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Title: Addressing Unknown Constants and Metabolic Network Behaviors Through Petascale Computing: Understanding H2 Production in Green Algae

Abstract

The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and, (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H{sub 2}-producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a modelmore » with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems ranging from distributed grids to unified petascale architectures.« less

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
982280
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of SciDAC 2007; Journal of Physics: Conference Series 78; Related Information: Article No. 012011
Country of Publication:
United States
Language:
English
Subject:
08 HYDROGEN; 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; ALGAE; BASIC; BIOLOGY; C CODES; CATALYSTS; CHLAMYDOMONAS; COMPARTMENTS; DIFFERENTIAL EQUATIONS; DNA; ENZYMES; FERMENTATION; GENES; GLYCOLYSIS; GRIDS; KINETICS; OPTIMIZATION; PHYSICS; SOLUTIONS; SPACE; TRANSFORMATIONS; Basic Sciences

Citation Formats

Chang, C., Alber, D., Graf, P., and Seibert, M. Addressing Unknown Constants and Metabolic Network Behaviors Through Petascale Computing: Understanding H2 Production in Green Algae. United States: N. p., 2007. Web. doi:10.1088/1742-6596/78/1/012011.
Chang, C., Alber, D., Graf, P., & Seibert, M. Addressing Unknown Constants and Metabolic Network Behaviors Through Petascale Computing: Understanding H2 Production in Green Algae. United States. doi:10.1088/1742-6596/78/1/012011.
Chang, C., Alber, D., Graf, P., and Seibert, M. Mon . "Addressing Unknown Constants and Metabolic Network Behaviors Through Petascale Computing: Understanding H2 Production in Green Algae". United States. doi:10.1088/1742-6596/78/1/012011.
@article{osti_982280,
title = {Addressing Unknown Constants and Metabolic Network Behaviors Through Petascale Computing: Understanding H2 Production in Green Algae},
author = {Chang, C. and Alber, D. and Graf, P. and Seibert, M.},
abstractNote = {The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and, (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H{sub 2}-producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the model's kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems ranging from distributed grids to unified petascale architectures.},
doi = {10.1088/1742-6596/78/1/012011},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}

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