2. Biochemistry, computing in biology

3. 1Introduction 2Theoretical background Biochemistry/molecular biology 3Theoretical background computer science 4History of the field 5Splicing systems 6P systems 7Hairpins 8Detection techniques 9Micro technology introduction 10Microchips and fluidics 11Self assembly 12Regulatory networks 13Molecular motors 14DNA nanowires 15Protein computers 16DNA computing - summery 17Presentation of essay and discussion Course outline

4. Recombination

5. Recombination and crossover

7. If no exchange of genes (i.e. phenotypic marker) occurs, recombination event can not be detected Recombination and crossover

10. Introduction to ciliates

11. literature  Genome Gymnastics: Unique Modes of DNA Evolution and Processing in Ciliates. David M. Prescott, Nature Reviews Genetics  Computational power of gene rearrangement. Lila Kari and Laura Landweber, DIMACS series in discreet mathematics and theoretical computer science

12.  Very ancient ( ~ 2. 10 9 years ago)  Very rich group ( ~ 10000 genetically different organisms)  Very important from the evolutionary point of view The ciliate

13.  DNA molecules in micronucleus are very long (hundreds of kilo bps)  DNA molecules in macronucleus are gene- size, short (average ~ 2000 bps) The ciliate

15. Baldauf et al. 2000. Science 290:972. The ciliate tree

16. Urostyla grandis Bar: 50  m Holosticha kessleri Bar: 100  m Uroleptus sp. Bar: 100  m Scrambled Genes Found S. lemnae O. trifallax O. nova Eschaneustyla sp. Bar: 25  m

17. The ciliate

19. Dapi staining of the ciliate

20. Nuclei  Micronucleus the small nucleus containing a single copy of the genome that is used for sexual reproduction  Macronucleus the large nucleus that carries up to several hundred copies of the genome and controls metabolism and asexual reproduction

21. Prescott, 2000 Macronucleus Micronucleus Cutting, splicing, elimination, reordering, and amplification of DNA Lifecycle of a ciliate

22. The ciliate, meiosis

23. Cell Pairing Meiosis and Nuclear Exchange Nuclear Fusion and Duplication of the Zygotic Nucleus Macronuclear Development and Nuclear Degeneration MIC MAC Modified from Larry Klobutcher & Carolyn Jahn Ann. Review Microbiology, 2002 Polytenization Chromatid breakage De novo telomere formation The ciliate, reproduction

24. Computing in ciliates

25. Astounding feats of ‘DNA computing’ are routine in this ‘simple’ single -celled organism— a protozoan. In initial micronucleus, DNA is‘junky’ and scrambled, but…. ….it reassembles itself in proper sequence by means of computer-like acrobatics (unscrambling, throwing out genetic ‘junk’)—in macronucleus The ciliate

26. IES: internal eliminated segments MDS: macronuclear destined sequences MAC MIC TelomerePointers The complexity of spirotrich biology

27. Splicing

28. Fractioned genes

29.  Intervening non-coding DNA regions (IES: internal eliminated segments) interrupt protein-coding sequences (MDS macronuclear destined sequences)  IESs are removed during macronuclear development  MDSs are unscrambled Prescott, 2000 The complexity of gene scrambling

30. Actin I DNA polymerase  Landweber et al., 2000 Hogan et al., 2001  -TBP Prescott et al., 1998 Oxytricha nova Scramble genes  -TBP, actin I, DNA pol 

31. Prescott et al, 1998 Degree of scrambling in  -TBP

32. Hogan et al, 2001 Unscrambling of actin I

33. Landweber et al, 2000 Degree of scrambling in DNA pol 

34. DNA folding and recombination DNA pol 

35. DNA folding and recombination

36. DNA pol  : Hairpin loop Prescott, 2000 DNA folding and recombination DNA pol 

37. Prescott et al, 1998 Recombination  -TBP

38. (i)Isolate the micronuclear and macronuclear forms of the  -TBP gene (ii)Compare the micronuclear and macronuclear gene structures (MDS and IESs) to determine whether the gene is scrambled (iii)Compare homologous MDSs and scrambling patterns in various stichotrich species (earlier diverging species vs later diverging species) (iv)Trace a parsimonious evolutionary scrambling pathway Tracing evolutionary scrambling

39. Uroleptus sp. Oxytrichidae and Paraurostyla weissei Comparisons of scrambling complexity

40. Oxytricha trifallax Oxytricha nova Stylonychia mytilus Uroleptus sp. Paraurostyla weissei 100 The evolution of recombination

41. P. weissei Uroleptus sp. Holosticha sp. O. trifallax O. nova S. mytilus Evolutionary scrambling pathway

42. Formal theory

43. Ciliate computing  The process of gene unscrambling in hypotrichous ciliates represents one of nature’s ingenious solutions to the computational problem of gene assembly.  With some essential genes fragmented in as many as 50 pieces, these organisms rely on a set of sequence and structural clues to detangle their coding regions.  For example, pointer sequences present at the junctions between coding and non-coding sequences permit reassembly of the functional copy. As the process of gene unscrambling appears to follow a precise algorithm or set of algorithms, the question remains: what is the actual problem being solved?

44.  Genomic Copies of some Protein-coding genes are obscured by intervening non- protein-coding DNA sequence elements (internally eliminated sequences, IES)  Protein-coding sequences (macronuclear destined sequences, MDS) are present in a permuted order, and must be rearranged. The problem in the cell

45.  By clever structural alignment…, the cell decides which sequences are IES and MDS, as well as which are guides.  After this decision, the process is simply sorting, O(n).  Decision process unknown, but amounts to finding the correct path. Most Costly. Assumption

46.  there is some as yet undiscovered “oracle”mechanism within the cell,  or the cell simulates non-determinism  the former solution lacks biological credibility and the latter implies exponential time and space explosion.  What we want is a deterministic algorithm for applying the inter- and intra- molecular recombination operations to descramble an arbitrary gene. Ciliate computing

47. The first proposed step in gene unscrambling— alignment or combinatorial pattern matching— may involve searches through several possible matches, via either intra-molecular or intermolecular strand associations. This part could be similar to Adleman’s (1994) DNA solution of a directed Hamiltonian path problem. Ciliate computing

48. The second step—homologous recombination at aligned repeats—involves the choice of whether to retain the coding or the non-coding segment between each pair of recombination junctions. This decision process could even be equivalent to solving an n-bit instance of a satisfiability problem, where n is the number of scrambled segments. Ciliate computing

49. We use our knowledge of the first step to develop a model for the guided homologous recombinations and prove that such a model has the computational power of a Turing machine, the accepted formal model of computation. This indicates that, in principle, these unicellular organisms may have the capacity to perform at least any computation carried out by an electronic computer. Ciliate computing

50.  Assume the cell simply reconstructs the genes by matching up pointers.  Just one problem... pointer sequences are not unique. In fact, may have multiplicities greater than 13.  The proposed solution to this was that the cell would simply try every possible combination of pointers until it found the right two. Ciliate computing, the naïve model

51.  Relies on short repeat sequences to act as guides in homologous recombination events  Splints analogous to edges in Adleman  One example represents solution of 50 city HP (50 pieces reordered) How the cell computes

52.  Guided recombination system Formal model

53.  Context necessary for a re- combination between repeats x (p, x, q) ~ (p’, x, q’) Formal model

54.  Formal Language Model Where u=u’p, w=qw’=w’’p’, v=q’v’  Intramolecular recombination. The guide is x. Delete x wx from original.  Intermolecuar recombination. Strand Exchange.  This is a universal Turing machine (proven by Tom Head) Formal model, splicing operation

56. Gene unscrambling algorithm

57. Ciliate computing

58.  Micronucleus: cell mating  Macronucleus: RNA transcripts (expression)  Micro: I 0 M 1 I 1 M 2 I 2 M 3 … I k M k I k+1  M = P 1 N P 2  Macro: permutation of (possibly rotated) M 1,…, M k and I 0,…, I k+1 are removed Gene assembly in ciliates

59. Molecular operators

72.  The pointer sequences must be in spatial proximity during unscrambling  Topology must be faithfully reproduced somehow Pointers

73.  Recombination event attaches Minor Locus to end of Major Locus Relocation of a locus