Statistical Analysis of Molecular Signal Recording
Publication Date
July 18, 2013
Journal
PLOS Computational Biology
Authors
Joshua I. Glaser, Bradley M. Zamft, Adam H. Marblestone, Jeffrey R. Moffitt, et al
Volume
9
Issue
7
Pages
e1003145
DOI
https://dx.plos.org/10.1371/journal.pcbi.1003145
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1003145
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/23874187
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715445
Europe PMC
http://europepmc.org/abstract/MED/23874187
Web of Science
000322320200033
Scopus
84880840717
Mendeley
http://www.mendeley.com/research/statistical-analysis-molecular-signal-recording
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Mendeley | Further Information

{"title"=>"Statistical Analysis of Molecular Signal Recording", "type"=>"journal", "authors"=>[{"first_name"=>"Joshua I.", "last_name"=>"Glaser", "scopus_author_id"=>"55806532700"}, {"first_name"=>"Bradley M.", "last_name"=>"Zamft", "scopus_author_id"=>"55241651400"}, {"first_name"=>"Adam H.", "last_name"=>"Marblestone", "scopus_author_id"=>"26645104000"}, {"first_name"=>"Jeffrey R.", "last_name"=>"Moffitt", "scopus_author_id"=>"14016344500"}, {"first_name"=>"Keith", "last_name"=>"Tyo", "scopus_author_id"=>"12447065900"}, {"first_name"=>"Edward S.", "last_name"=>"Boyden", "scopus_author_id"=>"35291447700"}, {"first_name"=>"George", "last_name"=>"Church", "scopus_author_id"=>"7005451318"}, {"first_name"=>"Konrad P.", "last_name"=>"Kording", "scopus_author_id"=>"6603812799"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84880840717", "doi"=>"10.1371/journal.pcbi.1003145", "pui"=>"369438565", "pmid"=>"23874187", "scopus"=>"2-s2.0-84880840717", "issn"=>"1553734X", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"cea9ac46-02a9-39ee-b902-bba5a0665fc7", "abstract"=>"A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a \"molecular ticker tape\", in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.", "link"=>"http://www.mendeley.com/research/statistical-analysis-molecular-signal-recording", "reader_count"=>73, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>16, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>4, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>14, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>8, "Researcher"=>16, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>4, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>14, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>32, "Chemical Engineering"=>1, "Chemistry"=>2, "Computer Science"=>4, "Engineering"=>7, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Materials Science"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>2, "Neuroscience"=>12, "Physics and Astronomy"=>3, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Engineering"=>{"Engineering"=>7}, "Chemistry"=>{"Chemistry"=>2}, "Neuroscience"=>{"Neuroscience"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}}, "reader_count_by_country"=>{"Austria"=>1, "Netherlands"=>1, "United States"=>3, "Japan"=>1, "Luxembourg"=>1, "Taiwan"=>1, "United Kingdom"=>1, "France"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1122611"], "description"=>"<p>Approximate spatial and temporal resolutions for a subset of technologies theoretically capable of recording from entire mammalian brains.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics"], "article_id"=>748365, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.t002", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Technology_comparison_/748365", "title"=>"Technology comparison.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122610"], "description"=>"<p>The maximum recording duration at which decoding at 100 ms temporal resolution is possible with 95% decoding accuracy. An optimal DNAP with an elongation time of 1 ms and no pausing is used, along with 10000 DNA templates. The search for maximal achievable recording durations was performed at 25 second intervals.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "decoding", "100", "ms"], "article_id"=>748364, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Binary_decoding_at_100_ms_resolution_/748364", "title"=>"Binary decoding at 100 ms resolution.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122607"], "description"=>"<p><b>A</b>) Estimation error of continuous concentration decoding at 1 second resolution as a function of the time of recording. Parameters are ms, <i>P</i> = 0, <i>N</i> = 1000, , and . <b>B</b>) Estimation error at 6000 seconds (10 minutes) of recording for polymerases that do not start recording simultaneously. Polymerase start-time distributions are drawn from gamma distributions that have almost all values between 0 and twice the average delay time. <b>C</b>) Estimation error of continuous concentration decoding at 100 ms resolution as a function of the time of recording. Parameters are ms, <i>P</i> = 0, <i>N</i> = 10000, , and varying . In all panels, solid lines are median estimation errors, and dashed lines are 95% confidence intervals.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "decoding"], "article_id"=>748361, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g004", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Continuous_concentration_decoding_at_high_resolutions_/748361", "title"=>"Continuous concentration decoding at high resolutions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122605"], "description"=>"<p>Continuous decoding to estimate sequences of eight concentrations over 20 minutes of recording using varying numbers of templates. The 95% confidence interval of the estimated concentrations (light red) that result from the decoding algorithm presented here on an ion concentration input sequence representing the word “RECORDER” (dark red). Concentrations are mapped to letters via A = 0/25, B = 1/25,,…,Z = 25/25, so that the concentration sequence representing the word RECORDER is 17/25, 4/25…). The numbers of templates used were, from top to bottom, 1000, 100, 10, and 1. For all panels, kinetic parameters are those of DNAP ( ms, ms, ), , and ().</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics"], "article_id"=>748359, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decoding_continuous_concentration_signals_/748359", "title"=>"Decoding continuous concentration signals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122606"], "description"=>"<p>Performance of continuous decoding to estimate randomly determined sequences of eight concentrations over 20 minutes of recording, as a function of experimental parameters. Solid lines are median estimation errors, and dashed lines are 95% confidence intervals. <b>A</b>) Varying numbers of templates, with the CMLF fixed at and , and using DNAP kinetic parameters. <b>B</b>) Varying CMLFs, with the number of templates fixed at 1000, and using DNAP kinetic parameters. <b>C, D</b>) Varying DNAP pausing parameters, with a fixed elongation time of 20 ms, a fixed CMLF of and , and 1000 and 100 templates, respectively.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "numbers", "dnap"], "article_id"=>748360, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Varying_numbers_of_templates_CMLFs_and_DNAP_parameters_/748360", "title"=>"Varying numbers of templates, CMLFs, and DNAP parameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122604"], "description"=>"<p><b>A</b>) Example time-varying ion concentration signal. In a neuron, peaks in calcium concentration occur during neural firing. <b>B</b>) Example products from the simultaneous replication of multiple template strands, showing correct (C) and incorrect (I) nucleotide additions, with the time of incorporation shown on the horizontal axis. Misincorporations are more likely in the presence of higher ion concentration. <b>C</b>) The misincorporation counts from each template copy are summed to calculate the misincorporation probability at every nucleotide position in the template. In this example, approximately 100 nucleotides are replicated per second on average.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "decoding", "signals", "molecular", "ticker"], "article_id"=>748358, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g001", "stats"=>{"downloads"=>5, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Encoding_and_decoding_of_signals_with_a_molecular_ticker_tape_/748358", "title"=>"Encoding and decoding of signals with a molecular ticker tape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122613", "https://ndownloader.figshare.com/files/1122614", "https://ndownloader.figshare.com/files/1122615", "https://ndownloader.figshare.com/files/1122616", "https://ndownloader.figshare.com/files/1122617", "https://ndownloader.figshare.com/files/1122618", "https://ndownloader.figshare.com/files/1122619", "https://ndownloader.figshare.com/files/1122625", "https://ndownloader.figshare.com/files/1122627", "https://ndownloader.figshare.com/files/1122630", "https://ndownloader.figshare.com/files/1122635"], "description"=>"<div><p>A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.</p></div>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "molecular"], "article_id"=>748366, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003145.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s008", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s009", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s010", "https://dx.doi.org/10.1371/journal.pcbi.1003145.s011"], "stats"=>{"downloads"=>14, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_Analysis_of_Molecular_Signal_Recording_/748366", "title"=>"Statistical Analysis of Molecular Signal Recording", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122609"], "description"=>"<p><b>A</b>) DNAP can copy one nucleotide directly after another (top path) or pause between additions (bottom path). <b>B</b>) Dwell-time distributions between nucleotide additions. Distributions for the continuous route and for the pausing route are mixed based on their relative frequencies to create the full dwell time distribution, . For this panel, the parameters are set as ms, ms, and <i>P</i> = 0.3, to best illustrate the concept of distribution mixing. <b>C</b>) Time distributions, , resulting from repeated convolutions of the dwell time distribution, are shown for nucleotides 50, 200, 400, and 600. Iterated convolutions cause the distribution to widen for later times. For this panel and below, parameters are ms, ms, . <b>D</b>) An example time-varying concentration. <b>E</b>) The probability of misincorporation for a polymerase subjected to the input concentration trace from panel B. The misincorporation probability is related to the concentration through a CMLF: here, <b>F</b>) The misincorporation probability of the nucleotide, . The more the nucleotide's incorporation-time distribution overlaps with the concentration peaks in the time-varying input signal, the larger the misincorporation probability at the nucleotide.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "misincorporation"], "article_id"=>748363, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g006", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Minimal_forward_model_of_misincorporation_by_a_DNAP_/748363", "title"=>"Minimal forward model of misincorporation by a DNAP.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/1122608"], "description"=>"<p><b>A</b>) The percent error of the estimated parameters compared to the true parameters (those of DNAP) as a function of the number of template copies. <b>B</b>) The ion concentration estimation error based on polymerase parameters estimated from data using varying numbers of templates. Ion concentration estimation used <i>N</i> = 1000, and . In both panels, solid lines are median estimation errors, and dashed lines are 95% confidence intervals.</p>", "links"=>[], "tags"=>["genetics", "neuroscience", "Applied mathematics", "statistics", "biophysics", "dnap", "parameter", "sequencing"], "article_id"=>748362, "categories"=>["Physics", "Mathematics", "Biological Sciences"], "users"=>["Joshua I. Glaser", "Bradley M. Zamft", "Adam H. Marblestone", "Jeffrey R. Moffitt", "Keith Tyo", "Edward S. Boyden", "George Church", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003145.g005", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimating_DNAP_parameter_values_from_sequencing_data_/748362", "title"=>"Estimating DNAP parameter values from sequencing data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-18 04:09:01"}

PMC Usage Stats | Further Information

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Relative Metric

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