Species Trees & Constraint Programming: recent progress and new challenges

1. Species Trees & Constraint Programming:recent progress and new challenges By Patrick Prosser Presented by Chris Unsworth at CP06

2. Outline • Tree of life (what’s that then?) • Previous work (conventional and CP model) • What’s new? (enhanced model, new problems) • Conclusions (what have I told you!?) • Future work (will this never end?)

3. Tree of life • A central goal of systematics • construct the tree of life • a tree that represents the relationship between all living things • The leaf nodes of the tree are species • The interior nodes are hypothesized species • extinct, where species diverged

5. Not to be confused with this

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15. To date, biologists have cataloged about 1.7 million species yet estimates of the total number of species ranges from 4 to 100 million. “Of the 1.7 million species identified only about 80,000 species have been placed in the tree of life” E. Pennisi “Modernizing the Tree of Life” Science 300:1692-1697 2003

16. Properties of a Species Tree • We have a set of leaf nodes, each labelled with a species • the interior nodes have no labels (maybe) • each interior node has 2 children and one parent (maybe/ideally) • a bifurcating tree (maybe/ideally) • Note: recently there has been a requirements that • interior nodes have divergence dates • leaf nodes correspond to other trees (such as a leaf “cats”) • trees might not bifurcate

17. Super Trees • We are given two trees, T1 and T2 • S1 and S2 are the sets of leaves for T1 and T2 respectively • remember, leaves are species! • S1 and S2 have a non-empty intersection • some species appear in both trees • We want to combine T1 and T2 • respecting the relationships in T1 and T2 • form a “super tree”

18. superTree combine

19. Overlap is highlighted in the trees and the superTree

20. Overlap is leafs “a” and “f” A simple wee example

21. c a b a is closer to b than c Most Recent Common Ancestors (mrca) mrca(a,c) = mrca(b,c) mrca(a,b) We have 3 species, a, b, and c Species a and b are more closely related to each other than they are to c mrca(a,b) mrca(a,c) mrca(a,b)  mrca(b,c) mrca(a,c)  mrca(b,c) The most recent common ancestor of a and b is further from the root than the most recent common ancestor of a and c (and b and c) NOTE: mrca(x,y) = mrca(y,x)

22. c a b Most Recent Common Ancestors (mrca) mrca(a,c) = mrca(b,c) mrca(a,b) mrca(a,b) mrca(a,c) mrca(a,b)  mrca(b,c) mrca(a,c)  mrca(b,c) Note: this defines that Think of mrca(x,y) having integer value “depth”

23. a a b b c c c b a triples fan b c a Ultrametric relationship Given 3 leaf nodes labelled a, b, and c there are only 4 possible situations

27. a b c a c b b c a a b c That’s all that there can be, for 3 leafs

28. Another view a b c a c b b c a a b c A space made up of triangles a b c Given any three vertices the triangle is either isosceles or equilateral

29. Ultrametric relationship Given 3 leaf nodes labelled a, b, and c there are only 4 possible situations We can represent this using primitive constraints Where D[i,j] is a constrained integer variable representing the depth in the tree of the most recent common ancestor of the ith and jth species

30. Ultrametric constraint Therefore the ultrametric constraint is as follows Constraint acting between leaf nodes/species a, b, and c Where D[x,y] is depth in tree of mrca(x,y) D[x,y] can also be thought of as distance

31. How it goes (part 1) Conventional technology (circa 1981) • Take 2 species trees T1 and T2 • Use the “breakUp” algorithm (Ng & Wormald 1996) on T1 then T2 • - This produces a set of triples and fans • Use the “oneTree” algorithm (Ng & Wormald 1996) • - Generates a superTree or fails This is the “conventional” (non-CP) approach Different versions of oneTree and breakUp from Semple and Steel (I think) that treats fans differently (ignores them) oneTree is essentially the algorithm of Aho, Sagiv, Szymanski and Ullman in SIAM J.Compt 1981

32. breakUp generates constraints! F G C A B D E 1. Find deepest interior node 2. Get its descendants (leaf nodes) 3. Get a cousin or uncle leaf node 4. Generate a triple or fan 5. Delete one of the leafs in 2 6. Take the other leaf in 2 and make its parent that leaf 7. Go to 1 unless we are at the root with degree 2

33. breakUp generates constraints! F G C A B D E A deepest interior node Generate triple AB|C This is the constraint D[A,C] = D[B,C] < D[A,B]

34. breakUp generates constraints! F G C B D E A deepest interior node Generate triple DE|C This is the constraint D[D,C] = D[E,C] < D[D,E]

35. breakUp generates constraints! F G C E B A deepest interior node Generate fan BCE This is the constraint D[B,C] = D[B,E] = D[C,E]

36. breakUp generates constraints! E F G A deepest interior node Generate triple FG|E This is the constraint D[E,F] = D[F,G] < D[F,G]

37. breakUp generates constraints! E G Done The triples and fans can be viewed as constraints that break the ultrametric disjunctions

38. The 1st CP approach

39. How it goes (part 2) CP approach (circa 2003) • Generate an n by n array of constrained integer variables • For all 0<i<j<k<n post the ultrametric constraint • - Yes, we have a cubic number of constraints • - Yes, we have a quadratic number of variables • - This gives us an “ultrametric matrix” • Use breakUp on trees T1 and T2 to produce triples and fans • Post the triples and fans as constraints, breaking disjunctions • Find a first solution • Convert the ultrametric matrix to an ultrametric tree Algorithm for ultrametric matrix to ultrametric tree given by Dan Gusfield This is the CP approach proposed by Gent, Prosser, Smith & Wei in CP03 (a great great paper, go read it )

40. Key here is that we have an array of variables Representing distances and this space must be ultrametric

41. An min ultrametric tree and its min ultrametric matrix 3 5 4 8 D B C A E Matrix value is the value of the most recent common ancestor of two leaf nodes As we go down a branch values on interior nodes increase Matrix is symmetric

42. The state of play in 2003 • Coded up in claire & choco • more a ”proof of concept” than a useful tool • small data sets only

43. Two species trees of sea birds from the CP03 paper

44. Resultant superTree On the left by oneTree and on the right by CP model

45. What’s new 2006 • Reimplemented in java & JChoco (so faster) • More robust (thanks to Pierre Flener’s help) • Can now deal with larger trees (about 70 species) • Can generate all solutions up to symmetry • Can handle divergence dates on interior nodes • Reimplemented breakUp & oneTree in Java • All code available on the web

47. Bigger Trees Attempted to reconstruct the supertree in Kennedy & Page’s “Seabird supertrees: Combining partial estimates of rocellariiform phylogeny” in “The Auk: A Quarterly Journal of Ornithology” 119:88-108 2002 • 7 trees of seabirds (A through G) • Varying in size from 14 to 90 species

48. From the paper Table shows on the diagonal the size of each tree, A through G A table entry is the size of the combined tree A table entry in () if trees are incompatible A table entry of – if trees are too big for CP model The only compatible trees are A, B, D and F The resultant supertree has 69 species This takes 20 seconds to produce

50. A “lifted” representation Rather than instantiate the “D” variables why not just break the disjunctions? Now the decision variables are P[i,j,k] And yes, we have a cubic number of P variables