Skip to main content
SpringerLink
Log in

Metabolic Systems Biology

  • Living reference work entry
  • First Online:
Encyclopedia of Complexity and Systems Science

  • 141 Accesses

Glossary

Bibliome:

The collection of primary literature, review literature, and textbooks on a particular topic. Biochemically, genetically and genomically (BiGG) structured reconstruction.

A structured genome-scale metabolic network reconstruction which incorporates knowledge about the genomic, proteomic, and biochemical components, including relationships between each component in a particular organism or cell (see section “Reconstructions, Knowledge Bases, and Models”).

Biomass function:

A pseudo-reaction representing the stoichiometric consumption of metabolites necessary for cellular growth (i.e., to produce biomass). When this pseudo-reaction is placed in a model, a flux through it represents the in silico growth rate of the organism or population (see section “Constraint-Based Methods of Analysis”).

Constraint-based reconstruction and analysis (COBRA):

A set of approaches for constructing manually curated, stoichiometric network reconstructions and analyzing the resulting models...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Bibliography

Primary Literature

  • Almaas E, Kovacs B, Vicsek T, Oltvai ZN, Barabasi AL (2004) Global organization of metabolic fluxes in the bacterium Escherichia coli. Nature 427:839–843

    Article  ADS  Google Scholar 

  • Almaas E, Oltvai ZN, Barabasi AL (2005) The activity reaction core and plasticity of metabolic networks. PLoS Comput Biol 1:e68

    Article  ADS  Google Scholar 

  • Alper H, Jin Y, Moxley JF, Stephanopoulos G (2005) Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab Eng 7:155–164

    Article  Google Scholar 

  • Antoniewicz MR, Kelleher JK, Stephanopoulos G (2006) Determination of confidence intervals of metabolic fluxes estimated from stable isotope measurements. Metab Eng 8:324–337

    Article  Google Scholar 

  • Barrett CL, Palsson BO (2006) Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLoS Comput Biol 2:e52

    Article  ADS  Google Scholar 

  • Barrett CL, Herring CD, Reed JL, Palsson BO (2005) The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states. Proc Natl Acad Sci U S A 102:19103–19108

    Article  ADS  Google Scholar 

  • Beard DA, Liang SD, Qian H (2002) Energy balance for analysis of complex metabolic networks. Biophys J 83:79–86

    Article  Google Scholar 

  • Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox. Nat Protoc 2:727–738

    Article  Google Scholar 

  • Beg QK, Vazquez A, Ernst J et al (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A 104:12663–12668

    Article  ADS  Google Scholar 

  • Blank LM, Kuepfer L, Sauer U (2005) Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol 6:R49

    Article  Google Scholar 

  • Bochner BR, Gadzinski P, Panomitros E (2001) Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 11:1246–1255

    Article  Google Scholar 

  • Brumen M, Heinrich R (1984) A metabolic osmotic model of human erythrocytes. Biosystems 17:155–169

    Article  Google Scholar 

  • Burgard AP, Maranas CD (2003) Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol Bioeng 82:670–677

    Article  Google Scholar 

  • Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84:647–657

    Article  Google Scholar 

  • Burgard AP, Nikolaev EV, Schilling CH, Maranas CD (2004) Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 14:301–312

    Article  Google Scholar 

  • Cakir T, Patil KR, Onsan Z, Ulgen KO, Kirdar B, Nielsen J (2006) Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2:50

    Article  Google Scholar 

  • Caspi R, Foerster H, Fulcher CA et al (2008) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 36(Database issue):D623–D631

    Google Scholar 

  • Church GM (2005) From systems biology to synthetic biology. Mol Syst Biol 1:0032

    Google Scholar 

  • Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429:92–96

    Article  ADS  Google Scholar 

  • Duarte NC, Becker SA, Jamshidi N et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A 104:1777–1782

    Article  ADS  Google Scholar 

  • Edwards JS, Palsson BO (2000a) Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1:1

    Article  Google Scholar 

  • Edwards JS, Palsson BO (2000b) Robustness analysis of the Escherichia coli metabolic network. Biotechnol Prog 16:927–939

    Article  Google Scholar 

  • Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19:125–130

    Article  Google Scholar 

  • Edwards JS, Ramakrishna R, Palsson BO (2002) Characterizing the metabolic phenotype: a phenotype phase plane analysis. Biotechnol Bioeng 77:27–36

    Article  Google Scholar 

  • Famili I, Mahadevan R, Palsson BO (2005) K-cone analysis: determining all candidate values for kinetic parameters on a network scale. Biophys J 88:1616–1625

    Article  Google Scholar 

  • Feist AM, Henry CS, Reed JL et al (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121

    Article  Google Scholar 

  • Fell DA, Small JR (1986) Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J 238:781–786

    Article  Google Scholar 

  • Gagneur J, Klamt S (2004) Computation of elementary modes: a unifying framework and the new binary approach. BMC Bioinformatics 5:175

    Article  Google Scholar 

  • Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31:3784–3788

    Article  Google Scholar 

  • Gianchandani EP, Papin JA, Price ND, Joyce AR, Palsson BO (2006) Matrix formalism to describe functional states of transcriptional regulatory systems. PLoS ComputBiol 2:e101

    Article  ADS  Google Scholar 

  • Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33:D514–D517

    Article  Google Scholar 

  • Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-based metabolic flux analysis. Biophys J 92:1792–1805

    Article  Google Scholar 

  • Herrgard MJ, Fong SS, Palsson BO (2006a) Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol 2:e72

    Article  ADS  Google Scholar 

  • Herrgard MJ, Lee BS, Portnoy V, Palsson BO (2006b) Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res 16:627–635

    Article  Google Scholar 

  • Herring CD, Raghunathan A, Honisch C et al (2006) Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat Genet 38:1406–1412

    Article  Google Scholar 

  • Hohmann S, Krantz M, Nordlander B (2007) Yeast osmoregulation. Methods Enzymol 428:29–45

    Article  Google Scholar 

  • Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420:186–189

    Article  ADS  Google Scholar 

  • Imielinski M, Belta C, Rubin H, Halasz A (2006) Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media. Biophys J 90:2659–2672

    Article  Google Scholar 

  • Jamshidi N, Palsson BO (2006) Systems biology of SNPs. Mol Syst Biol 2:38

    Article  Google Scholar 

  • Jamshidi N, Palsson BO (2007) Investigating the metabolic capabilities of mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 1:26

    Article  Google Scholar 

  • Joshi A, Palsson BO (1989a) Metabolic dynamics in the human red cell. Part I-A comprehensive kinetic model. J Theor Biol 141:515–528

    Article  Google Scholar 

  • Joshi A, Palsson BO (1989b) Metabolic dynamics in the human red cell. Part II-Interactions with the environment. J Theor Biol 141:529–545

    Article  Google Scholar 

  • Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280

    Article  Google Scholar 

  • Kim PJ, Lee DY, Kim TY et al (2007) Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci U S A 104:13638–13642

    Article  ADS  Google Scholar 

  • Kirschner MW (2005) The meaning of systems biology. Cell 121:503–504

    Article  Google Scholar 

  • Kitano H (2002) Computational systems biology. Nature 420:206–210

    Article  ADS  Google Scholar 

  • Klamt S, Stelling J (2002) Combinatorial complexity of pathway analysis in metabolic networks. Mol Biol Rep 29:233–236

    Article  Google Scholar 

  • Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 1:2

    Article  Google Scholar 

  • Klipp E, Nordlander B, Kruger R, Gennemark P, Hohmann S (2005) Integrative model of the response of yeast to osmotic shock. Nat Biotechnol 23:975–982

    Article  Google Scholar 

  • Knorr AL, Jain R, Srivastava R (2007) Bayesian-based selection of metabolic objective functions. Bioinformatics 23:351–357

    Article  Google Scholar 

  • Kummel A, Panke S, Heinemann M (2006) Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol 2:2006.0034

    Article  Google Scholar 

  • Lee S, Palakornkule C, Domach MM, Grossmann IE (2000) Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Comput Chem Eng 24:711–716

    Article  Google Scholar 

  • Lee DY, Yun H, Park S, Lee SY (2003) MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19:2144–2146

    Article  Google Scholar 

  • Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY (2005) Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 71:7880–7887

    Article  Google Scholar 

  • Leino RL, Gerhart DZ, van Bueren AM, McCall AL, Drewes LR (1997) Ultrastructural localization of GLUT 1 and GLUT 3 glucose transporters in rat brain. J Neurosci Res 49:617–626

    Article  Google Scholar 

  • Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276

    Article  Google Scholar 

  • Marhl M, Schuster S, Brumen M, Heinrich R (1997) Modeling the interrelations between the calcium oscillations and ER membrane potential oscillations. Biophys Chem 63:221–239

    Article  Google Scholar 

  • Palsson BØ (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Papin JA, Palsson BO (2004) The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys J 87:37–46

    Article  Google Scholar 

  • Papin JA, Price ND, Edwards JS, Palsson BBO (2002a) The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy. J Theor Biol 215:67–82

    Article  Google Scholar 

  • Papin JA, Price ND, Palsson BO (2002b) Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Res 12:1889–1900

    Article  Google Scholar 

  • Papin JA, Reed JL, Palsson BO (2004a) Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem Sci 29:641–647

    Article  Google Scholar 

  • Papin JA, Stelling J, Price ND, Klamt S, Schuster S, Palsson BO (2004b) Comparison of network-based pathway analysis methods. Trends Biotechnol 22:400–405

    Article  Google Scholar 

  • Papin JA, Hunter T, Palsson BO, Subramaniam S (2005) Reconstruction of cellular signalling networks and analysis of their properties. Nat Rev Mol Cell Biol 6:99–111

    Article  Google Scholar 

  • Papoutsakis ET (1984) Equations and calculations for fermentations of butyric acid bacteria. Biotechnol Bioeng 26:174–187

    Article  Google Scholar 

  • Patil KR, Rocha I, Forster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 6:308

    Article  Google Scholar 

  • Pharkya P, Maranas CD (2006) An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab Eng 8:1–13

    Article  Google Scholar 

  • Pharkya P, Burgard AP, Maranas CD (2004) OptStrain: a computational framework for redesign of microbial production systems. Genome Res 14:2367–2376

    Article  Google Scholar 

  • Price ND, Papin JA, Palsson BO (2002) Determination of redundancy and systems properties of the metabolic network of Helicobacter pylori using genome-scale extreme pathway analysis. Genome Res 12:760–769

    Article  Google Scholar 

  • Price ND, Reed JL, Palsson BO (2004a) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2:886–897

    Article  Google Scholar 

  • Price ND, Schellenberger J, Palsson BO (2004b) Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 87:2172–2186

    Article  Google Scholar 

  • Price ND, Thiele I, Palsson BO (2006) Candidate states of Helicobacter pylori’s genome-scale metabolic network upon application of “loop law” thermodynamic constraints. Biophys J 90:3919–3928

    Article  Google Scholar 

  • Reed JL, Palsson BO (2004) Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res 14:1797–1805

    Article  Google Scholar 

  • Reed JL, Vo TD, Schilling CH, Palsson BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4:R54

    Article  Google Scholar 

  • Reed JL, Famili I, Thiele I, Palsson BO (2006a) Towards multidimensional genome annotation. Nat Rev Genet 7:130–141

    Article  Google Scholar 

  • Reed JL, Patel TR, Chen KH et al (2006b) Systems approach to refining genome annotation. Proc Natl Acad Sci U S A 103:17480–17484

    Article  ADS  Google Scholar 

  • Riley M, Abe T, Arnaud MB et al (2006) Escherichia coli K-12: a cooperatively developed annotation snapshot-2005. Nucleic Acids Res 34:1–9

    Article  Google Scholar 

  • Satish Kumar V, Dasika MS, Maranas CD (2007) Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics 8:212

    Article  Google Scholar 

  • Sauer U (2006) Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2:62

    Article  Google Scholar 

  • Savinell JM, Palsson BO (1992) Network analysis of intermediary metabolism using linear optimization. I Development of mathematical formalism. J Theor Biol 154:421–454

    Article  Google Scholar 

  • Schilling CH, Letscher D, Palsson BO (2000) Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J Theor Biol 203:229–248

    Article  Google Scholar 

  • Schomburg I, Chang A, Ebeling C et al (2004) BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res 32:D431–D433

    Article  Google Scholar 

  • Schuetz R, Kuepfer L, Sauer U (2007) Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol Syst Biol 3:119

    Article  Google Scholar 

  • Schuster S, Hilgetag C (1994) On elementary flux modes in biochemical reaction systems at steady state. J Biol Syst 2:165–182

    Article  Google Scholar 

  • Schuster S, Hilgetag C, Woods JH, Fell DA (2002) Reaction routes in biochemical reaction systems: algebraic properties, validated calculation procedure and example from nucleotide metabolism. J Math Biol 45:153–181

    Article  MathSciNet  MATH  Google Scholar 

  • Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99:15112–15117

    Article  ADS  Google Scholar 

  • Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci U S A 102:7695–7700

    Article  ADS  Google Scholar 

  • Shlomi T, Eisenberg Y, Sharan R, Ruppin E (2007) A genome-scale computational study of the interplay between transcriptional regulation and metabolism. Mol Syst Biol 3:101

    Article  Google Scholar 

  • Smallbone K, Simeonidis E, Broomhead DS, Kell DB (2007) Something from nothing: bridging the gap between constraint-based and kinetic modelling. FEBS J 274:5576–5585

    Article  Google Scholar 

  • Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED (2002) Metabolic network structure determines key aspects of functionality and regulation. Nature 420:190–193

    Article  ADS  Google Scholar 

  • Taegtmeyer H, McNulty P, Young ME (2002) Adaptation and maladaptation of the heart in diabetes: part I: general concepts. Circulation 105:1727–1733

    Article  Google Scholar 

  • Thiele I, Price ND, Vo TD, Palsson BO (2005) Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J Biol Chem 280:11683–11695

    Article  Google Scholar 

  • Thiele I, Jamshidi N, Fleming RMT, Palsson BØ (2008) Genome-scale reconstruction of E. coli’s transcriptional and translational machinery: a knowledge-base and its mathematical formulation. (under review)

    Google Scholar 

  • Trawick JD, Schilling CH (2006) Use of constraint-based modeling for the prediction and validation of antimicrobial targets. Biochem Pharmacol 71:1026–1035

    Article  Google Scholar 

  • Varma A, Palsson BO (1993a) Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors. J Theor Biol 165:477–502

    Article  Google Scholar 

  • Varma A, Palsson BO (1993b) Metabolic capabilities of Escherichia coli II. Optimal growth Patterns. J Theor Biol 165:503–522

    Article  Google Scholar 

  • Varma A, Palsson BO (1994) Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol 12:994–998

    Article  Google Scholar 

  • Varma A, Boesch BW, Palsson BO (1993) Stoichiometric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Appl Environ Microbiol 59:2465–2473

    Google Scholar 

  • Vastrik I, D’Eustachio P, Schmidt E et al (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol 8:R39

    Article  Google Scholar 

  • Vo TD, Greenberg HJ, Palsson BO (2004) Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. J BiolChem 279:39532–39540

    Google Scholar 

  • Wiback SJ, Famili I, Greenberg HJ, Palsson BO (2004) Monte Carlo sampling can be used to determine the size and shape of the steady-state flux space. J Theor Biol 228:437–447

    Article  MathSciNet  Google Scholar 

  • Yeung M, Thiele I, Palsson BO (2007) Estimation of the number of extreme pathways for metabolic networks. BMC Bioinformatics 8:363

    Article  Google Scholar 

Books and Reviews

  • Fiest AM, Palsson BØ (2008) The growing scope of applications of genome-scale metabolic reconstructions: the case of E. coli. Nat Biotechnol 26:659–667

    Article  Google Scholar 

  • Joyce AR, Palsson BØ (2007) Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach. In: Boshoff HI, Barry CE III (eds) Systems biological approaches in infectious diseases, vol 265. Birkhauser Verlag, Basel, pp 267–309

    Google Scholar 

  • Mo ML, Jamshidi N, Palsson BØ (2007) A genome-scale, constraint-based approach to systems biology of human metabolism. Mol BioSyst 3:9

    Article  Google Scholar 

  • Thiele I, Palsson BØ (2007) Bringing genomes to life: the use of genome-scale in silico models. In: Choi S (ed) Introduction to systems biology. Humana Press, Totowa

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by NSF IGERT training grant DGE0504645.

Author information

Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Nathan E. Lewis, Neema Jamshidi & Bernhard Ø. Palsson

  2. Bioinformatics Program, University of California, San Diego, La Jolla, CA, USA

    Ines Thiele

Authors
  1. Nathan E. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neema Jamshidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ines Thiele
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bernhard Ø. Palsson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nathan E. Lewis .

Editor information

Editors and Affiliations

  1. Ramtech Ltd., Larkspur, California, USA

    Robert A. Meyers

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this entry

Cite this entry

Lewis, N.E., Jamshidi, N., Thiele, I., Palsson, B.Ø. (2017). Metabolic Systems Biology. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_329-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27737-5_329-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27737-5

  • Online ISBN: 978-3-642-27737-5

  • eBook Packages: Springer Reference Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

Publish with us

Policies and ethics

Search

Navigation