Genomics:GTL

Genomes to Life Contractor-Grantee Workshop II
February 29-March 2, 2004, Washington, D.C.

Genomics:GTL Program Projects

Harvard Medical School

Microbial Ecology, Proteogenomics, and Computational Optima

1

Flux Balance Based Whole-Cell Modeling of the Marine Cyanobacterium Prochlorococcus

George M. Church¹ (g1m1c1@arep.med.harvard.edu), Daniel Segre¹, Xiaoxia Lin¹, Kyriacos Leptos¹, Jeremy Zucker², Aaron Brandes², Dat Nguyen¹, and Jay MacPhee¹

¹Department of Genetics, Harvard Medical School, Boston, MA and ²Dana-Farber Cancer Institute, Boston, MA
http://arep.med.harvard.edu/DOEGTL/

The marine unicellular cyanobacterium Prochlorococcus is the dominant oxygenic phototroph in the tropical and subtropical oceans, and contributes to a significant fraction of the global photosynthesis (Rocap et al., 2003). Our goal in this project is to develop whole-cell mathematical models for studying the metabolism of this cyanobacterium using flux balance based approaches, which has proven very successful in performing whole-cell modeling for a variety of microorganisms (Price et al., 2003). An especially interesting challenge is the inclusion of photosynthesis pathway in our model. Night-day cycles are known to play a central role in the metabolism of Prochlrococcus, and different strains are adapted to different light intensities and wavelengths. Flux balance models give the opportunity to study quantitatively the influence of photon fluxes on global cell behavior.

The completion of the Prochlorococcus genome sequencing has provided us a promising starting point for building whole-cell flux balance models of this bacterium. By utilizing an automatic bioinformatics pipeline which was recently developed (Segre et al., 2003), we have combined the genome annotation of Prochlorococcus MED4, a high-light-adapted strain, with an extensive pathway/genome database, MetaCyc (Karp et al., 2002), and generated a Prochlorococcus MED4 pathway database. This organism-specific pathway database is then used to generate flux balance models in which given the stoichiometric matrix representing the metabolic networks and limits on nutrient uptakes, linear programming (LP) or other optimization techniques are used to calculate the flux distribution that reflects the metabolic state of the cell. Our preliminary studies have shown that a substantial number of the biomass components can not be produced with the current identified metabolic networks. This is mainly due to i) incomplete annotation of the genome, for example, not identifying a gene encoding the enzyme catalyzing a metabolic reaction in the biosynthesis pathway of a certain amino acid; and ii) incomplete inclusion of pathways from the MetaCyc database. In order to generate flux balance models that can capture the primary components of the metabolic networks of Prochlorococcus and then can be used to study its genotype-metabolic phenotype relationship under varying conditions, we are currently improving and refining the models by i) using network debugging methods to identify missing reactions/pathways in the constructed in silico metabolic network; ii) including additional reactions/pathways based on information from a variety of other sources, such as identification of enzymes through manual search of homologs, proteomic data, existing knowledge about the bacterium’s metabolism, etc.

Another important requirement for the construction of whole-cell flux balance models of Prochlorococcus is to incorporate an appropriate set of transport reactions, which are currently lacking in the MetaCyc database. Approximately 50 transport proteins have been classified according to the Transport Classification system, which includes substrate specificity, through a combination of TC-BLAST, pfam, COG, and phylogenetic tree analysis (available at http://membranetransport.org). The transport reactions associated with these proteins can be deduced directly from their Transport classification number. We are working closely with the curators of MetaCyc and the Membrane transport database to incorporate these reactions into the pathway/genome database for Prochlorococcus.

Upon the successful construction of whole-cell flux balance models for Prochlorococcus, we plan to i) investigate how the metabolic network of this cyanobacterium works to enable it grow/live under its natural environmental conditions, in specific, in the light and in the dark; ii) investigate the differences between high-light-adapted strains, for example, MED4, and low-light-adapted strains, for example, MIT9313, by comparing the structures of their metabolic networks and the calculated flux distributions under varying conditions; and iii) investigate the effect of gene knockouts on cellular properties, such as growth rate and photosynthesis, using the MOMA approach developed earlier in the Church lab (Segre et al., 2002). Hypotheses generated with flux balance models will be tested experimentally using expression and proteomic data.

Reference

1. Karp PD, Riley M, Paley S, and Pellegrini-Toole A (2002) The MetaCyc Database. Nucleic Acids Research 30(1):59-61.
2. Price ND, Papin JA, Schilling CH, and Palsson BO (2003) Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21(4): 162-169.
3. Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, Arellano A, Coleman M, Hauser L, Hess WR, Johnson ZI, Land M, Lindell D, Post AF, Regala W, Shah M, Shaw SL, Steglich C, Sullivan MB, Ting CS, Tolonen A, Webb EA, Zinser ER, and Chisholm SW (2003) Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 424(6952):1042-1047.
4. Segre D, Vitkup D, and Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc. Nat. Acad. Sci USA 99: 15112-7.
5. Segre D, Zucker J, Katz J, Lin X, D’haeseleer P, Rindone W, Karchenko P, Nguyen D, Wright M, and Church GM (2003) From annotated genomes to metabolic flux models and kinetic parameter fitting. Omics 7:301-16.