A Chinese Postman Problem Based on DNA Computing Z. Yin, F. Zhang, and J. Xu* J. Chem. Inf. Comput. Sci. 2002, 42, 222-224 Summarized by Shin, Soo-Yong.

Slides:



Advertisements
Similar presentations
CS 336 March 19, 2012 Tandy Warnow.
Advertisements

DNA Computation and Circuit Construction Isabel Vogt 2012.
Lecture 24 Coping with NPC and Unsolvable problems. When a problem is unsolvable, that's generally very bad news: it means there is no general algorithm.
DNA Computing COMP308 I believe things like DNA computing will eventually lead the way to a “molecular revolution,” which ultimately will have a very dramatic.
1 Appendix B: Solving TSP by Dynamic Programming Course: Algorithm Design and Analysis.
Graphs Chapter 12. Chapter Objectives  To become familiar with graph terminology and the different types of graphs  To study a Graph ADT and different.
CS 206 Introduction to Computer Science II 11 / 07 / 2008 Instructor: Michael Eckmann.
Midwestern State University Department of Computer Science Dr. Ranette Halverson CMPS 2433 CHAPTER 4 - PART 2 GRAPHS 1.
 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.
Article for analog vector algebra computation Allen P. Mils Jr, Bernard Yurke, Philip M Platzman.
ITEC200 – Week 12 Graphs. 2 Chapter Objectives To become familiar with graph terminology and the different types of graphs To study.
1 DNA Computing: Concept and Design Ruoya Wang April 21, 2008 MATH 8803 Final presentation.
Graphs. Overview What is a graph? Some terminology Types of graph Implementing graphs (briefly) Some graph algorithms Graphs 2/18.
Montek Singh COMP Nov 15,  Two different technologies ◦ TODAY: DNA as biochemical computer  DNA molecules encode data  enzymes, probes.
CS 206 Introduction to Computer Science II 11 / 10 / 2008 Instructor: Michael Eckmann.
Graphs Chapter 12. Chapter 12: Graphs2 Chapter Objectives To become familiar with graph terminology and the different types of graphs To study a Graph.
Spring 2010CS 2251 Graphs Chapter 10. Spring 2010CS 2252 Chapter Objectives To become familiar with graph terminology and the different types of graphs.
CS 206 Introduction to Computer Science II 11 / 05 / 2008 Instructor: Michael Eckmann.
Fall 2007CS 2251 Graphs Chapter 12. Fall 2007CS 2252 Chapter Objectives To become familiar with graph terminology and the different types of graphs To.
CS 206 Introduction to Computer Science II 03 / 30 / 2009 Instructor: Michael Eckmann.
Chapter 4 Graphs.
Genome Assembly Charles Yan Fragment Assembly Given a large number of fragments, such as ACC AC AT AC AT GG …, the goal is to figure out the original.
GRAPH Learning Outcomes Students should be able to:
DNA Sequencing (Lecture for CS498-CXZ Algorithms in Bioinformatics) Sept. 8, 2005 ChengXiang Zhai Department of Computer Science University of Illinois,
Maximum clique. 1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing.
Genomic walking (1) To start, you need: -the DNA sequence of a small region of the chromosome -An adaptor: a small piece of DNA, nucleotides long.
Graphs and DNA sequencing CS 466 Saurabh Sinha. Three problems in graph theory.
Beyond Silicon: Tackling the Unsolvable with DNA.
Graph Theory Topics to be covered:
O PTICAL M APPING AS A M ETHOD OF W HOLE G ENOME A NALYSIS M AY 4, 2009 C OURSE : 22M:151 P RESENTED BY : A USTIN J. R AMME.
Which of these can be drawn without taking your pencil off the paper and without going over the same line twice? If we can find a path that goes over all.
Structures 7 Decision Maths: Graph Theory, Networks and Algorithms.
Graph Theory And Bioinformatics Jason Wengert. Outline Introduction to Graphs Eulerian Paths & Hamiltonian Cycles Interval Graph & Shape of Genes Sequencing.
Combinatorial Optimization Problems in Computational Biology Ion Mandoiu CSE Department.
Computing with DNA Many thanks to Dave Bevan for providing some of the material for this lecture.
Cell and Microbial Engineering Laboratory Solving TSP on Lab-on-a-Chip 1 Jiyoun Lee.
Manipulating DNA. Scientists use their knowledge of the structure of DNA and its chemical properties to study and change DNA molecules Different techniques.
Computational and experimental analysis of DNA shuffling : Supporting text N. Maheshri and D. V. Schaffer PNAS, vol. 100, no. 6, Summarized by.
The Inference via DNA Computing Piort Wasiewicz et al. Proceedings of the 1999 Congress on Evolutionary Computation, vol. 2, pp Cho, Dong-Yeon.
1 Biological Computing – DNA solution Presented by Wooyoung Kim 4/8/09 CSc 8530 Parallel Algorithms, Spring 2009 Dr. Sushil K. Prasad.
Graphs Chapter 12. Chapter 12: Graphs2 Chapter Objectives To become familiar with graph terminology and the different types of graphs To study a Graph.
(C) 2004, SNU Biointelligence Lab, DNA Extraction by Cross Pairing PCR Giuditta Franco, Cinzia Giagulli, Carlo Laudanna, Vincenzo.
Source: CSE 214 – Computer Science II Graphs.
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
Review: Graph Theory in Bioinformatics Yunkai Liu Assistant Professor Computer Science Department University of South Dakota.
Molecular Evolutionary Computing (MEC) for Maximum Clique Problems March 9, 2004 Biointelligence Laboratory School of Computer Science and Engineering.
DNA Sequencing (Lecture for CS498-CXZ Algorithms in Bioinformatics)
CSCI2950-C Genomes, Networks, and Cancer
Redraw these graphs so that none of the line intersect except at the vertices B C D E F G H.
DNA Solution of the Maximal Clique Problem
Wet (DNA) computing 2001년 9월 20일 이지연
CSE 421: Introduction to Algorithms
Genome Assembly.
Fuzzy logic with biomolecules
Summarized by Ji-Yeon Lee & Soo-Yong Shin
Some Thoughts on Theorem Proving
Problem Solving 4.
Fitness measures for DNA Computing
CSCI2100 Data Structures Tutorial
All pairs shortest path problem
Chapter 15 Graph Theory © 2008 Pearson Addison-Wesley.
Graphs G = (V, E) V are the vertices; E are the edges.
Molecular Genetic Programming
DNA Solution of the Maximal Clique Problem
Parallel BFS for Maximum Clique Problems
Hamilton Paths and Circuits
Shortest Route Problems
Designs for Autonomous Unidirectional Walking DNA Devices
Three Dimensional DNA Structures in Computing
Presentation transcript:

A Chinese Postman Problem Based on DNA Computing Z. Yin, F. Zhang, and J. Xu* J. Chem. Inf. Comput. Sci. 2002, 42, Summarized by Shin, Soo-Yong

© 2002, SNU BioIntelligence Lab, Chinese Postman Problem (CPP) Similar with TSP CPP has to visit all edges in the graph, but can visit same edges more than twice.  cf) TSP for all vertexes.

© 2002, SNU BioIntelligence Lab, Algorithm for CPP The same as Adleman’s algorithm 1. Generate a random closed walk 2. Keep only those closed walks that begins with fixed vertices and end with fixed vertices 3. Keep only those closed walks that enter all of the edges of the given graph at least once. 4. Find the shortest path 5. Determine

© 2002, SNU BioIntelligence Lab, Implementation Step 1  Edge weights are represented by the length.  Vertex : 20mer  Edge : 10mer for starting, 10mer for ending, 10* w ij for weight

© 2002, SNU BioIntelligence Lab, Implementation Step 2  PCR with rear 10 bp of ‘0’ and former 10 bp of ‘0’ Step 3  Affinity-purification for all edges Step 4  Gel electrophoresis Step 5  Sequencing

© 2002, SNU BioIntelligence Lab, 참고사항 실험 없음. Journal of Chemical Information and Computer Science  Core SCI! impact factor is (1998)

DNA Solution of a Graph Coloring Problem Y. Liu, J. Xu*, L. Pan, and S. Wang J. Chem. Inf. Comput. Sci. 2002, 42, Summarized by Shin, Soo-Yong

© 2002, SNU BioIntelligence Lab, Graph Coloring Problem Find minimum number of color to paint a different color for the adjacent vertexes.

© 2002, SNU BioIntelligence Lab, Algorithm Convert to the problem an ensemble of all rearrangements of the required colors.  Ex) (2,1,5,3,6,5) (1,2,3,1,2,3) (2,3,4,2,3,4)  Wrong answer : (1,1,3,1,2,3) Delete illegal answer Sort the answer (find minimum length)

© 2002, SNU BioIntelligence Lab, Representation dsDNA strands Color section ( C i ) and name section ( N i )  N 1 & N 7 for PCR, N i : 20 bp  C i : j if C i = j ( j = 1,2,…,6)  380 bp for , 230 bp for

© 2002, SNU BioIntelligence Lab, Representation Initial fragment

© 2002, SNU BioIntelligence Lab, Making a pool Parallel overlap assembly (POA)

© 2002, SNU BioIntelligence Lab, Delete illegal answer Illegal answer template  (x, x, *, *, *, *), (x, *, x, *, *, *), (x, *, *, *, x, *), (x, *, *, *, *, x), (*, x, x, *, *, *), (*, x, *, x, *, *), (*, x, *, *, *, x), (*, *, x, x, *, *), (*, *, x, *, x, *), (*, *, *, x, x, *), (*, *, *, x, *, x), (*, *, *, *, x, x) Divide into two tubes  T1 cut out by restriction enzyme (1, *, *, *, *, *)  T2 cut out (*, 1, *, *, *, *) Combine T1 and T2 Repeat all strings for given templates.

© 2002, SNU BioIntelligence Lab, Experimental Results Product of POA and PCR Final product

© 2002, SNU BioIntelligence Lab, To reduce errors Digesting the ssDNA with S 1 nuclease before restriction digestions Two cycles of digestion-PCR Avoid accidental homologies longer than 4 bp