Carbon sequestration in Synechococcus Sp.: from molecular machines to hierarchical modeling.
The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.
Heffelfinger
Grant S
GS
Sandia National Laboratories, Building 701/2101, MS-0885, 1515 Eubank SE, Albuquerque, NM 87123, USA. gsheffe@sandia.gov
Martino
Anthony
A
Gorin
Andrey
A
Xu
Ying
Y
Rintoul
Mark D
MD
Geist
Al
A
Al-Hashimi
Hashim M
HM
Davidson
George S
GS
Faulon
Jean Loup
JL
Frink
Laurie J
LJ
Haaland
David M
DM
Hart
William E
WE
Jakobsson
Erik
E
Lane
Todd
T
Li
Ming
M
Locascio
Phil
P
Olken
Frank
F
Olman
Victor
V
Palenik
Brian
B
Plimpton
Steven J
SJ
Roe
Diana C
DC
Samatova
Nagiza F
NF
Shah
Manesh
M
Shoshoni
Arie
A
Strauss
Charlie E M
CE
Thomas
Edward V
EV
Timlin
Jerilyn A
JA
Xu
Dong
D
D000465:Algorithms; D002244:Carbon / Q000378:metabolism* / Q000502:physiology; D000458:Cyanobacteria / Q000378:metabolism / Q000502:physiology*; D016678:Genome; D013058:Mass Spectrometry; D008954:Models, Biological; D015233:Models, Statistical; D012106:Research / Q000639:trends; D012984:Software
D002244:Carbon