Editing Metabolism (section)

==Investigation and manipulation==
{{further|Protein methods|Proteomics|Metabolomics|Metabolic network modelling}}
[[File:A thaliana metabolic network.png|thumb|upright=1.35|right|[[Metabolic network]] of the ''[[Arabidopsis thaliana]]'' [[citric acid cycle]]. [[Enzyme]]s and [[metabolite]]s are shown as red squares and the interactions between them as black lines.]]

Classically, metabolism is studied by a [[reductionism|reductionist]] approach that focuses on a single metabolic pathway. Particularly valuable is the use of [[radioactive tracer]]s at the whole-organism, tissue and cellular levels, which define the paths from precursors to final products by identifying radioactively labelled intermediates and products.<ref>{{cite journal | vauthors = Rennie MJ | title = An introduction to the use of tracers in nutrition and metabolism | journal = The Proceedings of the Nutrition Society | volume = 58 | issue = 4 | pages = 935–44 | date = November 1999 | pmid = 10817161 | doi = 10.1017/S002966519900124X | doi-access = free }}</ref> The enzymes that catalyze these chemical reactions can then be [[protein purification|purified]] and their [[enzyme kinetics|kinetics]] and responses to [[enzyme inhibitor|inhibitors]] investigated. A parallel approach is to identify the small molecules in a cell or tissue; the complete set of these molecules is called the [[metabolome]]. Overall, these studies give a good view of the structure and function of simple metabolic pathways, but are inadequate when applied to more complex systems such as the metabolism of a complete cell.<ref>{{cite journal | vauthors = Phair RD | title = Development of kinetic models in the nonlinear world of molecular cell biology | journal = Metabolism | volume = 46 | issue = 12 | pages = 1489–95 | date = December 1997 | pmid = 9439549 | doi = 10.1016/S0026-0495(97)90154-2 | doi-access = free }}</ref>

An idea of the complexity of the [[metabolic network]]s in cells that contain thousands of different enzymes is given by the figure showing the interactions between just 43 proteins and 40 metabolites to the right: the sequences of genomes provide lists containing anything up to 26.500 genes.<ref>{{cite journal | vauthors = Sterck L, Rombauts S, Vandepoele K, Rouzé P, Van de Peer Y | title = How many genes are there in plants (... and why are they there)? | journal = Current Opinion in Plant Biology | volume = 10 | issue = 2 | pages = 199–203 | date = April 2007 | pmid = 17289424 | doi = 10.1016/j.pbi.2007.01.004 }}</ref> However, it is now possible to use this genomic data to reconstruct complete networks of biochemical reactions and produce more [[Holism|holistic]] mathematical models that may explain and predict their behavior.<ref>{{cite journal | vauthors = Borodina I, Nielsen J | title = From genomes to in silico cells via metabolic networks | journal = Current Opinion in Biotechnology | volume = 16 | issue = 3 | pages = 350–5 | date = June 2005 | pmid = 15961036 | doi = 10.1016/j.copbio.2005.04.008 }}</ref> These models are especially powerful when used to integrate the pathway and metabolite data obtained through classical methods with data on [[gene expression]] from [[proteomics|proteomic]] and [[DNA microarray]] studies.<ref>{{cite journal | vauthors = Gianchandani EP, Brautigan DL, Papin JA | title = Systems analyses characterize integrated functions of biochemical networks | journal = Trends in Biochemical Sciences | volume = 31 | issue = 5 | pages = 284–91 | date = May 2006 | pmid = 16616498 | doi = 10.1016/j.tibs.2006.03.007 }}</ref> Using these techniques, a model of human metabolism has now been produced, which will guide future drug discovery and biochemical research.<ref>{{cite journal | vauthors = Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BØ | display-authors = 6 | title = Global reconstruction of the human metabolic network based on genomic and bibliomic data | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 104 | issue = 6 | pages = 1777–82 | date = February 2007 | pmid = 17267599 | pmc = 1794290 | doi = 10.1073/pnas.0610772104 | bibcode = 2007PNAS..104.1777D | doi-access = free }}</ref> These models are now used in [[Network theory|network analysis]], to classify human diseases into groups that share common proteins or metabolites.<ref>{{cite journal | vauthors = Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL | title = The human disease network | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 104 | issue = 21 | pages = 8685–90 | date = May 2007 | pmid = 17502601 | pmc = 1885563 | doi = 10.1073/pnas.0701361104 | bibcode = 2007PNAS..104.8685G | doi-access = free }}</ref><ref>{{cite journal | vauthors = Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, Barabási AL | title = The implications of human metabolic network topology for disease comorbidity | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 105 | issue = 29 | pages = 9880–5 | date = July 2008 | pmid = 18599447 | pmc = 2481357 | doi = 10.1073/pnas.0802208105 | bibcode = 2008PNAS..105.9880L | doi-access = free }}</ref>

Bacterial metabolic networks are a striking example of [[Bow tie (biology)|bow-tie]]<ref name="Csete-2004">{{cite journal | vauthors = Csete M, Doyle J | title = Bow ties, metabolism and disease | journal = Trends in Biotechnology | volume = 22 | issue = 9 | pages = 446–50 | date = September 2004 | pmid = 15331224 | doi = 10.1016/j.tibtech.2004.07.007 }}</ref><ref name="Ma-2003">{{cite journal | vauthors = Ma HW, Zeng AP | title = The connectivity structure, giant strong component and centrality of metabolic networks | journal = Bioinformatics | volume = 19 | issue = 11 | pages = 1423–30 | date = July 2003 | pmid = 12874056 | doi = 10.1093/bioinformatics/btg177 | citeseerx = 10.1.1.605.8964 }}</ref><ref name="Zhao-2006">{{cite journal | vauthors = Zhao J, Yu H, Luo JH, Cao ZW, Li YX | title = Hierarchical modularity of nested bow-ties in metabolic networks | journal = BMC Bioinformatics | volume = 7 | pages = 386 | date = August 2006 | pmid = 16916470 | pmc = 1560398 | doi = 10.1186/1471-2105-7-386 | arxiv = q-bio/0605003 | bibcode = 2006q.bio.....5003Z | doi-access = free }}</ref> organization, an architecture able to input a wide range of nutrients and produce a large variety of products and complex macromolecules using a relatively few intermediate common currencies.<ref>{{Cite web |title=Macromolecules: Nutrients, Metabolism, and Digestive Processes {{!}} Virtual High School - KeepNotes |url=https://keepnotes.com/virtual-high-school/sbi3u/1294-macromolecules-nutrients-metabolism-and-digestive-processes |access-date=2023-12-29 |website=keepnotes.com |archive-date=29 December 2023 |archive-url=https://web.archive.org/web/20231229164242/https://keepnotes.com/virtual-high-school/sbi3u/1294-macromolecules-nutrients-metabolism-and-digestive-processes |url-status=dead }}</ref>

A major technological application of this information is [[metabolic engineering]]. Here, organisms such as [[yeast]], plants or [[bacteria]] are genetically modified to make them more useful in [[biotechnology]] and aid the production of [[drug]]s such as [[antibiotic]]s or industrial chemicals such as [[1,3-Propanediol|1,3-propanediol]] and [[shikimic acid]].<ref>{{cite journal | vauthors = Thykaer J, Nielsen J | title = Metabolic engineering of beta-lactam production | journal = Metabolic Engineering | volume = 5 | issue = 1 | pages = 56–69 | date = January 2003 | pmid = 12749845 | doi = 10.1016/S1096-7176(03)00003-X }}</ref><ref>{{cite journal | vauthors = González-Pajuelo M, Meynial-Salles I, Mendes F, Andrade JC, Vasconcelos I, Soucaille P | title = Metabolic engineering of Clostridium acetobutylicum for the industrial production of 1,3-propanediol from glycerol | journal = Metabolic Engineering | volume = 7 | issue = 5–6 | pages = 329–36 | year = 2005 | pmid = 16095939 | doi = 10.1016/j.ymben.2005.06.001 | hdl-access = free | hdl = 10400.14/3388 }}</ref><ref>{{cite journal | vauthors = Krämer M, Bongaerts J, Bovenberg R, Kremer S, Müller U, Orf S, Wubbolts M, Raeven L | display-authors = 6 | title = Metabolic engineering for microbial production of shikimic acid | journal = Metabolic Engineering | volume = 5 | issue = 4 | pages = 277–83 | date = October 2003 | pmid = 14642355 | doi = 10.1016/j.ymben.2003.09.001 }}</ref> These genetic modifications usually aim to reduce the amount of energy used to produce the product, increase yields and reduce the production of wastes.<ref>{{cite journal | vauthors = Koffas M, Roberge C, Lee K, Stephanopoulos G | title = Metabolic engineering | journal = [[Annual Review of Biomedical Engineering]] | volume = 1 | pages = 535–57 | year = 1999 | pmid = 11701499 | doi = 10.1146/annurev.bioeng.1.1.535 | s2cid = 11814282 }}</ref>