BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=curation_notes.txthttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156.pdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=manifest.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156-matlab.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156.scihttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156-octave.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156_url.sedmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=curation_image.jpeghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156.pnghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=metadata.rdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000156?filename=BIOMD0000000156.odeprimaryOK200Harish DharuriManually curatedordinary differential equation modelL2V1https://www.ebi.ac.uk/biomodels/BIOMD000000015616773083falseBioModelsSBMLModelsZatorsky2006 p53 Model52006MODEL0076384106Naama Geva-Zatorsky, Nitzan Rosenfeld, Shalev Itzkovitz, Ron Milo, Alex Sigal, Erez Dekel, Talia Yarnitzky, Yuvalal Liron, Paz Polak, Galit Lahav, Uri AlonNaama Geva-Zatorsky16773083, Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.. null, 2. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.hdharuri@cds.caltech.eduCalifornia Institute of TechnologyBIOMD0000000156Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.p53 is activated in response to events compromising the genetic integrity of a cell. Recent data show that p53 activity does not increase steadily with genetic damage but rather fluctuates in an oscillatory fashion. Theoretical studies suggest that oscillations can arise from a combination of positive and negative feedbacks or from a long negative feedback loop alone. Both negative and positive feedbacks are present in the p53/Mdm2 network, but it is not known what roles they play in the oscillatory response to DNA damage. We developed a mathematical model of p53 oscillations based on positive and negative feedbacks in the p53/Mdm2 network. According to the model, the system reacts to DNA damage by moving from a stable steady state into a region of stable limit cycles. Oscillations in the model are born with large amplitude, which guarantees an all-or-none response to damage. As p53 oscillates, damage is repaired and the system moves back to a stable steady state with low p53 activity. The model reproduces experimental data in quantitative detail. We suggest new experiments for dissecting the contributions of negative and positive feedbacks to the generation of oscillations.Oscillations and variability in the p53 system.Steady states and oscillations in the p53/Mdm2 network.Geva-Zatorsky Naama N, Rosenfeld Nitzan N, Itzkovitz Shalev S, Milo Ron R, Sigal Alex A, Dekel Erez E, Yarnitzky Talia T, Liron Yuvalal Y, Polak Paz P, Lahav Galit G, Alon Uri UCiliberto Andrea A, Novak Béla B, Tyson John J JJbiochemical pathways, dmBest1, dp53, PNT-P1, DmelCG17077, Addresses, Longterm., Phases, EY3-1, protein, Pnt, betaTub3, DmelCG6264, Tp53, Divisions, Long Term, dmTAF[[II]]230, Readability, DMP53, png, bbl, ARB, 1, Impacts, cellular degradation, Dmp53, Environmental Impacts, D-ets-2, 3520, protein aggregate, Effect, BEST1_HUMAN, Pnt-P1, average, 1323/07, thymus nucleic acid, me75, M Phases, Man (Taxonomy), TFIID TAF250, pre-mortem, BCC7, cel, catabolism, M, Iprit, beta-Tub6D, dmp53, T, DmP53, 1422/04, D17Mit170, T1, Division Phase, Environmental Impact, B3t, DmelCG3401, CG14648, Dp53, mustard gas, biotransformation, Double-Stranded DNA, deoxyribonucleic acids, DNAn, CG17117, Long-Term Effects, p50/tubulin, single-organism behavior, CG10873, Noise, dTAF[[II]]230, Growl, CG6264, Process, Modern, frequency, Pollution, TAF200, Longterm Effect, D-Ets-2, Brother, ets94F, 0123/09, Double-Stranded, TAFII-250, Tl3, TAF250/230, Cell Divisions, Tl2, Acceptance Processes, 3t, (Deoxyribonucleotide)n+m, Menstruation, Acceptance Process, TAFII250, bis(2-chloroethyl)sulfane, p53-binding protein Mdm2, secretion, bfy, desoxyribose nucleic acid, TU15B, Tub60D, HTH, Hth, dBest1, beta-tub, Tumor suppressor p53, pntP2, Pointed-P1, dbest1, DmelCG33336, Ets94F, double minute 2 protein, CG3401, beta3Tub, beta3TUB, CG17603, surveillance, TAF[[II]], morbidity, human, DTB3, ptd, living, PntP2, hth1, hth2, Taf250, SR3-5, PntP1, E(E2F)3D, ds DNA, P53, p44, bhy, Sisters, PNTP2, Noise Pollution, DNA, Feedbacks, CG31325, PNTP1, betaTub, TAF230, beta[[3]]-Tub, DmelCG17117, 0998/12, d230, human being, DNS, p50, (Deoxyribonucleotide)n, Effects, ACTFS, Processes, ETS2, BEST1, p53, Ets2, Gene, dTAFII250, 143391_i_at, beta3 TU, Yperite, protein-containing complex, anon-WO0118547.380, LFS1, EfW1, cellular catabolism, l(3)05745, Deoxyribonucleic acids, Human, DMPOINT1A, pntegfr, 0608/07, Homo sapiens, Deoxyribonucleic Acid, dmTAF1, Taf230, 1700007J15Rik, Gene Products, Mustard gas, Low, anon-EST:Liang-2.13, 1'-thiobis(2-chloroethane), Man, TAF250, RING-type E3 ubiquitin transferase Mdm2, Brothers, study, Taf200, anatomical systems, dTAF[[II]]250, clone 2.13, Cell Division Phase, pointed-RC, occurrence, breakdown of chemical, Longterm, cell, VMD2, D.m.BETA-60D, Antigen NY-CO-13, anon-WO0118547.126, hdm2, prevalence, Double Stranded, Taf1p, Deoxyribonucleic acid, Cell Division, BMD, Long-Term, growl, Division, EK3-2, Phosphoprotein p53, bis(2-chloroethyl) sulphide, Lost, l(3)07825, dTAF250, Trp53, betaTub60C, beta3-tubulin, CG17077, DmelCG14648, Long-Term Effect, beta[[3]]-tubulin, (Deoxyribonucleotide)m, TRP53, MDM2, Behaviors, TAF, l(3)j1B7, incidence, sulfur mustard, RP50, vitelliform macular dystrophy 2 (Best disease, beta-Tub60D, cellular breakdown, oncoprotein Mdm2, Ets, TAF[[II]]250, HDMX, Dmbeta3, Dm-HTH, cou, 1-chloro-2-[(2-chloroethyl)thio]ethane, degradation, protein complex, DNAn+1, BETA 60D, Proteins, prac, beta3t, beta3-Tub, l(3)84Ab, BG:DS00004.13, Cell, nev, Xp53, Impact, dTAF230, Environmental, Lr, native protein, Period, p230, p53/tubulin, Protein, Long Term Effects, TAF[[II]]250/230, TFIID, beta[[3]] tubulin, ds-DNA, M Phase, pnt-P1, outbreaks, pnt-P2, bestrophin), l(3)s118306, breakdown of molecule, Taf[[II]]250, CG33336, Acceptance, Phase, Dbest, TAF[[II]]230, biodegradation, best, Sibling, D-p53, beta60C, Senfgas, AA415488, Dm-P53, l(3)86Ca, TAF[II]250, Mdm-2, beta3, Understanding, Sister, dtl, endemics, Noises, Longterm Effects, Protein Gene Products, Tub, Gene Proteins, Meis1, breakdown of substance, Ets58AB, DmelCG17603, betatub60D, Modern Man, Environments, Desoxyribonukleinsaeure, Bra, epidemics, variable, BEST, POINT, CG8705, TAF1extent, beta[[3]]-Tub, DmelCG17117, dp53, Sectors, Public Sectors, YB, p50, Effects, ACTFS, p53, AUTSX5, number, 143391_i_at, Copyrights, beta3 TU, NOVH, CCN3, LFS1, betaTub3, 2210402P09Rik, QM, Tp53, Long Term, l(3)05745, DMP53, Yb, 1700007J15Rik, bbl, Figs, Dmp53, anon-EST:Liang-2.13, Public Enterprise, Effect, Enterprises, CG2706, fs(1)M104, PIST, RING-type E3 ubiquitin transferase Mdm2, 1323/07, DmelCG7615, clone 2.13, BCC7, Longterm, D.m.BETA-60D, beta-Tub6D, Public Domains, dmp53, hdm2, AI844555, T, DmP53, 1422/04, Long-Term, IGFBP9, Public Enterprises, DEPP, Rubberplants, IBP-9, B3t, Kiaa4053, DmelCG3401, Trp53, Rubberplant, Dp53, L10, Fseg, betaTub60C, beta3-tubulin, Long-Term Effect, beta[[3]]-tubulin, TRP53, NOVh, CG17117, MDM2, Enterprise, Long-Term Effects, p50/tubulin, beta-Tub60D, Decidual protein induced by progesterone, CG10873, oncoprotein Mdm2, Dmbeta3, HDMX, Dm-HTH, Papers, completeness, DmelCG2706, BETA 60D, prac, beta3t, beta3-Tub, Longterm Effect, DXS648, results, Xp53, presence., 3t, Fasting-induced gene protein, count in organism, IGFBP-9, Public, Public Domain, p53-binding protein Mdm2, p53/tubulin, Long Term Effects, CAL, Domains, bfy, NOV, beta[[3]] tubulin, PlexA1, GOPC1, Tub60D, Domain, HTH, Hth, Data Base, CG33336, beta-tub, Plxn1, DmelCG33336, D-p53, beta60C, CG3401, double minute 2 protein, beta3Tub, beta3TUB, AA415488, nov, Dm-P53, l(3)86Ca, beta3, Mdm-2, mKIAA4053, dtl, fs(1)Y[b], Longterm Effects, dJ94G16.2, Tub, DTB3, Meis1, hth1, FIG, Fig, Sector, hth2, betatub60D, C130088N23Rik, P53, EG:95B7.8, CG7615, p44, bhy, 2600013D04Rik, PLXN1, DXS648E, CG31325, betaTubbeta-Tub60D, beta[[3]]-Tub, CG10873, DmelCG17117, dp53, Dmbeta3, Dm-HTH, p50, p53, BETA 60D, prac, beta3t, beta3-Tub, 143391_i_at, beta3 TU, LFS1, betaTub3, Tp53, l(3)05745, Xp53, 3t, DMP53, bbl, p53/tubulin, Dmp53, bfy, beta[[3]] tubulin, anon-EST:Liang-2.13, Tub60D, HTH, Hth, CG33336, beta-tub, 1323/07, clone 2.13, BCC7, D.m.BETA-60D, beta-Tub6D, dmp53, DmelCG33336, D-p53, beta60C, CG3401, beta3Tub, beta3TUB, T, DmP53, Dm-P53, l(3)86Ca, 1422/04, beta3, dtl, Tub, DTB3, B3t, Meis1, DmelCG3401, hth1, Trp53, hth2, Dp53, betatub60D, P53, betaTub60C, p44, bhy, beta3-tubulin, beta[[3]]-tubulin, TRP53, anatomical systems., CG17117, CG31325, betaTub, p50/tubulinfalseZatorsky2006_p53_Model5 The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 5. Results obtained using MathSBML. 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 CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92. 2024-08-212024-09-022008-01-14BIOMD0000000156hsa041151677308315725723MODEL0076384106BIOMD0000000156GO:0030330GO:0006412GO:0042176GO:0006351GO:00442579606BTO:0000093P04637Q00987