1. 1 Introduction to Biocomputing: Structure (DNA & RNA)

2. 2 genome: biological information in an organism DNA: deoxyribonucleic acid, carries genome of cellular lifeforms RNA: ribonucleic acid, carries genome of some viruses, carries messages within the cell bases: the four bases found in DNA are adenine (A), cytosine (C), guanine (G), and Thymine (T); in a “double helix” of DNA, bonds are always A--T or C--G; thus a single strand of DNA carries the information about the strand it would bond to So DNA can be thought of as a “base 4” storage medium, a “linear tape” containing information in a 4-character alphabet

3. 3 DNA—the “double helix”

4. 4 DNA— ”direction ” http://www.swbic.org/products/clipart/images/dna2.jpg

5. 5 RNA: Thymine (T) replaced by Uracil (U) and deoxyribose replaced by ribose http://www.swbic.org/products/clipart/images/rna.jpg

6. 6 comparison

7. 7 Translation: DNA  rRNA  mRNA  tRNA  protein http://www.swbic.org/products/clipart/images/translation.jpg http://www.swbic.org/products/clipart/images/dogmag.jpg

8. 8 DNA provides the basic “code”. RNA copies this code from the DNA and used this information to form a string of amino acids—i.e., a protein. Proteins “are the machines that make all living things function”

9. 9 Central Dogma: Before the discovery of retroviruses and prions, this was believed to be the basic mechanism of inheritance in all living things

10. 10 Relative sizes: 10 -18 : electron 10 -15 : proton, neutron 10 -14 : atomic nucleus 10 -10 : water molecule (angstrom) 10 -9 : (nanometer, nm), one DNA “twist” 10 -8 : wavelength of UV light 10 -7 : thickness of cell membrane 10 -6 : diameter of typical bacterium (micron, mm) 10 -5 : diameter of typical cell 10 -4 : width of human hair 10 -3 : diameter of sand grain (millimeter, mm) 10 -2 : diameter of nickel (centimeter, cm) 10 0 : 1 meter 35 mm--one side of Pentium 4 chip 2-10 mm, typical MEMS feature size 0.18 or 0.13 mm, Pentium 4 wire width “nanotechnology”: molecules, atoms

11. 11 Why is biomolecular computing attractive? Size: --typical bacterium has diameter on ht order of 10 -6 m. (1 micron); --one twist of DNA double helix is on the order of 10 -9 m. (nanometer scale) Power requirements should be low Massive parallel computation is theoretically possible I/O can be two-dimensional Instabilities of quantum systems are much less of a problem here

12. 12 What are the disadvantages? Speed--typical reaction can take hours or days Error rates--may be unacceptably high; may be introduced by mechanical steps in proocessing data I/O--we do not yet have efficient mechanisms for doing input/output with these systems “Herd” property--we can affect a mixture of data items; we cannot in general pick out one specific item; biomolecular computing is inherently parallel Exponential growth in size of computation--it may be that the speed barrier in traditional computing is replaced by a size barrier in biomolecular computing--we may need too much biological material to solve a reasonable sized problem for the “computation” to be feasible

13. 13 What interesting projects can build on our knowledge of traditional computer engineering? “structural” designs—DNA computing “chemical” designs—using proteins as signals

14. 14 Computing using DNA structures: polynucleotide: a single DNA strand oligonucleotide: short, single-stranded DNA molecule, usually less than 50 nucleotides in length In DNA computing, specific oligonucleotides are constructed to represent data items. nucleotide: phosphate group + sugar + one of the 4 bases (A,C,G,T): the phosphate end is labeled 5’, the base end, 3’ Example: in Adelman’s seminal 1994 paper, oligonucleotides of length 20 were built to represent vertices and edges in a given graph: Vertex V1 Edge V1-V2 Vertex V2 A TGT CAAG CTAT

15. 15 Possible operations on DNA: building up custom oligonucleotide sequences to represent parts of your data splitting--can be done by heating, e.g. recombining--can be done by cooling cutting strand at a particular site “sticking” two fragments together (at their ends) sorting by some string property (including length) DNA computing (“structural”, “digital”)

16. 16 So-----DNA computing: uses structure of the DNA relies on mechanical operations answers “self-assemble” basic steps: encode the problem make a “solution” of problem fragments cool the solution so fragments will form longer strands filter out the answers you want

17. 17 Example: solving graph problems CAAG ATTG CAAT Encode vertices and edges—use DNA properties to encode graph “structure” Mix up a solution of your fragments Cool down, get resulting “paths”, “spanning trees”, etc.

18. 18 “Standard cell architectures, FPGAs” The BioBrick Project Basic idea (after Prof. Tom Knght, MIT): “gates” are functional units Ends of gates are standard “join” DNA sequences—reserved for this purpose So we can build computational chains easily Web page: http://parts.mit.edu/registry/index.php/Main_Page http://parts.mit.edu/registry/index.php/Main_Page

19. 19 Other applications of DNA computing: general computing using “sticker” language study of relationship between traditional architectures and DNA configurations: ---FSMs-linear DNA ---stack machines--branching DNA ---“Turing machines” (general purpose computers)-- sheet DNA

20. 20 Other applications of DNA computing (continued): 3-D self-assembled structures: “walking and rolling DNA”: structures for nanotube assembly: (recently reported in Science)