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Computational models of of biological biological Computational - - PowerPoint PPT Presentation

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Computational models of of biological biological Computational models systems systems Giancarlo Mauri Universit di Milano-Bicocca Complexity in in biology biology Complexity Molecular level Regulatory gene networks Protein

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Computational models Computational models of

  • f biological

biological systems systems

Giancarlo Mauri Università di Milano-Bicocca

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Complexity Complexity in in biology biology

  • Molecular level

– Regulatory gene networks – Protein folding

  • Cellular level

– Cell physiology

  • Organism level

– Immune system – Nervous system

  • Population level

– Population dynamics – Ecological systems

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Does Neural Communication Does Neural Communication Grow Grow on

  • n Trees

Trees? ?

Analysis of interspike intervals sequences to learn and generalize correlations among neurons

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The The Goals Goals

  • To search for discriminating parameters between

neural substrates sottending different perceptive states

  • To develop analysis strategies applicable to

spontaneous neural activities

  • To understand neural code
  • To infer (thalamocortical) networks of neurons

from simultaneous record of their firing activity

  • To study the neurophysiology of (cronic) pain
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State of the art State of the art

  • Gerstein, Aertsen 1985: Crosscorrelograms to

study cooperative firing activity in simultaneously recorded populations of neurons

  • Knierim, McNaughton 2001: analysis of records of

hippocampal place-cells firing through embedding in a vector space

  • Victor, Purpura 2001: metric space based on edit

distance

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State of the art State of the art

  • Rieke et al. 1997; Borst, Theunissen 1999; Johnson

et al 2001: Information theoretical analysis of neural coding

  • Panzeri et al. 1999: study of the capacity of neural

channels

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The The tools tools

  • Longest Common Subsequence
  • Lempel-Ziv complexity and LZ-Trees
  • Tree Compression
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Encoding Encoding neuron neuron’ ’s activity s activity

Record

Time Diagram

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Encoding Encoding neuron neuron’ ’s activity s activity

1 2 3 4 5 6 7 8 9 10 11 12

Record

Time discretization

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Encoding Encoding neuron neuron’ ’s activity s activity

1 2 3 4 5 6 7 8 9 10 11 12

0 1 0 1 0 0 0 0 1 0 0 0

Record

Binary encoding

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Encoding Encoding neuron neuron’ ’s activity s activity

1 2 3 4 5 6 7 8 9 10 11 12

Interspike Intervals Spike Times Record

Encoding through interspike intervals

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Alphabets Alphabets, , words words, , languages languages Alphabet

=

finite set S of elements called letters,characters or symbols

Examples S = {0,1} S = {a, b, c, ..., v, z} S = {A, C, G, T} S = {GLY, ALA, VAL, LEU}

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Alphabets Alphabets, , words words, , languages languages Word, string or sequence over S

=

function w from {1,... ,n} to S

n We write w = a1 a2 ... an where ai = w(i) ΠS n n is the length of the sequence, denoted by |w| n S* denotes the set of words over S

EX: w = AATGCA |w| = 6 Empty word e |e| = 0

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Alphabets Alphabets, , words words, , languages languages Concatenation of w and v, wv

=

word consisting of the characters from w, followed by the characters from v

  • ES: w = AATGCATAGGC

v = GGCTACT w v = AATGCATAGGCGGCTACT

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Alphabets Alphabets, , words words, , languages languages Prefix of w

=

string v such that w = vt for some t ŒS*

Suffix of w

=

string v such that w = tv for some t ŒS*

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Longest Common Longest Common Subsequence Subsequence

Let S1 and S2 be two sequences over S. S2 is a subsequence of S1 if it can be obtained from S1 by removing some of its symbols S1 = T A T A G C G C A A T C G S2 = T A T G C A T G S2 is subsequence of S1

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Longest Common Longest Common Subsequence Subsequence

Let S be a set of sequences. S is a common subsequence of S if it is a subsequence of every sequence in S Problem (LCS): Given a set S of sequences, compute a longest common subsequence lcs(S)

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Longest Longest Common Common Subsequence Subsequence, , an an example example

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Longest Longest Common Common Subsequence Subsequence

Def: Given an alphabet S and sequences S1, S2 Œ S*, lcs(S1, S2) is a sequence W such that: 1) "i, 1£ i £ |W|-1, $j, j’: 1 £ j < j’ £ | S1|, $ k, k’: 1 £ k < k’ £ | S2| such that: W[i]= S1[j]= S2[k], and W[i+1]= S1[j’]= S2[k’]; 2) ¬ $ W’ ŒS*: (1) and |W’| > |W|.

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LCS in LCS in sequence analysis sequence analysis

The lcs is able to:

  • Measure the similarity among a set of sequences

through its length

  • Exhibit the nature of the similarity through the

symbols it contains Applications in:

  • data compression
  • syntactic pattern recognition
  • file comparison
  • bioinformatics
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Complexity Complexity of LCS

  • f LCS
  • Many polynomial time algorithms for LCS on two

sequences

  • Maier 78: LCS among k sequences is NP-hard
  • Jiang, Li 95: nonapproximability results
  • Jiang, Li 95: Long Run, approximation algorithm
  • ver a fixed alphabet
  • Bonizzoni, Della Vedova, Mauri 98:better

approximation ratio on the average

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LCS, LCS, Relaxed Relaxed

Def: Given an alphabet S, Sà SÃN, sequences S1, S2 Œ S*, d ≥ 0, LCSd(S1, S2) is a sequence W such that: d 1) "i, 1£ i £ |W|-1, $j, j’: 1 £ j < j’ £ | S1|, $ k, k’: 1 £ k < k’ £ | S2| such that: W[i] = S1[j] = S2[k] ± e, and W[i+1] = S1[j’] = S2[k’] ± e, with 0 £ e £ d ; 2) ¬ $ W’ŒS*: (1) and g(MW’, S1, S2) > g(MW, S1, S2), where:

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LCS, LCS, Relaxed Relaxed

"S1, S2, WŒS ŒS*, MW(S1, S2):={(j, k) | 1£j£| S1|, 1£ k£| S2|, $i: 1£ i£|W| st: W[i]= S1[j]= S2[k] ± e, with 0 £ e £ d ; and if 1 £ i £ |W|-1, then $j’: 1£ j’£| S1|, $k’: 1£k’£| S2| such that: (W[i+1]= S1[j’]= S2[k’] ± e) Ÿ (j’>j) Ÿ (k’>k), with 0 £ e £ d ; } and where: g(M, S1, S2):= _(j, k) ŒMcost(S[j], S[k]); and cost(a, b):=1-|a-b|, with a, b ŒS.

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LCS ( LCS (Relaxed Relaxed), ), an example an example

S1: S2: LCS(S1,S2):

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Lempel Lempel-

  • Ziv

Ziv complexity complexity

  • L. & Z. propose as a complexity measure of a sequence the

minimum number of steps needed to produce it from its prefixes using copy and paste operations

  • L. & Z. give an algorithm to compute the above measure
  • The complexity notion defined by L. & Z. is compatible

with the algorithmic complexity theory (Kolmogorov, Chaitin)

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Lempel Lempel-

  • Ziv

Ziv Algorithm Algorithm

INPUT: SŒS ŒS*; OUTPUT: w={Q ŒS ŒS* | $i, j: S[i:j]=Q}; w := f; w := w » {e}; curr := 1; while curr ≤ |S| do begin S’ := S[curr:n] s.t. S’ Œ w and S’°S[n+1] œ w; w := w » {S’°S[n+1] }; curr := n+2; end NOTE: S[i:j]= e for j<i

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Lempel Lempel-

  • Ziv

Ziv -

  • Trees

Trees

  • The vocabulary w obtained can be organized in a

hierarchical (tree) structure through the prefix relation: prefix := { (u, v) | u, vŒw and $i: u=v[1:i] };

  • Every word in w (except e) can be obtained by adding a

single symbol to another word in w; hence, it can be encoded through a pointer to its maximal prefix, plus the last symbol

  • LZCompl(S) := |w| / |S|
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Lempel Lempel-

  • Ziv

Ziv-

  • Trees

Trees, , an example an example

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Lempel Lempel-

  • Ziv

Ziv-

  • Trees

Trees, , meaning meaning

  • Acquisition of knowledge about the regularity of
  • ccurrence of symbol patterns in the sequence
  • Structuring of knowledge so as to give a

representation of the sequence shortest than the list of its symbols.

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Tree Tree Compression Compression, , an example an example

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Tree Compression Tree Compression, , meaning meaning

  • Reduction of redundancy in the tree structure
  • Minimization of hierarchical knowledge representations
  • Abstraction and generalization of the knowledge

empirically acquired

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Edit Distance Edit Distance between trees between trees

Let T be a rooted labeled tree over a given alphabet S : T = < V, E, r, lab: VÆS > and let have the following operations on it :

  • Insertion of an element: eÆ

eÆa, aŒS ŒS;

  • Deletion of an element: aÆe, aŒS;
  • Substitution of the label of an element: aÆb, a, b ŒS

ŒS;

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Edit Distance between trees Edit Distance between trees

EditOps := {aÆb | a, b Œ S»{e} }\{eÆe}; Given the (metric) cost function : g: EditOps Æ R+; We define the cost of a sequence SopŒ EditOps* as g(Sop) = Si=1,..,|Sop| g(Sop[i]).

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Edit Distance between trees Edit Distance between trees

Def: Given two labeled trees T e T’, the edit distance between them is defined by: Edist(T, T’) := min SopŒEditOps*{g(Sop) | T’= Sop(T) }.

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Tree Compression Tree Compression, , Algorithm Algorithm

proc TreeCompr( tot ŒR, < &T, &Sop > ) :

if ( VT ≠ f ){ if ( Edist(Tdx(rT), Tsx(rT)) < threshold ) { Prune(Tdx(rT)); TreeCompr( tot, < Tdx, Sop°SopEdist(Tdx(rT), Tsx(rT)) > ); } else { TreeCompr( tot, < Tdx, Sop > ); TreeCompr( tot, < Tsx, Sop > ); } }

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Tree Complexity Tree Complexity

Def: given a tree T, let T’ and SopŒEditOps the results of the compression of T through TreeCompr; the Tree Complexity of T is: TC(T) := ( |T’| / |T| ) +a·g(Sop)

where 0 £ a £ 1

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Tree Complexity Tree Complexity

Teorema: The computation of the tree complexity ofa tree T based on an Edit Distance Structure Respecting has time complexity : O(D3·|T|2), where D is the maximum degree of nodes in T.

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Application Application

Analysis of sequences of Interspike Intervals from simultaneous recordings of talamic and cortical cells populations. Motivation: key role of talamocortical areas in the elaboration of somatosensorial stimuli. Goal: to discover rythmic correlations among cells activities.

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Application Application, LCS , LCS

NORM: CCI:

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Application Application, LZ- , LZ-Complexity Complexity

NORM: CCI:

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Applicazione, Applicazione, CplArb CplArb

NORM: CCI:

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Application Application, , conclusions conclusions

The three kinds of di analysis help us to enlightening different aspects of the process we are

  • bserving:
  • LCS

Omogeneity

  • Ziv-Tree

Monotonicity

  • Tree compression

Fault Tolerance