Introduction to Biocomputing: Structure (DNA & RNA)

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

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 DNAcan be thought of as a “base 4” storage medium, a “linear tape” containing information in a 4-character alphabet

3. DNA—the “double helix”

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

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

6. comparison

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

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

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) 100: 1 meter “nanotechnology”: molecules, atoms 0.18 or 0.13 mm, Pentium 4 wire width 2-10 mm, typical MEMS feature size 35 mm--one side of Pentium 4 chip

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

14. A T G T T C A T C A A G • 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 Vertex V2 Edge V1-V2

15. DNA computing (“structural”, “digital”) • 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)

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. C A C A T A A T A G G T Example: solving graph problems • 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. “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

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