 proteins, of which 120 were determined by experiment and 358 were modeled. Structural analysis revealed that proteins forming the network are dominated by a small number (only 182) of basic shapes (folds) performing diverse but mostly related functions. Most of these folds are already present in the essential core (approximately 30%) of the network, and its expansion by nonessential proteins is achieved with relatively few additional folds. Thus, integration of structural data with networks analysis generates insight into the function, mechanism, and evolution of biological networks.

Metabolic network analysis integrated with transcript verification for sequenced genomes

Abstract: Iterative cycles of metabolic modeling and experimental open reading frame verification in Chlamydomonas reinhardtii lay the groundwork for more accurate gene annotation and provide resources for metabolic engineering. With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.

Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling

Abstract: Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases, phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and applied to this network to characterize input–output relationships. The input–output relationships enabled significant modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for further research in the task of mediating the Toll-like receptor signaling network and its effects.

Genome-Scale Reconstruction, Modeling, and Simulation of E. coli℉s Metabolic Network

Abstract: Since the release of the first genome-scale metabolic reconstruction of the E. coli metabolic network in 2000, there has been a growing number of researchers around the world adapting it for a broad range of studies (Feist 2008). The uses range from practical applications to obtaining basic biological understanding of cellular behavior. This range of uses is further expected to expand as the reconstruction broadens in scope and as new in silico methods are developed, implemented, and put to use.

Correction: Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling

Abstract: Dataset S1 contains some errors. The correct dataset is available here: Click here for additional data file.(53K, zip)

A stoichiometric model of Escherichia coli 's macromolecular synthesis machinery and its integration with metabolism

Abstract: Systems biology is a rapidly growing discipline. It is widely believed to have a broad transformative potential on both basic and applied studies in the life sciences. In particular, biochemical network reconstructions are playing a key role as they provide a framework for investigation of the mechanisms underlying the genotype- phenotype relationship. In this thesis, the procedure to reconstruct metabolic networks is illustrated and extended to other cellular processes. In particular, the constraint -based reconstruction and analysis approach was applied to reconstruct the transcriptional and translational (tr/tr) machinery of Escherichia coli. This reconstruction, denoted 'Expression-matrix'/ (E-matrix), represents stoichiometrically all known proteins and RNA species involved in the macromolecular synthesis machinery. It accounts for all biochemical transformations to produce active, functional proteins, tRNAs, and rRNAs known to be involved in macromolecular synthesis in E. coli. An initial study investigated basic properties of the E- matrix, including its capability to produce ribosomes, which was found to be in good agreement with experimental data from literature. Furthermore, quantitative gene expression data could be integrated with, and analyzed in the context of, the resulting constraint-based model. Adding mathematically derived constraints to couple certain reactions in the model allowed the quantitative representation of the size of steady state protein and RNA pools. Furthermore, the E-matrix was integrated with the genome-scale E. coli metabolic model and extended the transcriptional and translational reactions to encompass genes encoding all the respective metabolic enzymes. The resulting Metabolite-Expression-matrix (MExv matrix), has exceeds the predictive capacity of the metabolic model and it can, for example, be used to predict the biomass yield since it represents the production of almost 2,000 proteins. E. coli 's ME-matrix is the first of its kind and represents a milestone in systems biology as demonstrates how to quantitatively integrate 'omics'- datasets into a network context, and thus, to study the mechanistic principles underlying the genotype-phenotype relationship. Possible applications are just beginning to become apparent and may include protein engineering, interpretation of adaptive evolution, and minimal genome design. An integration of the ME-matrix with remaining cellular processes, such as regulation, signaling, and replication, will be a next step to complete the first whole-cell model.