Compensatory ability to null mutation in metabolic networks

Robustness is an inherent property of biological system. It is still a limited understanding of how it is accomplished at the cellular or molecular level. To this end, this article analyzes the impact degree of each reaction to others, which is defined as the number of cascading failures of following and/or forward reactions when an initial reaction is deleted. By analyzing more than 800 organism's metabolic networks, it suggests that the reactions with larger impact degrees are likely essential and the universal reactions should also be essential. Alternative metabolic pathways compensate null mutations, which represents that average impact degrees for all organisms are small. Interestingly, average impact degrees of archaea organisms are smaller than other two categories of organisms, eukayote and bacteria, indicating that archaea organisms have strong robustness to resist the various perturbations during the evolution process. The results show that scale‐free feature and reaction reversibility contribute to the robustness in metabolic networks. The optimal growth temperature of organism also relates the robust structure of metabolic network. Biotechnol. Bioeng. 2009;103: 361–369. © 2008 Wiley Periodicals, Inc.

[1]  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.

[2]  S. Lee,et al.  Systems metabolic engineering of Escherichia coli for L-threonine production , 2007, Molecular systems biology.

[3]  R. Kaul,et al.  A comprehensive transposon mutant library of Francisella novicida, a bioweapon surrogate , 2007, Proceedings of the National Academy of Sciences.

[4]  Steffen Klamt,et al.  Computing Knock-Out Strategies in Metabolic Networks , 2007, J. Comput. Biol..

[5]  Erwin P. Gianchandani,et al.  Predicting biological system objectives de novo from internal state measurements , 2008, BMC Bioinformatics.

[6]  D. Fell,et al.  The small world inside large metabolic networks , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  Dongxiao Zhu,et al.  BMC Bioinformatics BioMed Central , 2005 .

[8]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[9]  S. Schuster,et al.  Analysis of structural robustness of metabolic networks. , 2004, Systems biology.

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

[11]  Oliver Ebenhöh,et al.  Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution , 2005, Journal of Molecular Evolution.

[12]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[13]  S. Ehrlich,et al.  Essential Bacillus subtilis genes , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  E. Rubin,et al.  Genes required for mycobacterial growth defined by high density mutagenesis , 2003, Molecular microbiology.

[15]  C. Hutchison,et al.  Essential genes of a minimal bacterium. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Ann E Loraine,et al.  Large‐scale transposon mutagenesis of Mycoplasma pulmonis , 2008, Molecular microbiology.

[17]  J. Stelling,et al.  Robustness of Cellular Functions , 2004, Cell.

[18]  D. Fell,et al.  Is maximization of molar yield in metabolic networks favoured by evolution? , 2008, Journal of theoretical biology.

[19]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[20]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[21]  E. Ruppin,et al.  Regulatory on/off minimization of metabolic flux changes after genetic perturbations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  K. Ulgen,et al.  Metabolic pathway analysis of enzyme-deficient human red blood cells. , 2004, Bio Systems.

[23]  Madhukar S. Dasika,et al.  A computational framework for the topological analysis and targeted disruption of signal transduction networks. , 2006, Biophysical journal.

[24]  S. Schuster,et al.  Metabolic network structure determines key aspects of functionality and regulation , 2002, Nature.

[25]  J. W. Campbell,et al.  Experimental Determination and System Level Analysis of Essential Genes in Escherichia coli MG1655 , 2003, Journal of bacteriology.

[26]  Markus J. Herrgård,et al.  Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. , 2006, Genome research.

[27]  Stefan Schuster,et al.  Adenine and adenosine salvage pathways in erythrocytes and the role of S‐adenosylhomocysteine hydrolase , 2005, The FEBS Journal.

[28]  S. Schuster,et al.  Structural robustness of metabolic networks with respect to multiple knockouts. , 2008, Journal of theoretical biology.

[29]  B. Palsson,et al.  Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data* , 2007, Journal of Biological Chemistry.

[30]  J. Stelling,et al.  Combinatorial Complexity of Pathway Analysis in Metabolic Networks , 2004, Molecular Biology Reports.

[31]  F. Blattner,et al.  In silico design and adaptive evolution of Escherichia coli for production of lactic acid. , 2005, Biotechnology and bioengineering.

[32]  J. C. Nacher,et al.  Two complementary representations of a scale-free network , 2005 .

[33]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[34]  A. Barabasi,et al.  Predicting synthetic rescues in metabolic networks , 2008, Molecular systems biology.

[35]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[36]  Adam M. Feist,et al.  The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli , 2008, Nature Biotechnology.

[37]  S. Lee,et al.  Metabolic engineering of Escherichia coli for the production of l-valine based on transcriptome analysis and in silico gene knockout simulation , 2007, Proceedings of the National Academy of Sciences.

[38]  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.

[39]  Christoph Wittmann,et al.  Metabolic pathway analysis for rational design of L-methionine production by Escherichia coli and Corynebacterium glutamicum. , 2006, Metabolic engineering.

[40]  A. Ramezanpour,et al.  Generating correlated networks from uncorrelated ones. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  B. Palsson,et al.  Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[42]  J. Nielsen.  Principles of optimal metabolic network operation , 2007, Molecular systems biology.

[43]  C. Maranas,et al.  An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. , 2006, Metabolic engineering.

[44]  Tatsuya Akutsu,et al.  Correlation between structure and temperature in prokaryotic metabolic networks , 2007, BMC Bioinformatics.

[45]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[46]  Erwin P. Gianchandani,et al.  Flux balance analysis in the era of metabolomics , 2006, Briefings Bioinform..

[47]  Martin Rosenberg,et al.  Identification of Critical Staphylococcal Genes Using Conditional Phenotypes Generated by Antisense RNA , 2001, Science.

[48]  Bernhard O. Palsson,et al.  Three factors underlying incorrect in silico predictions of essential metabolic genes , 2015 .

[49]  S. Lee,et al.  Metabolic Engineering of Escherichia coli for Enhanced Production of Succinic Acid, Based on Genome Comparison and In Silico Gene Knockout Simulation , 2005, Applied and Environmental Microbiology.

[50]  Masanori Arita.  The metabolic world of Escherichia coli is not small. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[52]  J. Schwender,et al.  Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds , 2004, Nature.

[53]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

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

[55]  Yudi Yang,et al.  Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production , 2006, Applied Microbiology and Biotechnology.

[56]  Andrés Moya,et al.  Structural analyses of a hypothetical minimal metabolism , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.