<HashMap><database>biostudies-other</database><scores/><additional><omics_type>Unknown</omics_type><volume>10</volume><submitter>Lucian Smith</submitter><journal>PLoS computational biology</journal><pagination>e1003728</pagination><species>Homo sapiens</species><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/MODEL1506230000</full_dataset_link><repository>biostudies-other</repository><additional_accession>25166345</additional_accession><pubmed_authors>administrator</pubmed_authors><pubmed_authors>Lucian Smith</pubmed_authors><pubmed_authors>Piero Dalle Pezze</pubmed_authors></additional><is_claimable>false</is_claimable><name>DallePezze2014 -  Cellular senescene-induced mitochondrial dysfunction</name><description>&lt;notes xmlns="http://www.sbml.org/sbml/level2/version4">      &lt;body xmlns="http://www.w3.org/1999/xhtml">        &lt;div class="dc:title">DallePazze2014 - Cellular senescene-inducedmitochondrial dysfunction&lt;/div>&lt;div class="dc:bibliographicCitation">  &lt;p>This model is described in the article:&lt;/p>  &lt;div class="bibo:title">    &lt;a href="http://identifiers.org/pubmed/25166345" title="Access to this publication">Dynamic modelling of    pathways to cellular senescence reveals strategies for targeted    interventions.&lt;/a>  &lt;/div>  &lt;div class="bibo:authorList">Dalle Pezze P, Nelson G, Otten EG,  Korolchuk VI, Kirkwood TB, von Zglinicki T, Shanley DP.&lt;/div>  &lt;div class="bibo:Journal">PLoS Comput. Biol. 2014 Aug; 10(8):  e1003728&lt;/div>  &lt;p>Abstract:&lt;/p>  &lt;div class="bibo:abstract">    &lt;p>Cellular senescence, a state of irreversible cell cycle    arrest, is thought to help protect an organism from cancer, yet    also contributes to ageing. The changes which occur in    senescence are controlled by networks of multiple signalling    and feedback pathways at the cellular level, and the interplay    between these is difficult to predict and understand. To    unravel the intrinsic challenges of understanding such a highly    networked system, we have taken a systems biology approach to    cellular senescence. We report a detailed analysis of    senescence signalling via DNA damage, insulin-TOR, FoxO3a    transcription factors, oxidative stress response, mitochondrial    regulation and mitophagy. We show in silico and in vitro that    inhibition of reactive oxygen species can prevent loss of    mitochondrial membrane potential, whilst inhibition of mTOR    shows a partial rescue of mitochondrial mass changes during    establishment of senescence. Dual inhibition of ROS and mTOR in    vitro confirmed computational model predictions that it was    possible to further reduce senescence-induced mitochondrial    dysfunction and DNA double-strand breaks. However, these    interventions were unable to abrogate the senescence-induced    mitochondrial dysfunction completely, and we identified    decreased mitochondrial fission as the potential driving force    for increased mitochondrial mass via prevention of mitophagy.    Dynamic sensitivity analysis of the model showed the network    stabilised at a new late state of cellular senescence. This was    characterised by poor network sensitivity, high signalling    noise, low cellular energy, high inflammation and permanent    cell cycle arrest suggesting an unsatisfactory outcome for    treatments aiming to delay or reverse cellular senescence at    late time points. Combinatorial targeted interventions are    therefore possible for intervening in the cellular pathway to    senescence, but in the cases identified here, are only capable    of delaying senescence onset.&lt;/p>  &lt;/div>&lt;/div>&lt;div class="dc:publisher">  &lt;p>This model is hosted on   &lt;a href="http://www.ebi.ac.uk/biomodels/">BioModels Database&lt;/a>  and identified by:   &lt;a href="http://identifiers.org/biomodels.db/BIOMD0000000582">BIOMD0000000582&lt;/a>.&lt;/p>  &lt;p>To cite BioModels Database, please use:   &lt;a href="http://identifiers.org/pubmed/20587024" title="Latest BioModels Database publication">BioModels Database:  An enhanced, curated and annotated resource for published  quantitative kinetic models&lt;/a>.&lt;/p>&lt;/div>&lt;div class="dc:license">  &lt;p>To the extent possible under law, all copyright and related or  neighbouring rights to this encoded model have been dedicated to  the public domain worldwide. Please refer to   &lt;a href="http://creativecommons.org/publicdomain/zero/1.0/" title="Access to: CC0 1.0 Universal (CC0 1.0), Public Domain Dedication">CC0  Public Domain Dedication&lt;/a> for more information.&lt;/p>&lt;/div>&lt;/body>    &lt;/notes></description><dates><release>2015-06-23T00:00:00Z</release><modification>2025-07-15T09:59:50.337Z</modification><creation>2025-03-29T18:02:07.268Z</creation></dates><accession>MODEL1506230000</accession><cross_references><biomodels___db>BIOMD0000000582</biomodels___db><sbo>SBO:0000291</sbo><sbo>SBO:0000405</sbo><pubmed>25166345</pubmed><chebi>CHEBI:26523</chebi><chebi>CHEBI:16991</chebi><chebi>CHEBI:5931</chebi><chebi>CHEBI:33709</chebi><mamo>MAMO_0000046</mamo><go>GO:0032869</go><go>GO:0005739</go><go>GO:0031931</go><go>GO:0000422</go><go>GO:2000772</go><taxonomy>9606</taxonomy><bto>BTO:0001590</bto><uniprot>Q9Y478</uniprot><uniprot>P16278</uniprot><uniprot>Q6I9V6</uniprot><uniprot>P31749</uniprot><uniprot>O43524</uniprot><uniprot>P45983</uniprot><uniprot>P38936</uniprot><uniprot>O14920</uniprot><uniprot>P54646</uniprot></cross_references></HashMap>