National Renewable Energy Laboratory
Computational Sciences
Chlamydomonas reinhardtii Systems Biology
Green Energy: Advancing Bio-hydrogen
Filling Knowledge Gaps in Biological Networks: Integrated Global Approaches to Understand H2 Metabolism in Chlamydomonas reinhardtii
National Renewable Energy Laboratory
Michael Seibert, Christopher Chang, Peter Graf, Kwiseon Kim
Colorado School of Mines
Matthew Posewitz, Glenn Murray
Carnegie Institution
Photosynthetic organisms offer a biological paradigm for converting light energy into chemical forms. Some of these organisms are capable of transducing this energy directly into H2. The green alga Chlamydomonas reinhardtii is an example of one such organism that could play a major role in future commercial systems for H2 production. However, the metabolism linked to H2-production pathways in this organism are complex. Consequently, it demands developing a computational model by which to integrate and understand disparate observations over various mutations and environmental conditions.
The grand scientific challenge of creating a complete, in silico simulation of a living cell still faces daunting obstacles. Biomolecular science has proceeded by studying prototypical systems, with the understanding that the knowledge gained is transferable to other systems to some extent. However, for quantitative modeling of a single system, complete knowledge must be available for that particular system to achieve consistency. One must understand that the limited transferability of knowledge from prototypical systems may lead to fundamental errors in model interpretation.
At the same time, forthcoming petascale computers (peta- = 1015) offer unprecedented quantities of raw computing power, dynamic memory, persistent storage, and network interconnect speed. To take full advantage of these unique, cutting-edge architectures, software applications must be written, tested, and modified. Furthermore, petascaling compute engines are not enough—user interfaces should allow non-specialists to take advantage of this new generation of computing speed. Through the U.S. Department of Energy (DOE) leadership-class facilities that host these machines, problems that were once the realm of fantasy can now be solved, provided that scalable, intuitive software tools are in place.
This project combines the quest for understanding biology relevant to energy production with the availability of petascale computing through the following:
Development of a user interface capable of interacting with standard systems biology toolsets, semi-automated merging of experimental -omics data with computational models, petascale simulation setup and job submission, and analysis and visualization of data distilled from the simulation.
Development of software for sampling steady-state concentrations and fluxes over many parametric dimensions, and storage in a form suitable for analysis and visualization of this abstract space with high-performance-computing visualization tools.
Software development for local and global optimization of metabolite concentrations or fluxes over user-defined subspaces of large kinetic parameter spaces, with objective functions including either absolute minmax conditions ("optimization") or experimental data constraints ("fitting").
Exploration of energy metabolism and regulation in C. reinhardtii using state-of-the-art proteomics and metabolomics technologies.
Development of a sub-cellular model of C. reinhardtii metabolism based on enzymological rate equations and focused on central and energy-related metabolism, that is simultaneously built from modular components in a standardized language to allow maximum reusability by and data provenance for the general biochemical community.
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Large-scale parameter estimation, high-throughput data fitting, and optimization studies within user-defined parameter spaces on parallel machines.
Studies will provide a fundamental understanding of metabolic response sensitivity to kinetic parameters in essential pathways of photosynthetic green algae, to enable rational engineering and optimization of those pathways through targeted mutagenesis. It will also serve a broader community by providing information critical to understanding other hydrogen-metabolizing and fermentative organisms of interest in renewable energy research.
The Department of Energy's mission is to advance the national, economic, and energy security of the United States. Within the Genomics:GTL program, systems biology has been identified as playing a key role in meeting DOE's mission. Furthermore, the "hydrogen economy" has been established as an important component in a multi-faceted strategy for energy independence and renewability.
Funding Partners
Office of Science — Office of Advanced Scientific Computing Research, and Office of Biological and Environmental Research.
