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

Arthur Grossman

Photosynthetic organisms offer a biological paradigm for the conversion of 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 H₂-production systems. However, the complexity of the metabolism linked to H₂-production pathways in this organism demands the development of 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—assuming transferability of knowledge from prototypical systems may lead to fundamental errors in model interpretation.

At the same time, forthcoming petascale computers (peta- = 10¹⁵) will 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 aren't enough—user interfaces should allow non-specialists to take advantage of this new generation of computing speed. Through the DOE leadership-class facilities hosting these machines, problems that were once the realm of fantasy can now be solved, provided 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

1. Development of a user interface capable of interaction 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;
   
   
2. Development of middleware able to distribute jobs efficiently over a network of processing cores, compile and analyze data according to users' specification on-the-fly, and automatically adapt to changing network conditions (e.g., node failures);
   
   
3. Exploration of energy metabolism and regulation in C. reinhardtii using state-of-the-art proteomics and metabolomics technologies;
   
   
4. Development of a sub-cellular model of C. reinhardtii metabolism based on enzymological rate equations and focused on central and energy-related metabolism;
   
   
5. Large-scale parameter estimation and interaction studies by discrete sampling of user-defined parameter spaces (uncoupled parallelism);
   
   
6. Perturbative studies in real-time in a distributed cellular model, in which each process or coupled group of processes is distributed over the petascale machine (coupled parallelism).

Studies will provide a fundamental understanding of essential metabolic pathways in photosynthetic green algae and enable rational engineering and optimization of those pathways. 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 the Department'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.

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