1. CS4030: Bio-Computing Revision Lecture

2. DNA Replication Prior to cell division, all the genetic instructions must be copied so that each new cell will have a complete set DNA polymerase is the enzyme that copies DNA –Reads the old strand in the 3´ to 5´ direction

3. Over time, genes accumulate mutations Environmental factors Radiation Oxidation Mistakes in replication or repair Deletions, Duplications Insertions Inversions Point mutations

4. Codon deletion: ACG ATA GCG TAT GTA TAG CCG… –Effect depends on the protein, position, etc. –Almost always deleterious –Sometimes lethal Frame shift mutation: ACG ATA GCG TAT GTA TAG CCG… ACG ATA GCG ATG TAT AGC CG?… –Almost always lethal Deletions

5. Why align sequences? The draft human genome is available Automated gene finding is possible Gene: AGTACGTATCGTATAGCGTAA –What does it do? One approach: Is there a similar gene in another species? –Align sequences with known genes –Find the gene with the best match

6. Are there other sequences like this one? 1) Huge public databases - GenBank, Swissprot, etc. 2) Sequence comparison is the most powerful and reliable method to determine evolutionary relationships between genes 3) Similarity searching is based on alignment 4) BLAST and FASTA provide rapid similarity searching a. rapid = approximate (heuristic) b. false + and - scores

7. Similarity Homology 1) 25% similarity 100 AAs is strong evidence for homology 2) Homology is an evolutionary statement which means descent from a common ancestor –common 3D structure –usually common function –homology is all or nothing, you cannot say "50% homologous"

8. Comparing two sequences Point mutations, easy: ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT Indels are difficult, must align sequences: ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT

9. Scoring a sequence alignment Match score:+1 Mismatch score:+0 Gap penalty:–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Gaps: 7 × (– 1) Score = +11

10. Origination and length penalties We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT-------ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap

11. Scoring a sequence alignment (2) Match/mismatch score:+1/+0 Origination/length penalty:–2/–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Origination: 2 × (–2) Length: 7 × (–1) Score = +7

12. Scoring Similarity 1) Can only score aligned sequences 2) DNA is usually scored as identical or not 3) modified scoring for gaps - single vs. multiple base gaps (gap extension) 4) AAs have varying degrees of similarity –a. # of mutations to convert one to another –b. chemical similarity –c. observed mutation frequencies 5) PAM matrix calculated from observed mutations in protein families

13. DNA Scoring Matrix ATCG A1000 T0100 C0010 G0001 ATCG A5-4 T 5 C 5 G 5 ATCG A1-5 T-51-5 C 1-5 G-5 1 IdentityBLASTTransition/Transversion

14. The dynamic programming concept Suppose we are aligning: ACTCG ACAGTAG Last position choices: G+1ACTC GACAGTA G-1ACTC -ACAGTAG --1ACTCG GACAGTA

15. We can use a table Suppose we are aligning: A with A … A 0 A

16. Needleman-Wunsch: Step 1 Each sequence along one axis Mismatch penalty multiples in first row/column 0 in [1,1]

17. Needleman-Wunsch: Step 2 Vertical/Horiz. move: Score + (simple) gap penalty Diagonal move: Score + match/mismatch score Take the MAX of the three possibilities

18. Needleman-Wunsch: Step 2 (contd) Fill out the rest of the table likewise…

19. Needleman-Wunsch: Step 2 (contd) Fill out the rest of the table likewise… The optimal alignment score is calculated in the lower-right corner

20. But what is the optimal alignment To reconstruct the optimal alignment, we must determine of where the MAX at each step came from…

21. A path corresponds to an alignment = GAP in top sequence = GAP in left sequence = ALIGN both positions One path from the previous table: Corresponding alignment (start at the end): AC--TCG ACAGTAG Score = +2

22. Semi-global alignment Suppose we are aligning: GCG GGCG Which do you prefer? G-CG-GCG GGCGGGCG Semi-global alignment allows gaps at the ends for free.

23. Initialize first row and column to all 0s Allow free horizontal/vertical moves in last row and column Semi-global alignment

24. Local alignment Global alignments – score the entire alignment Semi-global alignments – allow unscored gaps at the beginning or end of either sequence Local alignment – find the best matching subsequence CGATG AAATGGA This is achieved by allowing a 4 th alternative at each position in the table: zero, if alternative neg. Smith-Waterman Algorithm (1981).

25. Local alignment Mismatch = –1 this time CGATG AAATGGA

26. CBA - Artificial Immune Systems Classical Immunity The purpose of the immune system is defence Innate and acquired immunity –Innate is the first line of defense. Germ line encoded (passed from parents) and is quite static (but not totally static) –Adaptive (acquired). Somatic (cellular) and is acquired by the host over the life time. Very dynamic. –These two interact and affect each other

27. CBA - Artificial Immune Systems Multiple layers of the immune system

28. CBA - Artificial Immune Systems Innate Immunity May take days to remove an infection, if it fails, then the adaptive response may take over Macrophages and neurophils are actors –Bind to common (known) things. This knowledge has been evolved and passed from generation to generation.

29. CBA - Artificial Immune Systems Processes within the Immune System (very basically) Negative Selection –Censoring of T-cells in the thymus gland of T-cells that recognise self Defining normal system behavior Clonal Selection –Proliferation and differentiation of cells when they have recognised something Generalise and learn Self vs Non-Self

30. CBA - Artificial Immune Systems Clonal Selection

31. CBA - Artificial Immune Systems Clonal Selection

32. CBA - Artificial Immune Systems Immune Responses

33. CBA - Artificial Immune Systems A Framework for AIS Algorithms Affinity Representation Application Solution AIS Shape-Space Binary Integer Real-valued Symbolic [De Castro and Timmis, 2002]

34. CBA - Artificial Immune Systems A Framework for AIS Algorithms Affinity Representation Application Solution AIS Euclidean Manhattan Hamming

35. CBA - Artificial Immune Systems A Framework for AIS Algorithms Affinity Representation Application Solution AIS Bone Marrow Models Clonal Selection Negative Selection Positive Selection Immune Network Models

36. Lecture 4CBA - Artificial Immune Systems Shape-Space An antibody can recognise any antigen whose complement lies within a small surrounding region of width (the cross-reactivity threshold) This results in a volume v e known as the recognition region of the antibody veve V S The Representation Layer veve veve [Perelson,1989]

37. Lecture 4CBA - Artificial Immune Systems Affinity Layer Computationally, the degree of interaction of an antibody-antigen or antibody-antibody can be evaluated by a distance or affinity measure The choice of affinity measure is crucial: It alters the shape-space topology It will introduce an inductive bias into the algorithm It needs to take into account the data-set used and the problem you are trying to solve The Affinity Layer

38. Lecture 4CBA - Artificial Immune Systems The Affinity Layer Affinity Affinity through shape similarity. On the left, a region where all antigens present the same affinity with the given antibody. On the right, antigens in the region b have a higher affinity than those in a

39. Lecture 4CBA - Artificial Immune Systems Hamming Shape Space 1 if Ab i != Ag i : 0 otherwise (XOR operator) The Affinity Layer

40. Lecture 4CBA - Artificial Immune Systems Hamming Shape Space (a) Hamming distance (b) r-contigous bits rule The Affinity Layer

41. CBA - Artificial Immune Systems Mutation - Binary Single point mutation Multi-point mutation

42. CBA - Artificial Immune Systems Affinity Proportional Mutation Affinity maturation is controlled – Proportional to antigenic affinity – (D*) = exp(- D*) – =mutation rate – D*= affinity – =control parameter

43. Lecture 4CBA - Artificial Immune Systems The Algorithms Layer Bone Marrow models ( Hightower, Oprea, Kim ) Clonal Selection – Clonalg(De Castro), B-Cell (Kelsey) Negative Selection – Forrest, Dasgputa,Kim,…. Network Models – Continuous models:Jerne,Farmer – Discrete models: RAIN (Timmis), AiNET (De Castro) The Algorithms Layer

44. Lecture 4CBA - Artificial Immune Systems Clonal Selection –CLONALG 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle The Algorithms Layer

45. Lecture 4CBA - Artificial Immune Systems 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Clonalg Create a random population of individuals (P) The Algorithms Layer

46. Lecture 4CBA - Artificial Immune Systems 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Clonalg For each antigenic pattern in the data-set S do: The Algorithms Layer

47. 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Lecture 4CBA - Artificial Immune Systems Clonal Selection Present it to the population P and determine its affinity with each element of the population The Algorithms Layer

48. 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Lecture 4CBA - Artificial Immune Systems Clonal Selection Select n highest affinity elements of P Generate clones proportional to their affinity with the antigen (higher affinity=more clones) The Algorithms Layer

49. Lecture 4CBA - Artificial Immune Systems 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Clonal Selection Mutate each clone High affinity=low mutation rate and vice-versa Add mutated individuals to population P Reselect best individual to be kept as memory m of the antigen presented The Algorithms Layer

50. 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Lecture 4CBA - Artificial Immune Systems Clonal Selection Replace a number r of individuals with low affinity with randomly generated new ones The Algorithms Layer

51. Lecture 4CBA - Artificial Immune Systems 1.Initialisation 2.Antigenic presentation a.Affinity evaluation b.Clonal selection and expansion c.Affinity maturation d.Metadynamics 3.Cycle Clonal Selection Repeat step 2 until a certain stopping criterion is met The Algorithms Layer

52. CBA - Artificial Immune Systems Naive Application of Clonal Selection Generate a set of detectors capable of identifying simple digits Represented as a simple bitmap

53. CBA - Artificial Immune Systems Representation Each individual is a bitstring Use hamming distance as affinity metric

54. CBA - Artificial Immune Systems Evolution of Detectors Clones Mutated clones

55. Lecture 5CBA - Artificial Immune Systems Negative Selection Algorithms Define Self as a normal pattern of activity or stable behavior of a system/process – A collection of logically split segments (equal-size) of pattern sequence. – Represent the collection as a multiset S of strings of length l over a finite alphabet. Generate a set R of detectors, each of which fails to match any string in S. Monitor new observations (of S) for changes by continually testing the detectors matching against representatives of S. If any detector ever matches, a change ( or deviation) must have occurred in system behavior. The Algorithms Layer

56. Lecture 5CBA - Artificial Immune Systems Illustration of NS Algorithm: Self Non_Self Self Match 1011 1000 Dont Match 1011 1101 r=2 The Algorithms Layer

57. CBA - Artificial Immune Systems Negative Selection Cross-reactivity threshold = 1 Here M[1,1], M[1,4] and M[2,2] are above the threshold Add these to Available repertoire Eliminate the rest.

58. QR Motivations Problems with RBS – Reasoning from First Principles – Dangers with nearest approximation Second Generation Expert Systems – Use deep knowledge – Provide explanations of reasoning process Commonsense reasoning – Capture how humans reason – Enable use of appropriate causality Model reuse – Improved ease of ES maintenance

59. Arithmetic Operations Sign Algebra + 0 0 + _ _ MULT DIV + + _ _ 0 0 0 0 0 + 0 0 + _ _ + + _ _ 0 0 X X X

60. Aritmetic Operations (2) + 0 0 + _ _ + + 0 _ + 0 0 + _ _ + _ 0 + ? ? _ _ ? ? + + _ _ ADD SUB

61. Arithmetic Operations (3) A = B - C where B & C both have value [+], A will be undefined Disambiguation – may be possible from other information – A = [+] if B > C – A = [0] if B = C – A = [-] if B < C Functional Relations – Y = M+(X) – Y = M-(X)

62. Curve Shapes + 0 0 + _ _ d1d1 d2d2

63. Transition Rules Intermediate Value Theorem (IVT) – States that for a continuous system, a function joining two points of opposite sign must pass through zero. Mean Value Theorem (MVT) – Defines the direction of change of a variable between two points. [++][+o][+-] [o+][oo][o-] [-+][-o][- -]

64. Single Compartment System plane 0 f10 = k10.x1 x1 = u - f10 plane 1 f10 = k10.x1 x1 = u - f10 plane 2 f10 = k10.x1 x1 = u - f10 1 u k 10.x 1

65. Models in Morven (define-fuzzy-model (short-name ) (variables ) (auxiliary-variables ) (input ) (constraints (print ) )

66. A JMorven Model model-name: single-tank short-name: fst NumSystemVariables: 2 variable: qorange: zero p-maxNumDerivatives: 1qspaces: tanks-quantity-space variable: V range: zero p-maxNumDerivatives: 2qsapces: tanks-quantity-space tanks-quantity-space2 NumExogenousVariables: 1 variable: qirange: zero p-maxNumDerivatives: 1qspaces: tanks-quantity-space Constraints: NumDiffPlanes: 2 Plane: 0NumConstraints: 2 Constraint: func (dt 0 qo) (dt 0 V) NumMappings: 9 Mappings: n-max n-large n-medium n-small zero p-small p-medium p-large p-max Constraint: sub (dt 1 V) (dt 0 qi) (dt 0 qo) NumVarsToPrint: 3VarsToPrint: V qi qo

67. A JMorven Quantity Space NumQSpaces: 2 QSpaceName: tanks-quantity-space NumQuantities: 9 n-max -1 -1 0 0.1 n-large -0.9 -0.75 0.05 0.15 n-medium -0.6 -0.4 0.1 0.1 n-small -0.25 -0.150.1 0.15 zero 0 0 00 p-small 0.15 0.25 0.15 0.1 p-medium 0.4 0.6 0.1 0.1 p-large 0.75 0.9 0.15 0.05 p-max 1 1 0.1 0 QSpaceName: tanks-quantity-space2 NumQuantities: 5 nl-dash -1 -0.750 0.15 ns-dash -0.6 -0.15 0.1 0.15 zero 0 0 0 0 ps-dash 0.15 0.6 0.15 0.1 pl-dash 0.751 0.15 0

68. Possible States statevectorstatevector 1+ + + +22+ - o + 2+ + + o23+ - o o 3+ + + -24+ - o - 4+ + o +25+ - - + 5+ + o o26+ - - o 6+ + o -27+ - - - 7+ + - +28o + + + 8+ + - o29o + + o 9+ + - -30o + + - 10+ o + +31o + o + 11+ o + o32o + o o 12+ o + -33o + o - 13+ o o +34o + - + 14+ o o o35o + - o 15+ o o -36o + - - 16+ o - +37o o + + 17+ o - o38o o + o 18+ o - -39o o + - 19+ - + +40o o o + 20+ - + o41o o o o 21+ - + -

69. Step Response t V

70. Solution Space 21 14 7 30 V qiqi

71. Cascaded Systems plane 0 qx = k1.h1 qo = k2.h2 h1 = qi - qx h2 = qx - qo plane 1 qx = k1.h1 qo = k2.h2 h1 = qi - qx h2 = qx - qo plane 2 qx = k1.h1 qo = k2.h2 h1 = qi - qx h2 = qx - qo Tank A Tank B 1 2 u k12.x1 k20.x2 h1h1 h2h2 qiqi qxqx qoqo

72. Cascaded Systems Envisionment 1 11 12 6 2 0 10 13 9 8 7 5 3 4

73. Cascaded Systems Solution Space h2h2 h1h1 h 1 =0 1 11 12 6 2 0 10 13 9 8 7 5 3 4