A Self-Organized Model for Cell-Differentiation Based on Variations of Molecular Decay Rates
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{"title"=>"A self-organized model for cell-differentiation based on variations of molecular decay rates", "type"=>"journal", "authors"=>[{"first_name"=>"Rudolf", "last_name"=>"Hanel", "scopus_author_id"=>"7005402171"}, {"first_name"=>"Manfred", "last_name"=>"Pöchacker", "scopus_author_id"=>"36697169700"}, {"first_name"=>"Manuel", "last_name"=>"Schölling", "scopus_author_id"=>"55235069400"}, {"first_name"=>"Stefan", "last_name"=>"Thurner", "scopus_author_id"=>"35588832000"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22693554", "pui"=>"364923428", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0036679", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84861714630", "sgr"=>"84861714630", "arxiv"=>"1202.0694"}, "id"=>"df405b73-ff37-3ebb-a7a2-a220bd2bd67d", "abstract"=>"Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of these dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.", "link"=>"http://www.mendeley.com/research/selforganized-model-celldifferentiation-based-variations-molecular-decay-rates", "reader_count"=>15, "reader_count_by_academic_status"=>{"Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>6, "Student > Ph. D. Student"=>4, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Environmental Science"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>2, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Mathematics"=>{"Mathematics"=>1}, "Environmental Science"=>{"Environmental Science"=>2}}, "reader_count_by_country"=>{"United States"=>2, "United Kingdom"=>1, "Spain"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/629513"], "description"=>"<p>Tree of all existing active sets for system shown in article <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036679#pone-0036679-g003\" target=\"_blank\">Fig. (3)</a>. In set all , yellow background stand for complex leading eigenvalues of the active interaction matrix. Black indicates that the agent associated with that index is not active. The gray lines indicate to all possible switching events where the number of active agents changes . Blue arrows mark the observed sequence of the dynamics for the examples Eq. (8) with .</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "developmental biology", "Biochemistry", "mathematics"], "article_id"=>299998, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.g005", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tree_of_active_sets_/299998", "title"=>"Tree of active sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:24:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/629572"], "description"=>"<p>Some characteristics of the four node system shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036679#pone-0036679-g001\" target=\"_blank\">Fig. 1</a> are listed, including the index of the time domain, the index of the sub-system , the number of active nodes , the time the system spends in the 'th sub-system, the real-part of the leading eigenvalue of , and whether sub-system is stable or not.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "developmental biology", "Biochemistry", "mathematics"], "article_id"=>300059, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Domain_properties_/300059", "title"=>"Domain properties.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 04:24:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/629456"], "description"=>"<p>Example of a SL system with and and identical initial conditions for all values of expressing different portions of the regulatory networks. (a) The Lyapunov exponent, (b) the number of active sets in a period (if then the sequence is not periodic but a steady state!), and (c) the fraction of expressed nodes are plotted as functions of the uniform decay rates . For is stable. In the range the has become unstable but the plateau () can not form since the dynamic finds active sets with stable and accessible . The inset in (b) shows that in the plateau region a small window, , exists where again an active set contains an accessible attracting the dynamics. In the range the plateau forms and dynamics gets periodic. For the system gets unstable.</p>", "links"=>[], "tags"=>["rates"], "article_id"=>299948, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Degradation_rates_and_active_networks_/299948", "title"=>"Degradation rates and active networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:23:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/629404"], "description"=>"<p>The Lyapunov exponent of the four node system, Eq. (8), is shown in (a) as a function of the decay rate , which exhibits a “plateau” with in the range . In (b) the length of the periodic sequence of domains is plotted in green triangles and the number of different active sets as red squares. In (c) the sequences of active sets are shown for decay rates , and . The limit circles for decay rates (short sequence) and (long sequence) are visualized in (d) in a Poincare map using three out of four phase-space dimensions. With decreasing the radius of the limit circle becomes wider and additional sets (marked with colors) become active. In (e) the spectra of eigenvalues are shown for all the appearing active sets with .</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "developmental biology", "Biochemistry", "mathematics"], "article_id"=>299892, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.g003", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_edge_of_chaos_/299892", "title"=>"The edge of chaos.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:23:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/629287"], "description"=>"<p>Sequentially linear system with decay rate and the fixed point for all agents simulated with time-increment . Periodic time-series organized into a sequence of four domains with three different active sets. For each time-domain the associated spectrum of eigenvalues for the active sets is shown in (b). In (c) a 3 d Poincare map of the limit cycle is plotted together with the projection of in the center. The domains are marked with bold numbers and switching events with dots. (d) The eigenvalue spectra of the different subsystems are plotted in the imaginary plane. The shift of the spectrum along the real axis depending on the decay rate is indicated.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "developmental biology", "Biochemistry", "mathematics"], "article_id"=>299773, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Periodic_dynamics_and_active_sets_/299773", "title"=>"Periodic dynamics and active sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:22:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/629351"], "description"=>"<p>Time series of periodic binding of four proteins to the pS2 promoter after addition of estradiol - experimental data has been extracted from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036679#pone.0036679-Pigolotti1\" target=\"_blank\">[54]</a>, where a negative feedback-loop was proposed to explain the dynamics. Experimental data due to <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036679#pone.0036679-Mtivier1\" target=\"_blank\">[25]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036679#pone.0036679-Mtivier2\" target=\"_blank\">[26]</a> (dotted lines) is compared with a simulation of a SL system, based on the network shown in the inset, with uniform decay rates for all agents and fixed point concentrations . Correlation coefficients for simulated and measured time-series are for time larger and agents in order of the legend. The model simulation uses zero concentrations for all agents as initial condition and a time increment . For matching the simulation with experiment time in the model is shifted by .</p>", "links"=>[], "tags"=>["sequentially", "linear"], "article_id"=>299845, "categories"=>["Biological Sciences", "Mathematics", "Biochemistry", "Genetics", "Developmental Biology"], "users"=>["Rudolf Hanel", "Manfred Pöchacker", "Manuel Schölling", "Stefan Thurner"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0036679.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Adequacy_of_sequentially_linear_systems_/299845", "title"=>"Adequacy of sequentially linear systems.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:23:06"}

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  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"4", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"2", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"9", "full-text"=>"4", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[322, 550, 671, 773, 864, 955, 1048, 1135, 1223, 1308, 1387, 1465, 1534, 1602, 1673, 1744, 1813, 1885, 1955, 2026, 2093, 2160, 2228, 2290, 2349]}, {"subject_area"=>"/Biology and life sciences/Biochemistry", "average_usage"=>[316, 541, 663, 766, 856, 950, 1041, 1128, 1218, 1302, 1382, 1456, 1526, 1593, 1657, 1729, 1796, 1862, 1930, 1999, 2065, 2132, 2202, 2261, 2319]}, {"subject_area"=>"/Biology and life sciences/Developmental biology", "average_usage"=>[313, 562, 690, 796, 893, 986, 1079, 1173, 1257, 1349, 1429, 1506, 1586, 1661, 1730, 1803, 1876, 1945, 2017, 2091, 2163, 2225, 2294, 2362, 2430]}, {"subject_area"=>"/Biology and life sciences/Physiology", "average_usage"=>[307, 536, 653, 753, 839, 926, 1017, 1103, 1189, 1268, 1348, 1425, 1492, 1557, 1620, 1686, 1759, 1825, 1891, 1950, 2014, 2079, 2141, 2200, 2255]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[352, 587, 696, 809, 901, 989, 1072, 1156, 1257, 1334, 1422, 1486, 1555, 1647, 1714, 1780, 1844, 1919, 1997, 2051, 2138, 2198, 2267, 2324, 2391]}, {"subject_area"=>"/Computer and information sciences/Systems science", "average_usage"=>[331, 552, 654, 739, 811, 881, 963, 1040, 1088, 1156, 1222, 1308, 1369, 1458, 1542, 1601, 1693, 1735, 1785, 1871, 1939, 2008, 2067, 2103, 2169]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[312, 547, 668, 769, 861, 953, 1046, 1135, 1224, 1307, 1388, 1464, 1536, 1606, 1676, 1744, 1812, 1882, 1954, 2020, 2089, 2155, 2218, 2282, 2344]}, {"subject_area"=>"/Medicine and health sciences/Anatomy and physiology", "average_usage"=>[308, 539, 659, 759, 852, 941, 1028, 1113, 1197, 1275, 1353, 1423]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[325, 522, 627, 718, 804, 884, 969, 1052, 1131, 1207, 1277, 1346, 1415, 1478, 1542, 1605, 1663, 1723, 1776, 1839, 1895, 1955, 2008, 2066, 2123]}]}
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