connections between computer science and biology n.
Skip this Video
Loading SlideShow in 5 Seconds..
Connections between Computer Science and Biology PowerPoint Presentation
Download Presentation
Connections between Computer Science and Biology

Connections between Computer Science and Biology

380 Vues Download Presentation
Télécharger la présentation

Connections between Computer Science and Biology

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Connections between Computer Science and Biology

  2. connections • bioinformatics: computational approach to problems in molecular biology • biological processes inspire algorithms and data structures in computer science • biomolecules “compute” • biological organisms “compute”

  3. bioinformatics • sequencing the genome • predicting the structure of molecules • predicting genes, molecular function • constructing evolutionary trees • modeling cellular networks • ...

  4. constructing evolutionary trees “The affinities of all the beings of the same class have sometimes been represented by a great tree. I believe this simile largely speaks the truth. The green and budding twigs may represent existing species; and those produced during each former year may represent the long succession of extinct species.” - Darwin, Origin of the Species

  5. constructing evolutionary trees • traditional approach: use morphological features of organisms (number of legs, etc.) • current approach: use base sequences of universal molecules such as RNA

  6. RNA molecules • strings of ribo-nucleic acids, of which there are four types, denoted by A, C, G, U. 5’ - ACCAUGGAC - 3’ • some “universal” RNA molecules function in life’s most basic processes, and so mutate slowly

  7. ficticious example

  8. CAGG Aardvark CAGA Bison CGCG Chimp UGCA Dog UGCG Elephant two possible evolutionary trees UGCG CACG • which is a better fit with the data? why? UGCG CACG CAGG UGCG UGCG CAGG CAGG Aardvark CAGA Bison CGCG Chimp UGCA Dog UGCG Elephant

  9. parsimony score • to get a parsimony score for a tree, count the number of places where a nucleotide differs from a parent to a child

  10. parsimony problem • input: RNA sequences for some taxa, or species • output: the most parsimonious tree for the input taxa the more taxa, the more possible trees that are candidates for being the output

  11. application of parsimony(Luo et al., Nature, Jan 2001) • did mammals evolve independently on the north and south continents?

  12. how many trees are there? • unfortunately, the number of possible trees grows exponentially with the number of taxa (organisms) • example of an exponential function: 2n (2 multiplied n times) • if there are n taxa, there are even more than 2n possible evolutionary trees

  13. exponential running time

  14. complexity of the parsimony problem • all known algorithms for exactly solving the parsimony problem require an exponential number of steps - this is a so-called NP-hard problem • in practice, heuristic algorithms are typically used, which try to search in an intelligent way for a good tree, but offer no guarantee of finding the best tree

  15. connections: biologically inspired data structures • tree structures for organizing data are ubiquitous in computing (e.g. folders in a windows environment) • programming language environments support operations on trees (add-node, find-parent, etc.) for the programmer to use

  16. summary • strong connections between biology and cs • many computational problems, such as constructing parsimonious evolutionary trees, are “intractable” • algorithms for intractable problems are often heuristic

  17. vocabulary • bioinformatics • evolutionary tree construction; parsimony problem • exponential running time, intractable problem (technically sometimes called NP-hard problem) • heuristic algorithms