A diffusive model of intra-cellular signaling ; Un modèle diffusif de la signalisation intracellulaire ; A diffusive model of intra-cellular signaling: Random Walk with Restart as a model of signaling and its parametrization using Linear Programming ; Un modèle diffusif de la signalisation intracellulaire: La marche aléatoire avec retour à l’origine comme modèle de la signalisation et sa paramétrisation à l’aide de la programmation linéaire
Uloženo v:
| Název: | A diffusive model of intra-cellular signaling ; Un modèle diffusif de la signalisation intracellulaire ; A diffusive model of intra-cellular signaling: Random Walk with Restart as a model of signaling and its parametrization using Linear Programming ; Un modèle diffusif de la signalisation intracellulaire: La marche aléatoire avec retour à l’origine comme modèle de la signalisation et sa paramétrisation à l’aide de la programmation linéaire |
|---|---|
| Autoři: | Perrin, Jérémie |
| Přispěvatelé: | Theories and Approaches of Genomic Complexity (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), AIX MARSEILLE UNIVERSITE, Christine BRUN, Olivier DESTAING |
| Zdroj: | https://amu.hal.science/tel-04954334 ; Bioinformatics [q-bio.QM]. AIX MARSEILLE UNIVERSITE, 2024. English. ⟨NNT : ⟩. |
| Informace o vydavateli: | CCSD |
| Rok vydání: | 2024 |
| Sbírka: | Aix-Marseille Université: HAL |
| Témata: | Intracellular Signaling, Phosphorylation, Random Walk with Restart, Diffusive processes, Path extraction, Optimization, Linear Programming, PPI networks, Graphs, Signalisation intracellulaire, Marche aléatoire avec retour à l’origine, Processus diffusifs, Extraction de chemin, Optimisation, Programmation linéaire, Réseaux PPI, kinase SRC, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] |
| Popis: | We are interested in deciphering the complexity of intra-cellular signaling mediatedby phosphorylation. We propose to model the intra-cellular propagation of the pertur-bation originating from a signaling element, a cell-surface receptor or an optogenetickinase, as a Random Walk with Restart (RWR) [Lovász 1993,Cowen et al. 2017] in aProtein-Protein Interaction (PPI) network built from both experimental evidence ofdirect interactions between proteins [Chapple et al. 2015] as well as kinase-substraterelationships [Lo Surdo et al. 2023; Gjerga et al. 2021]. Random Walks with Restartarise naturally when describing diffusive processes, and the propagation of a signalinside a cell can arguably be described as such [Friedman et al. 2007].We have shown that RWR can be formulated as a Linear Program, meaning that wecan learn the edge weights of a RWR (interaction probability profiles of the proteinsof the network) to best match observations on the network’s nodes (phosphorylationintensities). Edge weights are often set as a prior, most often with an equiprobabilityassumption, we have shown that we can efficiently choose them to best fit data. Basedon our theoretical result, we have developed the Optimally Biased Random Walkwith Restart (OBRWR) method, an approach which uses both a phospho-proteomicsdataset and, as previously stated, a heterogeneous PPI network to compute the optimalinteraction probabilities to propagate the signal from the perturbed protein to thedifferentially phosphorylated proteins.We first show in an optogenetic context [Kerjouan et al. 2021] that our methodretrieves expected, and condition specific, intermediary signaling elements whichwere not differentially phosphorylated. Its predictions agree with the underlyingknown biology, and we experimentally validate some of these predictions. We thencompare our method with a pre-dating tool, PHONEMeS [Gjerga et al. 2021], using theHPN-DREAM dataset [Hill et al. 2016]. We show that the two methods qualitativelybehave comparably, although we are aware of ... |
| Druh dokumentu: | doctoral or postdoctoral thesis |
| Jazyk: | English |
| Dostupnost: | https://amu.hal.science/tel-04954334 https://amu.hal.science/tel-04954334v1/document https://amu.hal.science/tel-04954334v1/file/THESE_TAGC_2024-12-17_Jeremie_PERRIN.pdf |
| Rights: | http://creativecommons.org/licenses/by-nc-nd/ ; info:eu-repo/semantics/OpenAccess |
| Přístupové číslo: | edsbas.1346E09E |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | We are interested in deciphering the complexity of intra-cellular signaling mediatedby phosphorylation. We propose to model the intra-cellular propagation of the pertur-bation originating from a signaling element, a cell-surface receptor or an optogenetickinase, as a Random Walk with Restart (RWR) [Lovász 1993,Cowen et al. 2017] in aProtein-Protein Interaction (PPI) network built from both experimental evidence ofdirect interactions between proteins [Chapple et al. 2015] as well as kinase-substraterelationships [Lo Surdo et al. 2023; Gjerga et al. 2021]. Random Walks with Restartarise naturally when describing diffusive processes, and the propagation of a signalinside a cell can arguably be described as such [Friedman et al. 2007].We have shown that RWR can be formulated as a Linear Program, meaning that wecan learn the edge weights of a RWR (interaction probability profiles of the proteinsof the network) to best match observations on the network’s nodes (phosphorylationintensities). Edge weights are often set as a prior, most often with an equiprobabilityassumption, we have shown that we can efficiently choose them to best fit data. Basedon our theoretical result, we have developed the Optimally Biased Random Walkwith Restart (OBRWR) method, an approach which uses both a phospho-proteomicsdataset and, as previously stated, a heterogeneous PPI network to compute the optimalinteraction probabilities to propagate the signal from the perturbed protein to thedifferentially phosphorylated proteins.We first show in an optogenetic context [Kerjouan et al. 2021] that our methodretrieves expected, and condition specific, intermediary signaling elements whichwere not differentially phosphorylated. Its predictions agree with the underlyingknown biology, and we experimentally validate some of these predictions. We thencompare our method with a pre-dating tool, PHONEMeS [Gjerga et al. 2021], using theHPN-DREAM dataset [Hill et al. 2016]. We show that the two methods qualitativelybehave comparably, although we are aware of ... |
|---|