\documentclass[landscape,dvips]{foils}

%\documentclass[a4paper,landscape,headrule]{foil


%\usepackage[latin1]{inputenc}
%\usepackage{graphicx}

%\MyLogo{Slide}

\usepackage{amsmath,amsthm,amssymb,amscd}
\usepackage{alltt}
\usepackage{epsf}
%\usepackage{amsfonts}
\def\w{{\cal W}}
\usepackage{latexsym}
\usepackage{times}
\usepackage{bookman}
\usepackage{graphics}
\usepackage{color}
\usepackage{epsfig}
\usepackage{psfrag}
\usepackage[ps2pdf]{hyperref}

\newcommand{\img}[2]{{\\epsfxsize=#1truecm\epsfbox{Images/PS/#2}}}
\newtheorem{theorem}{Theorem}
\newtheorem{lemma}{Lemma}
\newtheorem{corollary}{Corollary}
\newtheorem{definition}{Definition}
\newtheorem{proposition}{Proposition}

\def\basswidt{4.0in}
\def\gutter{0.2in}

\newenvironment{comment}{\setbox0=\vbox\bgroup}{\egroup}
\newenvironment{singlespace}{\def\baselinestretch{1.0}\large\normalsize}{\par}

%\setlength{\textheight}{8.5in}
%\setlength{\textwidth}{6in}
\setlength{\textheight}{8in}
\setlength{\textwidth}{9in}
\voffset=-0.9in

\def\helv{\sffamily bfseries}
\def\noi{\noindent}
\def\la{\langle}
\def\rf{\rangle}
\def\eod{\vrule height 6pt width 5pt depth 0pt}
\def\parsec{\par\noindent}
\def\big{\bigskip\parsec}
\def\med{\medskip\parsec}
\def\aa{\alpha}
\def\bb{\beta}
\def\dd{\delta}
\def\pr{{\rm Pr}}
\def\l{\ell}
\def\t{\theta}
\def\d{\delta}
\def\eps{\varepsilon}
\def\lf{\lfloor}
\def\rf{\rfloor}
\def\wt{\widetilde}
\def\ol{\widehat}
\def\TG{\wt{G}}
\def\TX{\wt{X}}
\def\TF{\wt{F}}
\def\TV{\wt{V}}
\def\C{{\cal C}}
\def\S{{\cal S}}
\def\P{{\cal P}}
\def\Var{{\bf Var}}
\def\E {{\bf E }}
\def\kk{\kappa}
\def\TH{\widetilde{H}}
\def\th{\widetilde{h}}
\def\la{\langle}
\def\ra{\rangle}
\def\om{\omega}
\def\L{{\cal L}}
\def\I{{\cal I}}
\def\sign{\hbox{\rm sign}}
\def\w{{\cal W}}
\def\W{{\cal W}}
\def\A{{\cal A}}
\def\M{{\cal M}}
\def\N{{\cal N}}
\def\D{{\cal D}}
\def\B{{\cal B}}
\def\hb{\hat{b}}
\def\MT{{\bf T}}
\def\bb{{\bf b}}
\def\vpi{\mbox{\boldmath $\pi$}}
\def\pstat{{\bf \Pi}}

%\def\bb{\beta}
\def\ee{\epsilon}
\def\GG{\Gamma}
\def\mm{\mu}
\def\oo{\omega}
\def\ss{\sigma}
\def\w{{\cal W}}
\def\E{{\mathbf E}}
\def\G{{\mathbf G}}
\def\V{{\mathbf V}}
\def\vv{{\mathbf v}}
\def\uu{{\mathbf u}}

\def\roh{\rho _{H}}
\def\alph{{\cal S}}
\def\te{{\bf t}}
\def\pat{{\bf p}}
\def\ahz{A_\w(z)}
\def\phb{P(H)}
\def\pht{P_H(H)}
\def\langa{{\cal S}}
\def\lgh{{\cal R}}
\def\lrh{{\cal R}}
\def\llh{{\cal L}}
\def\luh{{\cal U}}
\def\lah{{\cal S}}
\def\lmh{{\cal M}}

\def\lbar{\bar{L} (z)}
\def\ltilde{L_{\w} (z)}
\def\dht{D_{\w}(z)}
\def\mht{{\bf M}(z)}
\def\uht{U_{\w}(z)}
\def\uhit{U_{\w}^i(z)}
\def\aht{A_{\w}(z)}
\def\bht{B_{\w}(z)}
\def\mfz{{F} (z)}
\def\mgz{{\cal G} (z)}
\def\rhi{R_{i}(z)}
\def\fah{A_\w(z)}
\def\fuh{L_\w(z)}
\def\dh{D_\w}
\def\nh{N_\w}
\def\ah{A_{\w}}
\def\dhz{{P(H)z^m+(1-z)A_\w(z)}}
\def\nhz{{P(H)z^m+(1-z)(A_\w(z)-1)}}
\def\ltr{{{\cal T} _r}}
\def\ltt{{{\cal T} }}
\def\lt0{{{\cal T}_0}}
\def\T{{\cal T}}
\def\trn{t_{r,n}}
\def\Var{{\bf Var}}
\def\L{{\cal L}}
\def\R{{\cal R}}
\def\U{{\cal U}}
\def\M{{\cal M}}
\def\TM{M_{\w}}
\def\TU{U_{\w}}
\def\VP{{\bf P}}
\def\VZ{{\bf Z}}

\def\I{{\bf I}}
\def\CD{{\cal D}}
\def\CP{{\cal P}}
\def\cha#1{{[\![\,#1\,]\!]}}

\def\noi{\noindent}
\def\lf{\lfloor}
\def\rf{\rfloor}
\def\la{\langle}
\def\ra{\rangle}
\def\eod{\vrule height 6pt width 5pt depth 0pt}
\def\parsec{\par\noindent}
\def\big{\bigskip\parsec}
\def\med{\medskip\parsec}
\def\ol{\overline}
\def\P{{\bf P}}
\def\l{\ell}
\def\eps{\varepsilon}
\def\r{\bar R}
\def\R{\bar R}
\def\A{{\cal A}}
\def\E{{\bf E}}
\def\CR{{\cal R}}
\def\P{{\cal P}}
\def\S{{\cal S}}
\def\a{\alpha}
\def\M{{\cal M}}
\def\H{{\cal H}}
\def\SF{{\cal SF}}
\def\Z{{\Bbb Z}}
\def\Real{{\Bbb R}}
\def\RR{\Real}
\def\CC{{\Bbb C}}
\def\th{\theta}
\def\Var{{\rm Var ~}}
\def\phb{P(H)}
\def\VP{{\bf P}}
\def\pstat{{\bf \Pi}}
\def\VT{{\bf T}}
\def\F{{\Bbb F}}
\def\l{\ell}
\def\W{{\cal W}}
\def\VZ{{\bf Z}}
\def\vpi{\mbox{\boldmath $\pi$}}
\def\I{{\bf I}}
\def\wt{\widetilde}
\def\pr{{\rm Pr}}
\def\eps{\varepsilon}
\def\lef{\preceq}

\newcommand{\blue}[1]{{\color{blue} #1}}
\newcommand{\green}[1]{{\color{green} #1}}
\newcommand{\red}[1]{{\color{red} #1}}
\newcommand{\stitle}[1]{\foilhead{\red{#1}}\vspace{-0.5in}}
\newcommand{\bigitem}[2]{\item{#1\begin{small}\begin{itemize}#2\end{itemize}\end{small}}}

\begin{document}

\title{{\color{red}
Building Cellular Interaction Databases: Analysis, Synthesis, and
Interfaces}}

\author{Mehmet Koyuturk, Yohan Kim, \\
Shankar Subramaniam, and Ananth Grama}

\maketitle

\thispagestyle{empty}

%Slide
\stitle{Outline}
\begin{enumerate}

\bigitem{Biological Networks}
{\item Definition, problems, practical implications}

\bigitem{Current Work}
{
 \item Mining biological networks for frequent molecular interaction patterns
 \item Alignment of protein interaction networks based on evolutionary models
 \item Module identification based on phylogenetic profiles
}

\bigitem{Ongoing and Future Work}
{
 \item Constructing reference module maps
 \item Building a fully functional interoperable signaling database
}

\end{enumerate}

%Slide
\stitle{Biological Networks}

\begin{itemize}

\bigitem{Interactions between \green{biomolecules} that drive cellular
\blue{processes}}
{\item \green{Genes, proteins, enzymes, chemical compounds}
 \item \blue{Mass \& energy generation, information transfer}
 \item  Coarser level than sequences in life's complexity pyramid
}

\bigitem{Experimental/induced data in various forms}
{\item Protein-protein interaction networks
 \item Gene regulatory networks
 \item Metabolic \& signaling pathways
}

\bigitem{What do we gain from analysis of cellular networks?}
{\item Modular analysis of cellular processes
 \item Understanding evolutionary relationships at a higher level
 \item Assigning functions to proteins through interaction information
 \item Intelligent drug design: block protein, preserve pathway 
}

\end{itemize}

%Slide
\stitle{Protein-Protein Interaction (PPI) Networks}

\begin{itemize}
\bigitem{Interacting proteins can be discovered experimentally}
{\item Two-hybrid
 \item Mass spectrometry
 \item Tandem affinity purification (TAP)
}
\end{itemize}

\vspace{-0.15in}
\centerline{
\begin{psfrags}
\psfrag{P}[c][c]{\blue{Protein}}
\psfrag{I}[c][c]{\red{Interaction}}
\epsfig{file=pin.eps, height=2.5in}
\leavevmode
\end{psfrags}
\hspace{1.7in}
\epsfig{file=pinsample.eps, height=2.8in}
\hspace{0.3in}
}
\centerline{\hspace{4in} {\sl S. Cerevisiae} protein interaction network}
\centerline{\hspace{4in}\scriptsize{Source: Jeong et al. Nature 411: 41-42, 2001.}}

%Slide
\stitle{Gene Regulatory Networks}

\begin{itemize}
\bigitem{Genes regulate each others' expression}
{\item A simple model: Boolean networks
 \item Can be derived from gene expression data
}
\end{itemize}
\vspace{0.5in}
\centerline{
\begin{psfrags}
\psfrag{G}[c][c]{\blue{Gene}}
\psfrag{D}[c][c]{\red{Down-regulation}}
\psfrag{U}[c][ct]{\green{Up-regulation}}
\epsfig{file=grn.eps, height=2.8in}
\leavevmode
\end{psfrags}
\hspace{1.7in}
\epsfig{file=grnsample.eps, height=3.1in}
\hspace{0.3in}
}
\centerline{\hspace{4in}Genetic network that controls}
\centerline{\hspace{4in}flowering time in {\sl A. Thaliania}}
\centerline{\hspace{4in}\scriptsize{Source: Blazquez et al. EMBO Reports  2: 1078-1082, 2001}}

%Slide
\stitle{Metabolic Pathways}
\begin{itemize}
\bigitem{Chains of reactions that perform a particular metabolic function}
{\item Reactions are linked to each other through substrate-product relationships
 \item Directed hypergraph/ graph models
}
\end{itemize}

\vspace{0.1in}
\centerline{
\begin{psfrags}
\psfrag{E}[l][r]{\blue{Enzyme}}
\psfrag{S}[l][r]{\red{Substrate}}
\psfrag{P}[l][r]{\red{Product}}
\psfrag{C}[l][r]{\green{Compound}}
\epsfig{file=pat.eps, height=2.8in}
\leavevmode
\end{psfrags}
\hspace{2.5in}
\epsfig{file=patsample.eps, height=4in}
\hspace{0.3in}
}
\centerline{\hspace{4in} Glycolysis pathway in {\sl S. Cerevisiae}}
\centerline{\hspace{4in}\scriptsize{Source: Hynne et al. Biophysical Chemistry, 94, 121-163, 2001.}}

%Slide
\stitle{Discrete Algebraic Techniques in Analysis}
\begin{itemize}
\bigitem{Non-orthogonal decomposition of binary matrices}
{\item Find a compact set of vectors that represent the entire matrix
 \item Recursive decomposition through rank-one approximations 
 \item Fast (linear-time) iterative heuristics for computing approximations
}
\end{itemize}

\centerline{\blue{Analysis of gene expression data}}
\vspace{0.2in}
\centerline{\green{Patterns of regulation} \hspace{1.8in} \green{Biclustering}\hspace{0.6in}}
\vspace{0.1in}
\centerline{
\epsfig{file=regpattern.eps, width=3.5in}
\hspace{0.5in}
\epsfig{file=bicluster.eps, width=3.5in}
}
\vspace{0.1in}
\centerline{\scriptsize{``Algorithms for bounded-error correlation of}\hspace{0.9in}
\scriptsize{``Biclustering gene-feature matrices for}\hspace{0.1in}}
\vspace{-0.1in}
\centerline{\scriptsize{high dimensional data in microarray experiments''}\hspace{0.7in}
\scriptsize{statistically significant dense patterns''}\hspace{0.4in}} 
\vspace{-0.1in}
\centerline{\scriptsize{Koyut\"urk, Grama, Szpankowski: {\sl CSB'03.}}\hspace{1in}
\scriptsize{Koyut\"urk, Grama, Szpankowski: {\sl CSB'04}.}}
%Slide
\stitle{Analysis of Biological Networks}

\begin{itemize}
\bigitem{Evolution thinks modularly}
{\item Selective pressure on preserving interactions
 \item Functional modules, protein complexes are highly conserved
}
\bigitem{Computational methods for discovery and analysis of modules and complexes}
{\item \blue{Graph clustering:} Functionally related entities are densely connected
 \item \green{Graph mining:} Common topological motifs, frequent interaction patterns reveal 
       modularity
 \item \green{Graph alignment:} Conservation/divergence of modules and pathways
 \item \red{Module maps:} Canonical pathways across species
 \item \red{Phylogenetic analysis:} Genes/proteins that belong to a common module
       are likely to have co-evolved
}
\end{itemize}

%Slide
\stitle{How do we detect conserved subgraphs: Graph Mining}
[Koyuturk, Grama, Szpankowski, ISMB04, Bioinformatics04]

\vspace{1in}
\centerline{
\epsfig{file=graph1.eps,width=2.2in}
\hspace{0.5in}
\epsfig{file=graph2.eps,width=2.2in}
\hspace{0.5in}
\epsfig{file=graph3.eps,width=2.2in}
}
\vspace{0.5in}
\centerline{Graph database}
\vspace{1in}
\centerline{
\epsfig{file=subgraph1.eps,width=4in}
\hspace{0.5in}
\epsfig{file=subgraph2.eps,width=4in}
}
\vspace{0.5in}
\centerline{Subgraphs with frequency 3}

%Slide
\stitle{Extending Frequent Itemset Mining to Graph Mining}
\begin{itemize}
\item {Given a set of transactions, find sets of items that
are frequent in these transactions}
\item Extensively studied in data mining literature
\bigitem {Algorithms exploit \blue{downward closure} property}
{\item A set is frequent only if all of its subsets are frequent
 \item Generate itemsets from small to large, pruning supersets of 
       infrequent sets}
\bigitem {Can be generalized to mining graphs}
{\item \blue{transaction} $\rightarrow$ \green{graph}
 \item \blue{item} $\rightarrow$ \green{node}, \green{edge}
 \item \blue{itemset} $\rightarrow$ \green{subgraph}
}
\item However, the graph mining problem is considerably more difficult
\end{itemize}

%Slide
\stitle{Graph Mining Challenges}
\begin{itemize}
\bigitem {Subgraph Isomorphism}
{\item For counting frequencies, it is necessary to check whether a given graph 
       is a subgraph of another one
 \item NP-complete
}
\bigitem{Canonical labeling}
{\item To avoid redundancy while generating subgraphs, canonical labeling of 
       graphs is necessary
 \item Equivalent to subgraph isomorphism
}
\bigitem{Connectivity}
{\item Patterns of interest are generally connected, so it is necessary to only 
       generate connected subgraphs
}
\bigitem{Existing algorithms mainly focus on minimizing redundancy and 
      mining \& extending simple substructures}
{\item AGM, FSG, gSpan, SPIM, {\sc CloseGraph}}

\end{itemize}

%Slide
\stitle{Uniquely-Labeled Graphs}

\begin{itemize}
\item {\blue{Contract nodes with identical label into a single node}}
\bigitem {\green{No} subgraph isomorphism}
{\item Graphs are uniquely identified by their edge sets}
\bigitem{Frequent subgraphs are \green{preserved} $\Rightarrow$ No information loss}
{\item Subgraphs that are frequent in general graphs are also frequent in 
       their uniquely-labeled representation}
\item Discovered frequent subgraphs are still \green{biologically interpretable}!
\end{itemize}
\vspace{0.3in}
\centerline{
\epsfig{file=unique1.eps,width=4.5in}
}

%Slide
\stitle{Node Contraction in Metabolic Pathways}

\begin{itemize}
\bigitem{Uniquely-labeled directed graph model}
{\item Nodes represent enzymes
 \item Global labeling by enzyme nomenclature (EC numbers)
 \item A directed edge from one enzyme to the other implies that the second consumes 
       a product of  the first}
\end{itemize}

\vspace{0.2in}
\centerline{\begin{psfrags}\begin{small}
\psfrag{C267}[c][c]{\green{267}}
\psfrag{C221}[c][c]{\green{221}}
\psfrag{C668}[c][c]{\green{668}}
\psfrag{C1172}[c][c]{\green{1172}}
\psfrag{5133}[c][c]{\blue{5.1.3.3}}
\psfrag{2711}[c][c]{\blue{2.7.1.1}}
\psfrag{2712}[c][c]{\blue{2.7.1.2}}
\psfrag{27163}[c][c]{\blue{2.7.1.63}}
\epsfig{file=patcontract.eps,height=4in}
\end{small}\end{psfrags}}

%Slide
\stitle{Node Contraction in Protein Interaction Networks}

\begin{itemize}
\bigitem{Relating proteins in different organisms}
{\item Clustering: Orthologous proteins show sequence similarities
 \item Phlyogenetic analysis: Allows multi-resolution analysis among distant species
 \item Literature, ortholog databases}
\bigitem{Contraction}
{\item Interaction between proteins $\rightarrow$ interaction between protein families}
\end{itemize}

\vspace{0.2in}
\centerline{\begin{psfrags}\begin{small}
\psfrag{548}[c][c]{\blue{KAPA}}
\psfrag{549}[c][c]{\blue{KAPB}}
\psfrag{550}[c][c]{\blue{KAPC}}
\psfrag{1735}[c][c]{\green{YPT7}}
\psfrag{2025}[c][c]{\green{YPT53}}
\psfrag{Kinase}[c][c]{\blue{cAMP}}
\psfrag{GTP}[c][c]{\green{GTP-binding}}
\epsfig{file=pincontract.eps,height=3in}
\end{small}\end{psfrags}}

%Slide
\stitle{Preservation of Subgraphs}

\blue{Theorem:} Let $\tilde{G}$ be the uniquely-labeled graph obtained
by contracting the same-label nodes of graph $G$. Then, \green{ if $S$ 
is a subgraph of $G$, $\tilde{S}$ is a subgraph of $\tilde{G}$}.

\blue{Corollary:} The uniquely-labeled representation of any frequent subgraph 
is frequent in the set of uniquely-labeled graphs.

\vspace{1in}
\centerline{\begin{psfrags}
\psfrag{G}[c][c]{$G$}
\psfrag{G2}[c][c]{$\tilde{G}$}
\epsfig{file=preserve.eps,height=2.5in}
\end{psfrags}}

%Slide
\stitle{Simplifying the Graph Mining Problem}

\blue{Observation:} A uniquely-labeled graph is uniquely determined by 
the set of its edges.

\vspace{0.5in}
\centerline{\blue{Maximal Frequent Subgraph Mining Problem}}
Given a set of \green{labeled graphs} $\{G_1, G_2, ..., G_m\}$, find all 
\green{connected graphs} $S$ such that $S$ is a \green{subgraph} of at 
least $\sigma m$ of the \green{graphs} (is frequent) and no \green{supergraph}
of $S$ is frequent (is maximal).

\vspace{0.5in}
\centerline{\blue{Maximal Frequent Edgeset Mining Problem}}
Given a set of \green{edge transactions} $\{E_1, E_2, ..., E_m\}$, find all 
\green{connected edge sets} $F$ such that $F$ is a \green{subset} of at 
least $\sigma m$ of the \green{edge transactions} (is frequent) and no 
\green{superset} of $F$ is frequent (is maximal).

%Slide
\stitle{From Graphs to Edgesets}

\vspace{0.8in}
\centerline{
\begin{psfrags}
\small 
\psfrag{a}[c][c]{$a$} 
\psfrag{b}[c][c]{$b$}
\psfrag{c}[c][c]{$c$} 
\psfrag{d}[c][c]{$d$} 
\psfrag{e}[c][c]{$e$}
\psfrag{G1}[c][c]{\green{$G_1$}} 
\psfrag{G2}[c][c]{\green{$G_2$}}
\psfrag{G3}[c][c]{\green{$G_3$}} 
\psfrag{G4}[c][c]{\green{$G_4$}} 
\psfrag{F1}[l][l]{\green{$F_1$}$=\{ab,ac,de\}$} 
\psfrag{F2}[l][l]{\green{$F_2$}$=\{ab,ac,bc,de,ea\}$} 
\psfrag{F3}[l][l]{\green{$F_3$}$=\{ab,ac,bc,ea\}$} 
\psfrag{F4}[l][l]{\green{$F_4$}$=\{ab,ce,de,ea\}$} 
\leavevmode
\epsfig{file=graphs.eps, height=4in}\hspace{2in}
\end{psfrags}
}

%Slide
\stitle{{\sc Mule}: Mining Uniquely Labeled Graphs}

\vspace{1in}
\centerline{
\begin{psfrags}
\scriptsize
\psfrag{e0}[c][c]{$E=\emptyset$}
\psfrag{h0}[c][c]{$H=\{1,2,3,4\}$}
\psfrag{v1}[r][r]{$D=\{ab\}$}
\psfrag{e1}[c][c]{$E=\{ab\}$}
\psfrag{c1}[c][c]{$C=\{ac,ea\}$}
\psfrag{h1}[c][c]{$H=\{1,2,3,4\}$}
\psfrag{v2}[l][l]{$D=\{ab,ac\}$}
\psfrag{e2}[c][c]{$E=\{ac\}$}
\psfrag{c2}[c][c]{$C=\{ea\}$}
\psfrag{h2}[c][c]{$H=\{1,2,3\}$}
\psfrag{v3}[l][l]{$D=\{ab,ac,de\}$}
\psfrag{e3}[c][c]{$E=\{de\}$}
\psfrag{c3}[c][c]{$C=\{ea\}$}
\psfrag{h3}[c][c]{$H=\{1,2,4\}$}
\psfrag{v4}[l][l]{$D=\{ab,ac,de,ea\}$}
\psfrag{e4}[c][c]{$E=\{ea\}$}
\psfrag{c4}[c][c]{$C=\emptyset$}
\psfrag{h4}[c][c]{$H=\{2,3,4\}$}
\psfrag{v5}[r][r]{$D=\{ab,ac\}$}
\psfrag{e5}[c][c]{$E=\{ab,ac\}$}
\psfrag{c5}[c][c]{$C=\{ea\}$}
\psfrag{h5}[c][c]{$H=\{1,2,3\}$}
\psfrag{v6}[l][l]{$D=\{ab,ac,ea\}$}
\psfrag{e6}[c][c]{$E=\{ab,ea\}$}
\psfrag{c6}[c][c]{$C=\{de\}$}
\psfrag{h6}[c][c]{$H=\{2,3,4\}$}
\leavevmode 
\epsfig{file=estree.eps, width=8in}
\end{psfrags}
}

%Slide
\stitle{Frequent Sub-Pathways in KEGG}

\vspace{0.5in}
\centerline{Glutamate metabolism (155 organisms)}

\vspace{0.5in}
\centerline{
\begin{psfrags}
\small 
\psfrag{gltX}[c][c]{gltX} \psfrag{glnA}[c][c]{glnA}
\psfrag{glmS}[c][c]{glmS} \psfrag{guaA}[c][c]{guaA}
\psfrag{nadE}[c][c]{nadE} \psfrag{purF}[c][c]{purF} 
\psfrag{B}[l][l]{\blue{45 (29\%) organisms}}
\psfrag{R}[l][l]{\red{30 (19\%) organisms}}
\psfrag{G}[l][l]{\green{22 (14\%) organisms}}
\leavevmode
\epsfig{file=glutamate.eps, width=4.5in}\hspace{2in}
\end{psfrags}
}

%Slide
\stitle{Frequent Interaction Patterns in DIP}

\begin{itemize}
\bigitem{Protein interaction networks for 7 organisms}
{\item Ecoli, Hpylo, Scere, Celeg, Dmela, Mmusc, Hsapi 
 \item 44070 interactions between 16783 proteins}
\bigitem{Clustering with TribeMCL \& node contraction}
{\item 30247 interactions between 6714 protein families}
\end{itemize}

\centerline{
\begin{psfrags}
\scriptsize
\psfrag{0}[c][c]{KI} 
\psfrag{1}[c][c]{MY} 
\psfrag{2}[c][c]{ME} 
\psfrag{3}[c][c]{ZF} 
\psfrag{4}[c][c]{PR} 
\psfrag{7}[c][c]{PN} 
\psfrag{8}[c][c]{GR} 
\psfrag{9}[c][c]{GT} 
\psfrag{12}[c][c]{HO} 
\psfrag{16}[c][c]{RH} 
\psfrag{17}[c][c]{PH} 
\psfrag{19}[c][c]{RB} 
\psfrag{20}[c][c]{KR} 
\psfrag{25}[c][c]{HS}
\psfrag{28}[c][c]{CA}
\psfrag{29}[c][c]{DK}
\psfrag{32}[c][c]{AC}
\psfrag{57}[c][c]{FH}
\psfrag{58}[c][c]{AR}
\psfrag{65}[c][c]{TU}
\psfrag{69}[c][c]{FT}
\psfrag{88}[c][c]{TI}
\psfrag{152}[c][c]{SK}
\psfrag{168}[c][c]{CK}
\psfrag{169}[c][c]{KA}
\psfrag{207}[c][c]{MI}
\psfrag{248}[c][c]{SF}
\psfrag{332}[c][c]{CD}
\psfrag{353}[c][c]{PI}
\psfrag{kinase}[l][l]{\green{Protein kinase}}
\psfrag{myosin}[l][l]{\blue{Myosin heavy chain}}
\psfrag{membrane}[l][l]{\blue{Membrane protein}}
\psfrag{prion}[l][l]{\blue{Prion-like Q/N rich domain}}
\leavevmode
\epsfig{file=./dip3.eps, width=5.5in}
\end{psfrags}
}

%Slide
\stitle{Runtime Characteristics}

\begin{center}
\blue{Comparison with isomorphism-based algorithms}\\
%\begin{small}
\begin{scriptsize}
\begin{tabular}{lccccccccc}
\hline
        &             & \hspace{0.3cm} & \multicolumn{3}{c}{FSG} & \hspace{0.3cm} & \multicolumn{3}{c}{{\sc Mule}}\\
        & Minimum    &  & Runtime & Largest & Number of & & Runtime & Largest & Number of \\
Dataset & Support (\%)& & (secs.) & pattern & patterns  & & (secs.) & pattern & patterns \\
\hline
 & 20 & & 0.2 & 9 & 12 & & 0.01 & 9 & 12\\
 & 16 & & 0.7 & 10 & 14 & & 0.01 & 10 & 14\\
 Glutamate & 12 & & 5.1 & 13 & 39 & & 0.10 & 13 & 39\\
 & 10 & & 22.7 & 16 & 34 & & 0.29 & 15 & 34\\ 
 & 8 & & 138.9 & 16 & 56 & & 0.99 & 15 & 56\\
\hline
 & 24 & & 0.1 & 8 & 11 & & 0.01 & 8 & 11\\
 & 20 & & 1.5 & 11 & 15 & & 0.02 & 11 & 15\\
 Alanine & 16 & & 4.0 & 12 & 21 & & 0.06 & 12 & 21\\
 & 12 & & 112.7 & 17 & 25 & & 1.06 & 16 & 25 \\
 & 10 & & 215.1 & 17 & 34 & & 1.72 & 16 & 34\\
\hline 
\end{tabular}
%\end{small}
\end{scriptsize}
\end{center}

\center{
\blue{Extraction of contracted patters}
\begin{scriptsize}
\begin{tabular}{cccccccccc}
\hline
\multicolumn{4}{c}{{Glutamate metabolism, $\sigma=8\%$}} &\hspace{0.7cm} &\multicolumn{4}{c}{{Alanine metabolism, $\sigma=10\%$}}\\
Size of & \multicolumn{2}{c}{Extraction time} & Size of & & Size of & \multicolumn{2}{c}{Extraction time} & Size of \\
contracted &  \multicolumn{2}{c}{(secs.)} & extracted  & & contracted &  \multicolumn{2}{c}{(secs.)}  & extracted\\
pattern    & FSG & gSpan & pattern & & pattern & FSG & gSpan & pattern\\
\hline
15 & 10.8 & 1.12 & 16 & & 16 & 54.1 & 10.13 & 17 \\
14 & 12.8 & 2.42 & 16 & & 16 & 24.1 & 3.92  & 16 \\
13 & 1.7  & 0.31 & 13 & & 12 & 0.9  & 0.27  & 12 \\
12 & 0.9  & 0.30 & 12 & & 11 & 0.4  & 0.13  & 11 \\
11 & 0.5  & 0.08 & 11 &  & 8 & 0.1  & 0.01  & 8  \\
\hline
\multicolumn{4}{l}{Total number of patterns: 56} & & \multicolumn{4}{l}{Total number of patterns: 34}\\
\multicolumn{4}{l}{Total runtime of FSG alone: 138.9 secs.} & & \multicolumn{4}{l}{Total runtime of FSG alone :215.1 secs.}\\
\multicolumn{4}{l}{Total runtime of {\sc Mule}+FSG: 0.99+100.5 secs.} & & \multicolumn{4}{l}{Total runtime of {\sc Mule}+FSG: 1.72+160.6 secs.}\\
\multicolumn{4}{l}{Total runtime of {\sc Mule}+gSpan: 0.99+16.8 secs.} & & \multicolumn{4}{l}{Total runtime of {\sc Mule}+gSpan: 1.72+31.0 secs.}\\
\hline
\end{tabular}
\end{scriptsize}}


%Slide
\stitle{Aligning Protein Interaction Networks}
[Koyuturk, Grama, Szpankowski, RECOMB04]

\begin{itemize}

\bigitem{Defining graph alignment is difficult in general}
{\item Biological meaning
 \item Mathematical modeling}

\bigitem{Existing algorithms are based on simplified formulations}
{\item PathBLAST aligns \blue{pathways} (linear chains) to render problem
       computationally  tractable
\item Motif search algorithms look for small \blue{topological motifs}, do
      not take into account conservation of proteins
} 

\bigitem{Our approach}
{\item Aligns \green{subsets of proteins} based on the observation
       that modules and complexes are conserved
 \item Guided by models of evolution
}  

\end{itemize}

%Slide
\stitle{Evolution of Protein Interaction Networks}

\begin{itemize}
\bigitem{\blue{Duplication}/\green{divergence} models for the evolution
of protein interaction networks}
{\item \blue{Interactions of duplicated proteins are also duplicated}
 \item \green{Duplicated proteins rapidly lose interactions through mutations}
}
\item This provides us with a simplified basis for solving a very hard
      problem
\end{itemize}

\vspace{0.6in}
\centerline{
\begin{psfrags}
\psfrag{u1}[c][c]{$u_1$}
\psfrag{u1p}[c][c]{$u_1'$}
\psfrag{u2}[c][c]{$u_2$}
\psfrag{u3}[c][c]{$u_3$}
\psfrag{Duplication}[c][c]{\blue{Duplication}}
\psfrag{Deletion}[c][c]{\green{Deletion}}
\psfrag{Insertion}[c][c]{\green{Insertion}}
\leavevmode
\epsfig{file=evolution.eps,width=8in}
\end{psfrags}}

%Slide
\stitle{Aligning Protein Interaction Networks: \blue{Input}}

\begin{itemize}
\item PPI networks $G(U,E)$ and $H(V,F)$
\bigitem{Sparse similarity function $S(u,v)$ for all $u,v \in U \cup V$} 
 {\item If $S(u,v)>0$, $u$ and $v$ are \green{homologous}}
\end{itemize}

\vspace{0.4in}
\centerline{
\begin{psfrags}
\small
\psfrag{u1}[c][c]{$u_1$}
\psfrag{u2}[c][c]{$u_2$}
\psfrag{u3}[c][c]{$u_3$}
\psfrag{u4}[c][c]{$u_4$}
\psfrag{v1}[c][c]{$v_1$}
\psfrag{v2}[c][c]{$v_2$}
\psfrag{v3}[c][c]{$v_3$}
\psfrag{v4}[c][c]{$v_4$}
\leavevmode
\epsfig{file=instance.eps, width=5in}
\end{psfrags}
}
\centerline{$G$ \hspace{2.5in} $H$}


%Slide
\stitle{Local Alignment Induced by Subsets of Proteins}

\begin{itemize}
\bigitem{\blue{Alignment} induced by \blue{protein subset pair} 
$P=\{\tilde{U}\in U ,\tilde{V}\in V\}$: 
$\cal{A}(P)=\{\cal{M},\cal{N},\cal{D}\}$}
{\item A \green{match} $\in \M$ corresponds to two pairs of homolog proteins from each 
       protein subset 
       such that both pairs interact in both PPI networks. A match is associated with 
       \green{score} $\mu$.
\item A \red{mismatch} $\in \N$ corresponds to two pairs of homolog proteins from each 
      PPI network such that
      only one pair is interacting. A mismatch is associated with 
       \red{penalty} $\nu$. 
\item A \red{duplication} $\in D$ corresponds to a pair of homolog proteins that are 
      in the
      same protein subset. A duplication is associated with \red{penalty} $\delta$.  
}
\end{itemize}

\centerline{
\begin{psfrags}
%\small
\psfrag{u1}[c][c]{$u_1$}
\psfrag{u2}[c][c]{$u_2$}
\psfrag{u3}[c][c]{$u_3$}
\psfrag{u4}[c][c]{$u_4$}
\psfrag{v1}[c][c]{$v_1$}
\psfrag{v2}[c][c]{$v_2$}
\psfrag{v3}[c][c]{$v_3$}
\psfrag{e1}[c][c]{$e_1$}
\psfrag{e2}[c][c]{$e_2$}
\psfrag{e3}[c][c]{$e_3$}
\psfrag{G}[c][c]{$G$:}
\psfrag{H}[c][c]{$H$:}
\leavevmode
\epsfig{file=alignment.eps, width=5in}
\end{psfrags}
}
\centerline{\small{Alignment induced by protein subset pair}} 
\centerline{\small{$\{\{u_1,u_2,u_3,u_4\},\{v_1,v_2,v_3\}\}$}}

%Slide
\stitle{Pairwise Local Alignment of PPI networks}

\begin{itemize}
\bigitem {\blue{Alignment score:}\\
$\sigma(\A(P))= \sum_{M \in \M}{\mu(M)} - \sum_{N \in \N}{\nu(N)}
           -\sum_{D \in \D}{\delta(D)}$}
{ \item Matches are rewarded for conservation of interactions
 \item Duplications are penalized for differentiation after split
 \item Mismatches are penalized for divergence and experimental error
}

\item All scores and penalties are functions of similarity between
      associated proteins

\bigitem{\green{Problem:} Find all protein subset pairs with alignment score
larger than a certain threshold.}
{\item High scoring protein subsets are likely to correspond to conserved
       modules or complexes}

\item A graph equivalent to BLAST
\end{itemize}


%Slide
\stitle{Weighted Alignment Graph $\mathbf{G}(\mathbf{V},\mathbf{E})$}

\begin{itemize}
\item $\mathbf{V}$ consists all pairs of homolog proteins $\mathbf{v}=\{u\in U, v \in V\}$
\bigitem{An edge $\mathbf{vv'}=\{uv\}\{u'v'\}$ in $\mathbf{E}$ is a}
{\item \green{match edge} if $uu' \in E$ and $vv' \in V$, with weight 
       $w(\mathbf{vv'})=\mu(uv,u'v')$
 \item \red{mismatch edge} if $uu' \in E$ and $vv' \notin V$ or vice versa, with weight 
       $w(\mathbf{vv'})=-\nu(uv,u'v')$
 \item \red{duplication edge} if $S(u,u') > 0$ or $S(v,v')>0$, with weight 
       $w(\mathbf{vv'})=-\delta(u,u')$ or $w(\mathbf{vv'})=-\delta(v,v')$
}

\end{itemize}

\vspace{0.3in}
\centerline{
\begin{psfrags}
\small
\psfrag{u1v1}[r][r]{$\{u_1,v_1\}$}
\psfrag{u2v1}[l][l]{$\{u_2,v_1\}$}
\psfrag{u3v3}[c][c]{$\{u_3,v_3\}$}
\psfrag{u4v2}[l][l]{$\{u_4,v_2\}$}
\psfrag{u4v4}[r][r]{$\{u_4,v_4\}$}
\psfrag{m}[c][c]{$\mu$}
\psfrag{n}[c][c]{-$\nu$}
\psfrag{d}[c][c]{-$\delta$}
\leavevmode
\epsfig{file=product.eps, height=3in}
\end{psfrags}
}

%Slide
\stitle{Maximum Weight Induced Subgraph Problem}

\begin{itemize}
\bigitem{\blue{Definition:} (\green{{\sc MaWISh}})}
{\item Given graph $\G(\V,\E)$ and a constant $\epsilon$, 
find $\tilde{\V} \in \V$ such that 
$\sum_{\vv,\uu \in \tilde{\V}}{w(\vv\uu)} \geq \epsilon$.
 \item NP-complete
}

\bigitem{\blue{Theorem:} (\green{{\sc MaWISh} $\equiv$ Pairwise alignment})} 
{\item If $\tilde{\V}$ is a solution for the {\sc MaWISh} problem on $\G(\V,\E)$, 
then $P=\{\tilde{U}, \tilde{V}\}$ induces an alignment 
$\A(P)$ with $\sigma(\A) \geq \epsilon$ , where 
$\tilde{\V} = \tilde{U} \times \tilde{V}$.}

\end{itemize}

\vspace{0.3in}
\centerline{
\begin{psfrags}
\small
\psfrag{u1v1}[r][r]{$\{u_1,v_1\}$}
\psfrag{u2v1}[l][l]{$\{u_2,v_1\}$}
\psfrag{u3v3}[c][c]{$\{u_3,v_3\}$}
\psfrag{u4v2}[l][l]{$\{u_4,v_2\}$}
\psfrag{u4v4}[r][r]{$\{u_4,v_4\}$}
\psfrag{m}[c][c]{$\mu$}
\psfrag{n}[c][c]{-$\nu$}
\psfrag{d}[c][c]{-$\delta$}
\leavevmode
\epsfig{file=wsub.eps, height=3in}
\end{psfrags}
}

%Slide
\stitle{A Greedy Algorithm for {\sc MaWISh}}

\begin{itemize}
\bigitem{\blue{Greedy graph growing}}
{\item Start with a \green{heavily connected} node, put it in $\tilde{\V}$
 \item Choose $\vv$ that is most heavily connected to $\tilde{\V}$
       and put it in $\tilde{\V}$ until no $\vv$ is \green{positively 
       connected} to $\tilde{\V}$.
 \item If total weight of the subgraph induced by $\tilde{\V}$ is greater
       than a threshold, return $\tilde{\V}$
 \item Works in linear time.
}

\item As modules and complexes are \green{densely connected} within the module
      and loosely connected to the rest of the network, this algorithm is
      expected to be effective.

\item For all local alignments, remove discovered subgraph and run the greedy 
      algorithm again.

\item If the number of homologs for each protein is constant, construction
      of alignment graph and solution of the {\sc MaWISh} takes $O(|E|+|F|)$ time.       

\end{itemize}

%Slide
\stitle{Scoring Matches, Mismatches and Duplications}

\begin{itemize}
\bigitem{Quantizing similarity between two proteins}
{ \item \blue{Confidence} in two proteins being \blue{orthologous} 
        (\blue{paralogous})
  \item BLAST E-value: $S(u,v) = log_{10}\frac{p(u,v)}{p_{random}}$
  \item Ortholog clustering: $S(u,v)=c(u)c(v)$
}

\bigitem{\green{Match score}}
{\item $\mu(uu',vv') = \bar{\mu} \min\{S(u,v),S(u',v')\}$}

\bigitem{\red{Mismatch penalty}}
{\item $\nu(uu',vv') = \bar{\nu} \min\{S(u,v),S(u',v')\}$}

\bigitem{\red{Duplication penalty}}
{\item $\delta(u,u') = \bar{\delta} (d-S(u,u'))$}
\end{itemize}

%Slide
\stitle{Alignment of Human and Mouse PPI Networks}

\vspace{0.8in}
\begin{small}
\begin{psfrags}
\psfrag{252}[c][c]{BMRB}
\psfrag{520}[c][c]{AVR2}
\psfrag{5793}[c][c]{ALK3}
\psfrag{5795}[c][c]{BMP4}
\psfrag{5796}[c][c]{BMRA}
\psfrag{5797}[c][c]{BMP6}
\psfrag{5798}[c][c]{AVR1}
\psfrag{5799}[c][c]{AVRB}
\psfrag{5800}[c][c]{BMP7}
\psfrag{5823}[c][c]{GDF5}
\centerline{\sl \blue{Homo Sapiens}}
\vspace{0.1in}
\centerline{\epsfig{file=Hsapi2.eps, width=8in}}
\vspace{0.5in}
\centerline{\sl \green{Mus Musculus}}
\vspace{0.1in}
\centerline{\epsfig{file=Mmusc2.eps, width=8in}}
\end{psfrags}
\end{small}
\vspace{0.5in}
\centerline{\red{A conserved subnet that is part of}}
\centerline{\red{transforming growth factor
beta receptor signaling pathway}}

%Slide
\stitle{Alignment of Yeast and Fly PPI Networks}

\vspace{0.8in}
\begin{small}
\begin{psfrags}
\psfrag{2262}[c][c]{HS82}
\psfrag{1524}[c][c]{HS83}
\psfrag{2329}[c][c]{STI1}
\psfrag{1271}[c][c]{CNS1}
\psfrag{17366}[c][c]{HS83}
\psfrag{19288}[c][c]{CG2720-PA}
\psfrag{20630}[c][c]{CG5393-PB}
\centerline{\sl \blue{Saccharomyces Cerevisiae}}
\vspace{0.1in}
\centerline{\epsfig{file=Scere5.eps, width=2.5in}}
\vspace{0.5in}
\centerline{\sl \green{Drosophila Melanogaster}}
\vspace{0.1in}
\centerline{\epsfig{file=Dmela5.eps, width=2.5in}}
\end{psfrags}
\end{small}
\vspace{0.5in}
\centerline{\red{A conserved subnet that is part of}}
\centerline{\red{response to stress}}



%Slide
\stitle{Ongoing Work on PPI Network Alignment}

\begin{itemize}
\bigitem{Assessing statistical significance}
{\item Constructing a refence model based on models of evolution}

\bigitem{BLAST-like search queries for network alignment}
{\item Given a query graph, find all high-scoring local alignments in a
       database of PPI networks}

\bigitem{Multiple Graph Alignment (CLUSTAL, BLASTCLUST)}
{\item How to combine graph mining and pairwise alignment}


\end{itemize}

%Slide
\stitle{Inferring Functional Modules from Phylogenetic Information}
[Kim, Koyuturk, Topkara, Grama, Subramaniam, ECCB05 (submitted)]

\begin{itemize}
\bigitem{Functionally related proteins are likely to have co-evolved}
{
\item Construct \blue{phylogenetic profile} for each genome: Vector of E-values
signifying existence of an orthologous protein in each organism
\item Identify \blue{pairwise functional association}s based on mutual 
information between phylogenetic profiles [Pellegrini et al. (1999)]
\item \green{Mutual information:}\\
$I(X,Y)= H(X)-H(X|Y) = \sum_{x}\sum_{y} p(x,y) \log(p(x,y)/p(x)p(y))$
\item Shown to identify functionally associated protein pairs at
a coarser level than high-throughput methods
}

\bigitem{However, \green{domains}, \red{not proteins}, co-evolve}
{
\item How can we incorporate domain information to enhance performance
of phylogeny-based interaction prediction?
}

\end{itemize}

%Slide 
\stitle{Identification of Co-evolved Domains}

\begin{itemize}

\item{While sequence information is widely available, 
domain information is not generally comprehensive}

\bigitem{Approximating domains between \blue{fixed-size} segments 
[Kim \& Subramaniam (2004)]}
{
\item Chop proteins into overlapping ({\em e.g.}, 30 b.p.) 
fixed-size ({\em e.g.}, 120 b.p.) segments 
\item Construct phylogenetic profile for each segment, find 
maximum-mutual-information segment pair for each protein pair 
\item Improves single-profile based approach
\item However, there is no fixed domain size
}

\bigitem{Can we \blue{identify} domains from phylogenetic information as well?}
{
\item \green{Residue phylogenetic profiles}!
}

\end{itemize}

%Slide
\stitle{Residue-Level Phylogenetic Analysis}

\begin{itemize}

\bigitem{\green{Residue phylogenetic profile}}
{
\item For each residue $r_{ij}$ on protein $P_i$, the existence of $r_{ij}$ in 
genome $G_k$ is signified by the minimum e-value of alignments between $P_i$ and 
$G_k$ that contain $r_{ij}$
}
\bigitem{\blue{Mutual information matrix}}
{
\item Matrix of mutual information between any pair of residues each from one protein 
\item $M(P_i, P_j)=[m_{kl}]$,\\
   where $m_{kl}=I(profile(r_{ik}), profile(r_{jl}))$
}

\end{itemize}

%Slide
\stitle{Mutual Information Matrix}

\centerline{\epsfig{file=miMatrixChemotaxis.eps,width=6in}}

\centerline{\blue{Mutual information matrix} for proteins \green{CheA} and \green{CheB}
 in E-coli.}
\centerline{Darker pixels indicate higher mutual information.}
\vspace{0.5in}

\centerline{\red{Co-evolved domains identified by dark rectangles!}}

%Slide
\stitle{Clustering Residue Phylogenetic Profiles}

\begin{itemize}

\item{Cluster residues to identify co-evolved domains [Kim et al., 2005]}

\bigitem{For each protein pair}
{
\item Downsample residues of each protein (for computational efficiency)
\item Construct residue phylogenetic profiles
\item Compute mutual information matrix
\item Identify \blue{sufficiently large contiguous rectangles} on mutual information 
      matrix with \green{consistently high mutual-information}
\item Set \red{phylogenetic association score} of the two proteins to the
      maximum of mutual information of such rectangles
}
\item{Can be used for \blue{domain identification} as well!}
\end{itemize}

%Slide
\stitle{Comparison of Domain-Profile and Single-Profile Methods}

\centerline{\epsfig{file=miDistributions.eps,width=6in}}
\centerline{\blue{Distribution of  Mutual Information}}

\centerline{Two proteins are considered \green{functionally associated}}
\centerline{if they co-occur in a metabolic pathway derived from KEGG}

%Slide
\stitle{Comparison of Domain-Profile and Single-Profile Methods}

\centerline{\epsfig{file=stringAccuCov.eps,width=6in}}
\centerline{\blue{Accuracy vs Coverage}}

\centerline{\green{Accuracy:} Fraction of true-positives among}
\centerline{ all predicted functional associations}
\centerline{\red{Coverage:} Number of functionally associated}
\centerline{protein pairs that are identified by the algorithm}

%Slide
\stitle{Augmenting PPI Networks with Phylogenetic Information
to Enhance Module Identification}

\begin{itemize}
\bigitem{Density-based clustering of PPI networks is commonly used 
to identify modules}
{
\item \blue{MCODE:} Greedy graph growing based on local neighborhood density of
each protein
\item \red{MCL:} Markovian clustering based on random walks on PPI network
\item \green{MCS:} Recursive min-cut partitioning
}

\bigitem{Information derived from PPI networks is not comprehensive}
{
\item High-throughput methods are prune to false-negatives and even 
false-positives
\item Available data is generated for target functional units in cell
({\em e.g.}, fly network mostly contains signaling information)
}
\end{itemize}

%Slide
\stitle{Functional Modules and Phylogeny}

\vspace{1in}
\centerline{\epsfig{file=repl.eps,width=3.5in}\hspace{0.5in}\epsfig{file=replprf.eps,width=6in}}

\vspace{0.5in}
\centerline{\blue{Replication Factor C} complex identified on yeast} 
\centerline{PPI network by \green{MCODE} algorithm and the \green{phylogenetic}}
\centerline{\green{profiles} of its proteins on 25 eukaryotic genomes}

\vspace{1in}
\centerline{\red{Conserved in all eukaryotic species}!}

%Slide
\stitle{Functional Modules and Phylogeny}

\vspace{1in}
\centerline{\epsfig{file=ribo.eps,width=3.5in}\hspace{0.5in}\epsfig{file=riboprf.eps,width=6in}}

\vspace{0.5in}
\centerline{A component of \blue{mitochondrial ribosome} identified on}
\centerline{yeast PPI network by \green{MCODE} algorithm and the}
\centerline{ \green{phylogenetic profiles} of its proteins on 25 eukaryotic genomes}

\vspace{1in}
\centerline{\red{Conserved in only yeast species}!}

%Slide
\stitle{Superposing PPI Networks with Phylogenically Predicted Networks}

\vspace{0.5in}
\centerline{\epsfig{file=dip.eps,width=3in}\hspace{0.5in}
            \epsfig{file=mutinf.eps,width=3in}\hspace{0.5in}
            \epsfig{file=join.eps,width=3in}}
\vspace{0.5in}
\centerline{\green{Protein Interaction}\hspace{0.5in}
            \blue{Phylogenetic Association}\hspace{0.5in}
            \red{Superposition of PPI and}}
\centerline{\green{(PPI) network}\hspace{1.4in}
            \blue{(PGA) network}\hspace{1.4in}
            \red{PGA networks}}
\vspace{1in}

\centerline{Cluster the superposed network!}

%Slide
\stitle{Clustering The Superposed Network}

\begin{itemize}
\bigitem{MCODE discovers}
{ \item 15 modules on E-coli \green{PPI} network
  \item 11 modules on E-coli \blue{PGA} network constructed by domain-profile method
  \item 26 modules on \red{PPI $\cup$ PGA}
}
\end{itemize}

\begin{scriptsize}
\begin{center}
\begin{tabular}{ccc}
\hline
\green{PPI} & \blue{PGA} &  \red{PPI $\cup$ PGA}\\
\hline
4 proteins &  4 proteins &     9 proteins \\
9 interactions & 6 phylogenetic associations & 21 functional associations \\
\hline
molybdopterin biosynthesis & molybdopterin biosynthesis & molybdopterin biosynthesis\\
molybdochetalase & & molybdochetalase \\
MGD biosynthesis B & & MGD biosynthesis B\\
molybdopterin $\rightarrow$ MGD & & molybdopterin $\rightarrow$ MGD\\
 & molybdopterin biosynthesis A & molybdopterin biosynthesis A\\
 & molybdopterin biosynthesis C & molybdopterin biosynthesis C\\
 & DMSO reductase & DMSO reductase \\
 & & molybdopterin biosynthesis B \\
 & & anaerobic DMSO reductase\\
\hline
\end{tabular}
\end{center}
\end{scriptsize}
\begin{small}
\vspace{0.5in}
\centerline{A module of the molybdopterin biosynthesis pathway in Ecoli}
\centerline{that can only be partially discovered on PPI and PGA networks}
\centerline{is comprehensively identified on the augmented network}
\end{small}

%Slide
\stitle{Building a Comprehensive Signaling Database}


\noindent
There are four major components to any such effort:
\begin{itemize}
{
\item
The availability of up-to-date, curated/annotated signaling data
(The Biology WorkBench provides us with an excellent starting point.
ITaP is in the process of mirroring the WorkBench at Purdue.
\item
Developing commonly accepted (flexible, extensible) data standards.
These do not exist in the signaling community at this point, although,
SBML addresses a closely related community.
\item
Developing analysis techniques -- we are one of the leading groups
in this area.
\item
Developing interfaces and middleware -- this presents a significant
opportunity for development.
}
\end{itemize}


\stitle{Building a Comprehensive Signaling Database}

Any such effort must:

\begin{itemize}
{
\item
Interoperate with other existing signaling databases in terms of
data formats, APIs for services, and standardized output formats.
\item
Interoperate with existing genotype and phenotype tools and databases.
\item
Provide support for building complex tools that build on a variety
of existing data sources and APIs.
}
\end{itemize}

\stitle{Using a Signaling Database: An Example (1)}

Consider the problem of predicting protein interactions using 
phylogeny data. The algorithm builds on the following sources:

\begin{itemize}
{
\item
Sequence data for generating phylogenetic profiles.
\item
BLAST for finding matches (populating the phylogenetic profiles).
\item
In-house analyses on generating phylogeny vectors and inferring
interactions.
\item
DIP for validating results.
}
\end{itemize}

Our current study downloads all the data and analysis tools (including
BLAST) and performs analyses locally. This is extremely cumbersome and
inaccurate, since databases are always in a state of flux.

\stitle{Using a Signaling Database: An Example (2)}

Consider the problem of detecting modules in protein interaction
networks using interaction and phylogeny data.
The algorithm builds on the following sources:

\begin{itemize}
{
\item
Sequence data and BLAST for generating profile based hyper-edges.
\item
DIP for protein interactions.
\item
In-house code for analysis and augmentation of interaction networks.
\item
MCODE/MCL/MCS for graph clustering.
\item
A variety of online sources for validation.
}
\end{itemize}

As before, even simple analyses tasks can be extremely data, effort,
and time intensive.


\stitle{Building a Comprehensive Signaling Database: Challenges}

\begin{itemize}
{
\item
Interfaces for data and API conversion.
\item
Analysis modules for interaction data (harden existing analyses tools
to production quality, develop new tools).
\item
Tools to enable submission of other tools to the infrastructure
(controlled vocabularies, ontologies, etc.).
\item
Service discovery service.
\item
Support for building applications (composing applications, type-checking,
consistency).
\item
Runtime system for RPC, debugging, and visualization.
}
\end{itemize}


\end{document}
