Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms
dc.contributor.author | Watson, Emma | |
dc.contributor.author | Yilmaz, L. Safak | |
dc.contributor.author | Walhout, Albertha J M | |
dc.date | 2022-08-11T08:11:00.000 | |
dc.date.accessioned | 2022-08-23T17:27:54Z | |
dc.date.available | 2022-08-23T17:27:54Z | |
dc.date.issued | 2015-11-23 | |
dc.date.submitted | 2016-03-23 | |
dc.identifier.citation | Annu Rev Genet. 2015 Nov 23;49:553-75. doi: 10.1146/annurev-genet-112414-055257. <a href="http://dx.doi.org/10.1146/annurev-genet-112414-055257">Link to article on publisher's site</a> | |
dc.identifier.issn | 0066-4197 (Linking) | |
dc.identifier.doi | 10.1146/annurev-genet-112414-055257 | |
dc.identifier.pmid | 26631516 | |
dc.identifier.uri | http://hdl.handle.net/20.500.14038/49965 | |
dc.description.abstract | Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with "omics" data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms. | |
dc.language.iso | en_US | |
dc.relation | <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=26631516&dopt=Abstract">Link to Article in PubMed</a> | |
dc.relation.url | http://dx.doi.org/10.1146/annurev-genet-112414-055257 | |
dc.subject | Caenorhabditis elegans | |
dc.subject | feedback loop | |
dc.subject | flux balance analysis | |
dc.subject | gene regulation | |
dc.subject | homeostasis | |
dc.subject | metabolic network | |
dc.subject | Biochemistry | |
dc.subject | Cellular and Molecular Physiology | |
dc.subject | Systems Biology | |
dc.title | Understanding Metabolic Regulation at a Systems Level: Metabolite Sensing, Mathematical Predictions, and Model Organisms | |
dc.type | Journal Article | |
dc.source.journaltitle | Annual review of genetics | |
dc.source.volume | 49 | |
dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/sysbio_pubs/86 | |
dc.identifier.contextkey | 8368716 | |
html.description.abstract | <p>Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with "omics" data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial diets that have revealed novel metabolic paradigms.</p> | |
dc.identifier.submissionpath | sysbio_pubs/86 | |
dc.contributor.department | Program in Molecular Medicine | |
dc.contributor.department | Program in Systems Biology | |
dc.source.pages | 553-75 |