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Title: The Organization of Complex Metabolic Networks

Authors:
Publication Date:
Research Org.:
Albert-Laszlo Barabasi
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
881797
Report Number(s):
DOE/ER/25495
None; TRN: US200716%%143
DOE Contract Number:
FG02-01ER25495
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ANTIBIOTICS; ARCHITECTURE; BIOLOGICAL PATHWAYS; ENZYMES; ESCHERICHIA COLI; FUNCTIONALS; METABOLISM; METABOLITES; PROBABILITY; SACCHAROMYCES CEREVISIAE; STOICHIOMETRY; SUBSTRATES; TARGETS; TOPOLOGY; Metabolic; Complex; Networks

Citation Formats

Albert-Laszlo Barabasi. The Organization of Complex Metabolic Networks. United States: N. p., 2006. Web. doi:10.2172/881797.
Albert-Laszlo Barabasi. The Organization of Complex Metabolic Networks. United States. doi:10.2172/881797.
Albert-Laszlo Barabasi. Mon . "The Organization of Complex Metabolic Networks". United States. doi:10.2172/881797. https://www.osti.gov/servlets/purl/881797.
@article{osti_881797,
title = {The Organization of Complex Metabolic Networks},
author = {Albert-Laszlo Barabasi},
abstractNote = {},
doi = {10.2172/881797},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}

Technical Report:

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  • This paper describes an approach that uses methods for automated sequence analysis and multiple databases accessed through an object+attribute view of the data, together with metabolic pathways, reaction equations, and compounds parsed into a logical representation from the Enzyme and Metabolic Pathway Database, as the sources of data for automatically reconstructing a weighted partial metabolic network for a prokaryotic organism. Additional information can be provided interactively by the expert user to guide reconstruction.
  • We parallelized the serial extreme pathways algorithm presented by Schilling et al., in J. Theor. Biol. 203 (2000) using the Message Passing Interface (MPI). The parallel algorithm exhibits super-linear scalability because the number of independence tests performed decreases as the number of MPI nodes increases. A subsystem of the metabolic network of Escherichia coli with 140 reactions and 96 metabolites (without preprocessing) is used as a benchmark. The extreme pathways of this system are computed in under 280 seconds using 70 2.4 GHz Intel Pentium-IV CPUs with Myrinet interconnection among the dual-CPU nodes of the Linux cluster.
  • During this project we have pioneered the development of integrated experimental-computational technologies for the quantitative dissection of metabolism in hydrogen and biofuel producing microorganisms (i.e. C. acetobutylicum and various cyanobacteria species). The application of these new methodologies resulted in many significant advances in the understanding of the metabolic networks and metabolism of these organisms, and has provided new strategies to enhance their hydrogen or biofuel producing capabilities. As an example, using mass spectrometry, isotope tracers, and quantitative flux-modeling we mapped the metabolic network structure in C. acetobutylicum. This resulted in a comprehensive and quantitative understanding of central carbon metabolism thatmore » could not have been obtained using genomic data alone. We discovered that biofuel production in this bacterium, which only occurs during stationary phase, requires a global remodeling of central metabolism (involving large changes in metabolite concentrations and fluxes) that has the effect of redirecting resources (carbon and reducing power) from biomass production into solvent production. This new holistic, quantitative understanding of metabolism is now being used as the basis for metabolic engineering strategies to improve solvent production in this bacterium. In another example, making use of newly developed technologies for monitoring hydrogen and NAD(P)H levels in vivo, we dissected the metabolic pathways for photobiological hydrogen production by cyanobacteria Cyanothece sp. This investigation led to the identification of multiple targets for improving hydrogen production. Importantly, the quantitative tools and approaches that we have developed are broadly applicable and we are now using them to investigate other important biofuel producers, such as cellulolytic bacteria.« less
  • The goal of this project is to identify gene networks that are critical for efficient biohydrogen production by leveraging variation in gene content and gene expression in independently isolated Rhodopseudomonas palustris strains. Coexpression methods were applied to large data sets that we have collected to define probabilistic causal gene networks. To our knowledge this a first systems level approach that takes advantage of strain-to strain variability to computationally define networks critical for a particular bacterial phenotypic trait.
  • In the first two years of this research we focused on the development of a DNA microarray for transcriptional studies in the photosynthetic organism Synechocystis and the elucidation of the metabolic pathway for biopolymer synthesis in this organism. In addition we also advanced the molecular biological tools for metabolic engineering of biopolymer synthesis in Synechocystis and initiated a series of physiological studies for the elucidation of the carbon fixing pathways and basic central carbon metabolism of these organisms. During the last two-year period we focused our attention on the continuation and completion of the last task, namely, the development ofmore » tools for basic investigations of the physiology of these cells through, primarily, the determination of their metabolic fluxes. The reason for this decision lies in the importance of fluxes as key indicators of physiology and the high level of information content they carry in terms of identifying rate limiting steps in a metabolic pathway. While flux determination is a well-advanced subject for heterotrophic organisms, for the case of autotrophic bacteria, like Synechocystis, some special challenges had to be overcome. These challenges stem mostly from the fact that if one uses {sup 13}C labeled CO{sub 2} for flux determination, the {sup 13}C label will mark, at steady state, all carbon atoms of all cellular metabolites, thus eliminating the necessary differentiation required for flux determination. This peculiarity of autotrophic organisms makes it imperative to carry out flux determination under transient conditions, something that had not been accomplished before. We are pleased to report that we have solved this problem and we are now able to determine fluxes in photosynthetic organisms from stable isotope labeling experiments followed by measurements of label enrichment in cellular metabolites using Gas Chromatography-Mass Spectrometry. We have conducted extensive simulations to test the method and also are presently validating it experimentally using data generated in collaboration with a research group at Purdue University. As result of these studies we can now determine, for the first time, fluxes in photosynthetic organisms and, eventually, in plants.« less