Statistical Analysis of Molecular Signal Recording

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 misincorp...

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Main Authors: Glaser, Joshua I. (Author), Zamft, Bradley M. (Author), Marblestone, Adam Henry (Author), Moffitt, Jeffrey R. (Author), Tyo, Keith (Author), Church, George M. (Author), Kording, Konrad P. (Author), Boyden, Edward (Author)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Media Laboratory (Contributor), McGovern Institute for Brain Research at MIT (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor), Boyden, Edward Stuart (Contributor)
Format: Article
Language:English
Published: Public Library of Science, 2013-09-27T17:02:50Z.
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Online Access:Get fulltext
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100 1 0 |a Glaser, Joshua I.  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Biological Engineering  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Media Laboratory  |e contributor 
100 1 0 |a McGovern Institute for Brain Research at MIT  |e contributor 
100 1 0 |a Program in Media Arts and Sciences   |q  (Massachusetts Institute of Technology)   |e contributor 
100 1 0 |a Boyden, Edward Stuart  |e contributor 
700 1 0 |a Zamft, Bradley M.  |e author 
700 1 0 |a Marblestone, Adam Henry  |e author 
700 1 0 |a Moffitt, Jeffrey R.  |e author 
700 1 0 |a Tyo, Keith  |e author 
700 1 0 |a Church, George M.  |e author 
700 1 0 |a Kording, Konrad P.  |e author 
700 1 0 |a Boyden, Edward  |e author 
245 0 0 |a Statistical Analysis of Molecular Signal Recording 
260 |b Public Library of Science,   |c 2013-09-27T17:02:50Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/81223 
520 |a 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. 
520 |a United States. Defense Advanced Research Projects Agency. Living Foundries Program 
520 |a Google (Firm) 
520 |a New York Stem Cell Foundation. Robertson Neuroscience Investigator Award 
520 |a National Institutes of Health (U.S.) (EUREKA Award 1R01NS075421) 
520 |a National Institutes of Health (U.S.) (Transformative R01 1R01GM104948) 
520 |a National Institutes of Health (U.S.) (Single Cell Grant 1 R01 EY023173) 
520 |a National Institutes of Health (U.S.) (Grant 1R01DA029639) 
520 |a National Institutes of Health (U.S.) (Grant 1R01NS067199) 
520 |a National Science Foundation (U.S.) (CAREER Award CBET 1053233) 
520 |a National Science Foundation (U.S.) (Grant EFRI0835878) 
520 |a National Science Foundation (U.S.) (Grant DMS1042134) 
520 |a Paul G. Allen Family Foundation (Distinguished Investigator in Neuroscience Award) 
546 |a en_US 
655 7 |a Article 
773 |t PLoS Computational Biology