Metabolic flux estimation in mammalian cell cultures

Metabolic flux analysis with its ability to quantify cellular metabolism is an attractive tool for accelerating cell line selection, medium optimization, and other bioprocess development activities. In the stoichiometric flux estimation approach, unknown fluxes are determined using intracellular metabolite mass balance expressions and measured extracellular rates. The simplicity of the stoichiometric approach extends its application to most cell culture systems, and the steps involved in metabolic flux estimation by the stoichiometric method are presented in detail in this chapter. Specifically, overdetermined systems are analyzed since the extra measurements can be used to check for gross measurement errors and system consistency. Cell-specific rates comprise the input data for flux estimation, and the logistic modeling approach is described for robust-specific rate estimation in batch and fed-batch systems. A simplified network of mammalian cell metabolism is used to illustrate the flux estimation procedure, and the steps leading up the consistency index determination are presented. If gross measurement errors are detected, a technique for determining the source of gross measurement error is also described. A computer program that performs most of the calculation described in this chapter is presented, and references to flux estimation software are provided. The procedure presented in this chapter should enable rapid metabolic flux estimation in any mammalian cell bioreaction network by the stoichiometric approach.

Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids

A molecular modeling strategy to screen the capacity of known enzymes to catalyze the reactions of non-native substrates is presented. The binding of pyruvic acid and non-native ketoacids in the active site of pyruvate ferredoxin oxidoreductase was examined using docking analysis, and our results suggest that enzyme-non-native ketoacid-bound species are feasible. Quantum mechanics/molecular mechanics methods were then used to study the geometry of the covalent intermediate formed from the enzyme and the various ketoacids. Finally, quantum mechanical methods were used to study the decarboxylation reaction of 2-keto acids at the mechanistic level. This hierarchical screening ranked the substrates from those that cannot be accommodated by the enzyme (phenyl pyruvate) to those whose conversion rate would most closely approach that of the native substrate (2-ketobutanoic acid and 2-ketovaleric acid). Most notably, our investigation suggests that novel pathways generated using generalized enzyme actions may be screened using the hierarchical approach employed here.

FOLy: an integrated database for the classification and functional annotation of fungal oxidoreductases potentially involved in the degradation of lignin and related aromatic compounds

The breakdown of lignin by fungi is a key step during carbon recycling in terrestrial ecosystems. This process is of great interest for green and white biotechnological applications. Given the importance of these enzymatic processes, we have classified the enzymes potentially involved in lignin catabolism into sequence-based families and integrated them in a newly developed database, designated Fungal Oxidative Lignin enzymes (FOLy). Families were defined after sequence similarity searches starting from protein sequences and validated by the convergence of results with biochemical experiments reported in the literature. The resulting database was applied as a tool for the functional annotation of genomes from different fungi, namely (i) the Basidiomycota Coprinopsis cinerea, Phanerochaete chrysosporium and Ustilago maydis and (ii) the Ascomycota Aspergillus nidulans and Trichoderma reesei. Genomic comparison of the oxidoreductases of these fungi revealed significant differences in the putative enzyme arsenals. Two Ascomycota fungal genomes were annotated and new candidate genes were identified that could be useful for lignin degradation and (or) melanin synthesis, and their function investigated experimentally. This database efforts aims at providing the means to get new insights for the understanding and biotechnological exploitation of the lignin degradation. A WWW server giving access to the routinely updated FOLy classifications of enzymes potentially involved in lignin degradation can be found at http://foly.esil.univ-mrs.fr.

GNARE: automated system for high-throughput genome analysis with grid computational backend

Recent progress in genomics and experimental biology has brought exponential growth of the biological information available for computational analysis in public genomics databases. However, applying the potentially enormous scientific value of this information to the understanding of biological systems requires computing and data storage technology of an unprecedented scale. The Grid, with its aggregated and distributed computational and storage infrastructure, offers an ideal platform for high-throughput bioinformatics analysis. To leverage this we have developed the Genome Analysis Research Environment (GNARE)--a scalable computational system for the high-throughput analysis of genomes, which provides an integrated database and computational backend for data-driven bioinformatics applications. GNARE efficiently automates the major steps of genome analysis including acquisition of data from multiple genomic databases; data analysis by a diverse set of bioinformatics tools; and storage of results and annotations. High-throughput computations in GNARE are performed using distributed heterogeneous Grid computing resources such as Grid2003, TeraGrid, and the DOE Science Grid. Multi-step genome analysis workflows involving massive data processing, the use of application-specific tools and algorithms and updating of an integrated database to provide interactive web access to results are all expressed and controlled by a "virtual data" model which transparently maps computational workflows to distributed Grid resources. This paper describes how Grid technologies such as Globus, Condor, and the Gryphyn Virtual Data System were applied in the development of GNARE. It focuses on our approach to Grid resource allocation and to the use of GNARE as a computational framework for the development of bioinformatics applications.

BRENDA: a resource for enzyme data and metabolic information

BRENDA (BRaunschweig ENzyme DAtabase), founded in 1987 by Dietmar Schomburg, is a comprehensive protein function database, containing enzymatic and metabolic information extracted from the primary literature. Presently, the database holds data on more than 40 000 enzymes and 4460 different organisms, and includes information about enzyme-ligand relationships with numerous chemical compounds. The collection of molecular and biochemical information in BRENDA provides a fundamental resource for research in biotechnology, pharmacology, medicinal diagnostics, enzyme mechanics, and metabolism. BRENDA is accessible free of charge to the academic community at http://www.brenda.uni-koeln.de/; commercial users need a license available from http://www.science-factory.com/