Exploiting the pathway structure of metabolism to reveal high-order epistasis

BackgroundBiological robustness results from redundant pathways that achieve an essential objective, e.g. the production of biomass. As a consequence, the biological roles of many genes can only be revealed through multiple knockouts that identify a set of genes as essential for a given function. The identification of such "epistatic" essential relationships between network components is critical for the understanding and eventual manipulation of robust systems-level phenotypes.ResultsWe introduce and apply a network-based approach for genome-scale metabolic knockout design. We apply this method to uncover over 11,000 minimal knockouts for biomass production in an in silico genome-scale model of E. coli. A large majority of these "essential sets" contain 5 or more reactions, and thus represent complex epistatic relationships between components of the E. coli metabolic network.ConclusionThe complex minimal biomass knockouts discovered with our approach illuminate robust essential systems-level roles for reactions in the E. coli metabolic network. Unlike previous approaches, our method yields results regarding high-order epistatic relationships and is applicable at the genome-scale.

[1]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[2]  E. Ruppin,et al.  Multiple knockout analysis of genetic robustness in the yeast metabolic network , 2006, Nature Genetics.

[3]  Steffen Klamt,et al.  Structural and functional analysis of cellular networks with CellNetAnalyzer , 2007, BMC Systems Biology.

[4]  G. Church,et al.  Modular epistasis in yeast metabolism , 2005, Nature Genetics.

[5]  Ádám M. Halász,et al.  Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities , 2005, Bioinform..

[6]  A. Burgard,et al.  Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions. , 2001, Biotechnology and bioengineering.

[7]  Robert Urbanczik,et al.  Functional stoichiometric analysis of metabolic networks , 2005, Bioinform..

[8]  B. Palsson,et al.  Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation , 2005, BMC Microbiology.

[9]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[10]  B. Palsson,et al.  Expanded Metabolic Reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an In Silico Genome-Scale Characterization of Single- and Double-Deletion Mutants , 2005, Journal of bacteriology.

[11]  Anne Kümmel,et al.  In silico genome-scale reconstruction and validation of the Staphylococcus aureus metabolic network. , 2005, Biotechnology and bioengineering.

[12]  B. Palsson,et al.  The underlying pathway structure of biochemical reaction networks. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Steffen Klamt,et al.  Computation of elementary modes: a unifying framework and the new binary approach , 2004, BMC Bioinformatics.

[14]  Robert Urbanczik,et al.  An improved algorithm for stoichiometric network analysis: theory and applications , 2005, Bioinform..

[15]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.

[16]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[17]  Jason A. Papin,et al.  Comparison of network-based pathway analysis methods. , 2004, Trends in biotechnology.

[18]  Roded Sharan,et al.  Systematic condition-dependent annotation of metabolic genes. , 2007, Genome research.

[19]  Bernhard O. Palsson,et al.  Expa: a Program for Calculating Extreme Pathways in Biochemical Reaction Networks , 2005, Bioinform..

[20]  Dietmar Schomburg,et al.  Observing local and global properties of metabolic pathways: "load points" and "choke points" in the metabolic networks , 2006, Bioinform..

[21]  H. Halberstam,et al.  North-Holland Mathematical Library , 2005 .

[22]  A. Burgard,et al.  Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization , 2003, Biotechnology and bioengineering.

[23]  Nagiza F. Samatova,et al.  Parallel out-of-core algorithm for genome-scale enumeration of metabolic systemic pathways , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[24]  Steffen Klamt,et al.  Minimal cut sets in biochemical reaction networks , 2004, Bioinform..

[25]  S. Klamt,et al.  Generalized concept of minimal cut sets in biochemical networks. , 2006, Bio Systems.