H IGH -F IDELITY DNA H YBRIDIZATION USING P ROGRAMMABLE M OLECULAR DNA D EVICES Nikhil Gopalkrishnan, Harish Chandran & John Reif.

Slides:



Advertisements
Similar presentations
RNA and Protein Synthesis
Advertisements

Theory of Computing Lecture 23 MAS 714 Hartmut Klauck.
Design of a biomolecular Device that executes process Algebra Urmi Majumder and John Reif Department of Computer Science Duke University DNA15, JUNE 10,
Models of Concurrency Manna, Pnueli.
Structure of DNA. Polymerase Chain Reaction - PCR PCR amplifies DNA –Makes lots and lots of copies of a few copies of DNA –Can copy different lengths.
1 Languages. 2 A language is a set of strings String: A sequence of letters Examples: “cat”, “dog”, “house”, … Defined over an alphabet: Languages.
1 Introduction to Computability Theory Lecture3: Regular Expressions Prof. Amos Israeli.
1 CSCI-2400 Models of Computation. 2 Computation CPU memory.
Finite Mathematics & Its Applications, 10/e by Goldstein/Schneider/SiegelCopyright © 2010 Pearson Education, Inc. 1 of 60 Chapter 8 Markov Processes.
Theoretical Computer Science COMP 335 Fall 2004
Topics Automata Theory Grammars and Languages Complexities
The polymerase chain reaction (PCR) rapidly
Exploration Session Week 8: Computational Biology Melissa Winstanley: (based on slides by Martin Tompa,
CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.
Nucleic Acid Structure Many thanks to Dave Bevan for providing some of the material for this lecture.
Autonomous DNA Nanomechanical Device Capable of Universal Computation and Universal Translational Motion Peng Yin*, Andrew J. Turberfield †, Sudheer Sahu*,
The Design of Autonomous DNA Nanomechanical Devices: Walking and Rolling John H. Reif Duke University.
The Fidelity of the Tag-Antitag System J. A. Rose, R. J. Deaton, M. Hagiya, And A. Suyama DNA7 poster Summarized by Shin, Soo-Yong.
1 Compact Error-Resilient Computational DNA Tiling Assemblies John H.Reif, Sudheer Sahu, and Peng Yin Presenter: Seok, Ho-SIK.
CS CM124/224 & HG CM124/224 DISCUSSION SECTION (JUN 6, 2013) TA: Farhad Hormozdiari.
1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing systems.
Mathematical Preliminaries. Sets Functions Relations Graphs Proof Techniques.
Implementing software in IEC Languages in IEC IEC uses the following languages Instruction List – Assembly level programming using.
DIGITAL COMMUNICATIONS Linear Block Codes
Computing with DNA Many thanks to Dave Bevan for providing some of the material for this lecture.
Whiplash PCR History: - Invented by Hagiya et all 1997] - Improved by Erik Winfree Made Isothermal by John Reif and Urmi Majumder 2008 Whiplash.
Lecture 8 Theory of AUTOMATA
Computer Communication & Networks Lecture 9 Datalink Layer: Error Detection Waleed Ejaz
Computational and experimental analysis of DNA shuffling : Supporting text N. Maheshri and D. V. Schaffer PNAS, vol. 100, no. 6, Summarized by.
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
John Reif and Urmi Majumder Department of Computer Science Duke University Isothermal Reactivating Whiplash PCR for Locally Programmable Molecular Computation.
CS 203: Introduction to Formal Languages and Automata
DNA Nanorobotics Reem Mokhtar. DNA Nanorobotics Book Chapter: Chandran, H., Gopalkrishnan, N., & Reif, J. (n.d.). DNA Nanorobotics. Aim: design and fabrication.
Motif Search and RNA Structure Prediction Lesson 9.
Towards Autonomous Molecular Computers Towards Autonomous Molecular Computers Masami Hagiya, Proceedings of GP, Nakjung Choi
Chapter 2 Scanning. Dr.Manal AbdulazizCS463 Ch22 The Scanning Process Lexical analysis or scanning has the task of reading the source program as a file.
Introduction Why do we study Theory of Computation ?
The Big Picture Chapter 3. A decision problem is simply a problem for which the answer is yes or no (True or False). A decision procedure answers a decision.
The Multistrand Simulator: Stochastic Simulation of the Kinetics of Multiple Interacting DNA Strands Joseph Schaeffer, Caltech (slides by John Reif)
Localized DNA Circuits Hieu Bui 1. Outline  Localized Kinetics & Modelling  Localized Hybridization Reactions  On Nanotracks  On DNA Origami 2.
Multiplication Timed Tests.
Alphabet, String, Language. 2 Alphabet and Strings An alphabet is a finite, non-empty set of symbols. –Denoted by  –{ 0, 1 } is a binary alphabet. –{
Languages.
Figure 1 Template-map sets used to generate a set of 108 8mers that contain 50% G/C content and are 4bm complements and reversals. 8mers are generated.
SNPs in forensic genetics: a review on SNP typing methodologies
Lecture 12 Analysis of Clocked Sequential Network
Advanced Computer Networks
Autonomous Programmable Nanorobotic Devices Using DNAzymes
PCR uses polymerases to copy DNA segments.
Jaya Krishna, M.Tech, Assistant Professor
On Template Method for DNA Sequence Design
A DNA computing readout operation based on structure-specific cleavage
Autonomous Programmable Nanorobotic Devices Using DNAzymes
PCR uses polymerases to copy DNA segments.
Yonatan Savir, Tsvi Tlusty  Molecular Cell 
PCR uses polymerases to copy DNA segments.
Fitness measures for DNA Computing
Fluorescence Imaging of Single-Copy DNA Sequences within the Human Genome Using PNA-Directed Padlock Probe Assembly  Anastasia I. Yaroslavsky, Irina V.
PCR uses polymerases to copy DNA segments.
Optical Measurement of Mechanical Forces Inside Short DNA Loops
Extracting Dwell Time Sequences from Processive Molecular Motor Data
Molecular Basis for Target RNA Recognition and Cleavage by Human RISC
Modeling of the RAG Reaction Mechanism
PCR uses polymerases to copy DNA segments.
PCR uses polymerases to copy DNA segments.
Instructor: Aaron Roth
Algorithms for Robust Self-Assembly
Yongli Zhang, Junyi Jiao, Aleksander A. Rebane  Biophysical Journal 
Languages Fall 2018.
PCR uses polymerases to copy DNA segments.
Presentation transcript:

H IGH -F IDELITY DNA H YBRIDIZATION USING P ROGRAMMABLE M OLECULAR DNA D EVICES Nikhil Gopalkrishnan, Harish Chandran & John Reif

F IDELITY OF H YBRIDIZATION Perfect hybridization Mismatched hybridization Difference in energy between red strand hybridization and green strand hybridization is small

F IDELITY OF H YBRIDIZATION Hybridization fidelity depends on length Errors in hybridization Noise: Strands with sequence similar to the target

D RAWBACKS OF L OW F IDELITY : S ELF -A SSEMBLY

D RAWBACKS OF L OW F IDELITY : DNA M ICROARRAYS From :

E XACT H IGH -F IDELITY H YBRIDIZATION Solution: ensemble of distinct sequences Target sequence s Problem statement: Completely hybridize all copies of s and don’t hybridize any other sequence Multiple strands may bind to s and cooperatively hybridize it

E XACT H IGH -F IDELITY H YBRIDIZATION Solution: ensemble of distinct sequences Target sequence s Problem statement: Completely hybridize all copies of s and don’t hybridize any other sequence Multiple strands may bind to s and cooperatively hybridize it Completion of hybridization should be detectable Example: by fluoroscence

A PPROXIMATE H IGH -F IDELITY H YBRIDIZATION Hybridization Error At most b bases may mismatch: b-hybridized Success probability probability of b-hybridization at least p Problem statement: b-hybridize each copy of s with probability at least p and no other sequence is b-hybridized with probability greater than 1-p p ≈ 95% and b ≈ 1/10 th of length of s

A SSUMPTIONS Short sequences have high fidelity of hybridization Subsequences sequestered in short hairpins are unreactive Strand displacement occurs whenever possible and proceeds to completion

A PPROXIMATE H IGH -F IDELITY H YBRIDIZATION

N OTATION Letters represent sequences Example: c i Sequences concatenate c i = a i b i Written from 5’ to 3’ Sequences differing only in the subscript are concatenations of subsequences differing only in the subscript c i = a i+1 b i implies c i+1 = a i+2 b i+1 Bar indicates reverse complement c i = b i a i is the reverse complement of c i = a i b i

H IGH -F IDELITY H YBRIDIZATION : 1 ST P ROTOCOL

1 ST P ROTOCOL : P OTENTIAL S OURCE OF ERROR

H IGH -F IDELITY H YBRIDIZATION : 2 ND P ROTOCOL

F AVORABLE P ROPERTIES OF THE P ROTOCOLS Autonomous Fluorophore based detection

S IMULATION OF F INITE A UTOMATA Finite automata: Mathematical constructs that define languages Limited computational power Memoryless

S IMULATION OF F INITE A UTOMATA Target strand encodes input to automata Checker sequences perform state transitions Green sequence performs δ(y,0) = z

S IMULATION OF F INITE A UTOMATA Incorrect checker sequence may attach Further attachment is blocked as second hairpin doesn’t open At each step, probability of correct attachment ≥ 0.5 Probability of successful completion ≥ 1/2 n where n=size of i/p Can process multiple inputs in parallel Number of checker sequences ≤ Twice number of edges in the transition diagram of the automata

P ROTOCOL K INETICS

F UTURE WORK Experimental verification for a simple case with just two checker sequences Computer simulation to predict reaction kinetics