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\title[Computational Aging] % (optional, use only with long paper titles)
{A Systems Study of Aging and Age-Related Pathologies}


\author[Grama et al.] % (optional, use only with lots of authors)
{Ananth Grama}

\institute[Purdue University] % (optional, but mostly needed)
{
  Center for Science of Information\\
  Purdue University
}

\date[EAC 2013] % (optional, should be abbreviation of conference name)
{EAC, Chicago 2013}


\subject{Computational Biology}


\pgfdeclareimage[height=0.5cm]{university-logo}{figures/purdue}
\logo{\pgfuseimage{university-logo}}


\AtBeginSubsection[]
{
  \begin{frame}<beamer>{Outline}
    \tableofcontents[currentsection,currentsubsection]
  \end{frame}
}

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\begin{document}

\begin{frame}
  \titlepage
\end{frame}

\begin{frame}
\vspace*{-0.3in}
\begin{center}
        \includegraphics[height=2.7in,angle=-90]{all.pdf}
\end{center}
\end{frame}

\begin{frame}{Outline}
  \tableofcontents
\end{frame}



\section{Phase 1: Constructing a map of aging pathways in yeast}
\subsection{Overview}
\begin{frame}{Yeast Aging}
  \begin{columns}[c]
    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/bacterial-growth}\\
		\tiny{Courtesy of Alper Uzan, PhD.}
    \end{center}
	  
    \column{.6\textwidth}
		\begin{itemize}
			\item Yeast as a model organism for aging research:
			\begin{itemize} [label={\checkmark}]
				\item Rapid growth
				\item Ease of manipulation
			\end{itemize}			
			\item \alert{Replicative life-span (RLS): } the number of buds a mother cell can produce before senescence occurs
			\item \alert{Chronological life-span (CLS): } duration of viability 
after entering the stationary-phase
		\end{itemize}
  \end{columns}  

\end{frame}





\subsection{Materials and Methods}
\subsubsection{Datasets}
\begin{frame}{Yeast Interactome}
  \begin{columns}[c]
    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=.8\columnwidth]{figures/Integration}\\
		\includegraphics[width=\columnwidth]{figures/Interactome}\\
    \end{center}
	  
    \column{.6\textwidth}
		\begin{itemize}
		\item \textit{Mixed network: } Contains both directed (biochemical activities) and undirected (protein-protein interactions) edges
		\item 103,619 (63,395 non-redundant) physical interactions among 5,691 proteins.
		\item 5,791 (5,443 non-redundant) biochemical activities (mostly phosphorylation events) among 2,002 kinase-substrate pairs.
	\end{itemize}
  \end{columns}  
\end{frame}


\begin{frame}{Transcriptional regulatory network (TRN) of yeast}
	\begin{itemize}
		\item Directed graph
		\item Downloaded from the Yeast Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT)
		\item Consists of 48,082 interactions between 183 transcription factors (TF) and 6,403
target genes (TG).	
	\end{itemize}
\end{frame}


\begin{frame}{Rapamycin-treatment dataset}
  \begin{columns}[c]
    \column{.25\textwidth}
	\includegraphics[height=.8\textheight]{figures/Rapamycin}\\
	\tiny{Adopted from Fournier et al., 2010}
	  
    \column{.75\textwidth}
		\begin{itemize}
		\item \textbf{Rapamycin}: A lipophilic macrolide that directly binds to and inhibits TOR \textit{in vivo}
		\item Temporal analysis of gene expression changes for 6,000 ORFs in Baker's yeast, \textit{Saccharomyces cerevisiae}, over 6h of rapamycin treatment.
		\item 366 repressed and 291 induced genes at a minimum threshold of 2-fold change.
	\end{itemize}
  \end{columns}  
\end{frame}





\subsubsection{Tracing Information Flow}

\begin{frame}{Directional information flow}{Random walk}
	\begin{definition}
	\alert{Random walk} on a graph $G$, initiated from vertex $v$, is the sequence of transitions among vertices, starting from $v$. At each step, the random walker randomly chooses the next vertex from among the neighbors of the current node.
	\end{definition}

It is a Markov chain with the transition matrix $P$, where $p_{ij} = Prob(S_{n+1} = v_i|S_{n} = v_j)$ and random variable $S_n$ represents the state of the random walk at the time step $n$. 
\end{frame}


\begin{frame}{Directional information flow}{Random walk with restart}

	\begin{definition}
	\alert{Random walk with restart (RWR)} is a modified Markov chain in which, at each step, a random walker has the choice of either continuing along its path, with probability $\alpha$, or jump (teleport) back to the initial vertex, with probability $1-\alpha$.
	\end{definition}
The transition matrix of the modified chain, $M$, can be computed as $M = \alpha P + (1-\alpha) \e_v \one^T$, where $\e_v$ is a stochastic vector of size $n$ having zeros everywhere, except at index $v$, and $\one$ is 
a vector of all ones.
\end{frame}


\begin{frame}{Directional information flow}{Stationary distribution}
The portion of time spent on each node in an infinite random walk with restart 
initiated at node $v$, with parameter $\alpha$.

	\begin{definition}
	\alert{Stationary distribution} of the modified chain
		\begin{eqnarray*}
			\PI_v(\alpha) &=& M \PI_v(\alpha) \nonumber \\
			&=& (\alpha P + (1-\alpha) \e_v \one^T) \PI_v(\alpha)
		\end{eqnarray*}
	\end{definition}

Enforcing a unit norm on the dominant eigenvector to ensure its stochastic property, 
$\parallel \PI_v(\alpha) \parallel_1 = \one^T\PI_v = 1$, we will have:
\end{frame}


\begin{frame}{Directional information flow}{Stationary distribution--continue}
	\begin{definition}
	\alert{Iterative form} of the information flow process:
		\begin{eqnarray*}
			\PI_v(\alpha) &=& \alpha P \PI_v(\alpha) + (1-\alpha) \e_v,
		\end{eqnarray*}
	\end{definition}

	\begin{definition}
	\alert{Explicit (direct) formulation} of the information flow process:
		\begin{eqnarray*}
			\PI_v(\alpha) &=& \underbrace{(1-\alpha)(I - \alpha P)^{-1}}_{Q} \e_v,
		\end{eqnarray*}
	\end{definition}
\end{frame}

\begin{frame}{Directional information flow}{Interpretation}

	\begin{definition}
		Expansion using the Neumann series:		
		\begin{eqnarray*}
			\PI_v(\alpha) &=& (1-\alpha) \sum_{i=0}^{\infty} (\alpha P)^i \e_v
		\end{eqnarray*}
	\end{definition}
	
	Thus, $\PI_v(\alpha)$ is a function of:
	\begin{itemize}
		\item Distance to source node ($v$)
		\item Multiplicity of paths
	\end{itemize}
\end{frame}



\begin{frame} {Sidebar: Functional PageRank (PR)}
\small{ 
{\bf Computing PageRank (PR)}
 \begin{itemize}
\item PageRank as a \emph{random surfer process}: Start surfing from a random node and keep following links with probability $\mu$ restarting  with probability $1 - \mu$; the node for restarting will be selected based on a personalization vector $v$. The ranking value $x_i$ of a node $i$ is the probability of visiting this node during surfing.
\item PR can also be cast in power series representation as
$x =  (1-\mu) \sum_{j=0}^k \mu^{j} S^j v$; $S$ encodes column-stochastic adjacencies.
  \end{itemize}
}

\small{
{\bf Functional rankings} 
 \begin{itemize}
 \item A general method to assign ranking values to graph nodes as $x =  \sum_{j=0}^k \zeta_j S^j v$. PR is a functional ranking, 
$\zeta_j = (1-\mu) \mu^j$.
\item Terms attenuated by outdegrees in $S$ \emph{and} damping coefficients $\zeta_j$.
  \end{itemize}
}
\end{frame}




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
  \frametitle{Functional Rankings Through Multidamping [Kollias, Gallopoulos, AG, TKDE'13]}
%\textbf{Q:} Is there a way to encode functional rankings as surfing processes?\\
%\textbf{A: Multidamping}
\small{
  \begin{columns}[t]    
    \begin{column}{4.cm}
      \begin{figure}[tbh]
        \centering
        \includegraphics[scale=0.5]{figures/montecarlo.pdf}
      \end{figure}
    \end{column}
   
    \begin{column}{7.2cm}
      \begin{block}{Computing $\mu_j$ in multidamping}
        Simulate a functional ranking by random surfers following emanating links with probability $\mu_j$ at step $j$ given by : \\
        
$\mu_{j} = 1  -\frac{1}{1+\frac{\rho_{k-j+1}}{1-\mu_{j-1}}}, j = 1, ..., k,$ \\
$\text{where } \mu_0 = 0$ and $\rho_{k-j+1} =
        \frac{\zeta_{k-j+1}}{\zeta_{k-j}}$
      \end{block}
    \end{column}
  \end{columns}
}

\footnotesize{
\textbf{Examples}\\
\emph{LinearRank (LR)}  
$ x^{\rm LR} =\sum_{j=0}^{k}  \frac{2(k+1-j)}{(k+1)(k+2)}  S^j v$ :
$\mu_j = \frac{j}{j+2}, j = 1, ..., k $.\\
\emph{TotalRank (TR)} 
$x^{\rm TR} = \sum_{j=0}^\infty \frac{1}{(j+1)(j+2)} S^j v$ :
$\mu_j = \frac{k-j+1}{k-j+2}, j =1, ..., k $.
}

\end{frame}
\begin{frame}
  \frametitle{Multidamping and Computational Cost}

{\bf Advantages of multidamping}

\begin{itemize}
\item Interpretability and Design!
\item Reduced computational cost in \emph{approximating} functional rankings using the Monte Carlo approach. A random surfer terminates with probability $1 - \mu_j$ at step $j$.
\item Inherently parallel and synchronization free computation.
\end{itemize}

\end{frame}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
  \frametitle{Multidamping Performance}
\footnotesize{ 
 \begin{columns}[t]    
    \begin{column}{4.cm}
      \begin{figure}[h]
        \includegraphics[width=1.0\textwidth]{figures/tau-TR-1000-crop.pdf}
      \end{figure}
    \end{column}
    \begin{column}{4.cm}
      \begin{figure}[h]
        \includegraphics[width=1.0\textwidth]{figures/shared-LR-20-crop.pdf}
        \end{figure}
      \end{column}
  \end{columns}

{\small
\textbf{Approximate ranking:}
  Run $n$ surfers to completion for graph size $n$. 
  How well does the computed ranking capture the ``reference'' ordering for
$\texttt{top-}k$ nodes, compared to standard iterations of equivalent computational
cost/number of operations? \emph{[Left]}\\

\textbf{Approximate personalized ranking:}
 Run less than $n$ surfers to completion (each called a microstep, x-axis), from a
 selected node (personalized). How well can we capture the ``reference'' $\texttt{top-}k$
 nodes, i.e., how many of them are shared (y-axis),
 compared to the simple approach? \emph{[Right]}
}\\
}
%\scriptsize{[\textsf{uk-2005}: $39,459,925$ nodes, $936,364,282$ edges. 
%\textsf{in-2004}: $1,382,908$ nodes, $16,917,053$ edges]}

\end{frame}





\subsection{Results and Discussion}

\begin{frame}{Experimental settings}
We set the \alert{preference vector} as:
\begin{equation*}
e_{\mathcal{S}}(i) = \begin{cases} \frac{1}{|\mathcal{S}|} & \text{if $v_i \in \mathcal{S}$,}
\\
0 &\text{O.W.}
\end{cases}
\end{equation*}


for $\mathcal{S}$ being the subset of vertices in the yeast interactome corresponding to members of the TORC1 protein complex. The diameter of the network is computed to be $6$ and \alert{$\alpha$ parameter} is set to $\frac{d}{d+1}=\frac{6}{7} \sim 0.85$ accordingly to give all nodes a fair chance of being visited.


\end{frame}


\begin{frame}{Distribution of information flow scores}
\scriptsize Distribution of information flow scores across nodes with similar distance from members of TORC1 are color coded accordingly. The $\mu$ parameter is the average of information flow scores for nodes under each distribution.
\begin{center}
	\includegraphics[height=.6\textheight]{figures/ScoreDistribution}\\
\end{center}
\end{frame}


\begin{frame}{Enrichment map of yeast GOslim terms}
\scriptsize Enriched terms are identified by mHG p-value, computed for the ranked-list of genes based on their information flow scores. Each node represents a significant GO term and edges represent the overlap between genesets of GO terms. Terms in different branches of GO are color-coded with red, green, and blue. Color intensity of each node represents the significance of its p-value, while the node size illustrates the size of its geneset. Thickness of edges is related to the extent of overlap among genesets.
\begin{center}
	\includegraphics[height=.6\textheight]{figures/EM}\\
\end{center}
\end{frame}



\begin{frame}{TOR-dependent control of transcription initiation}
\scriptsize Induced subgraph in the yeast interactome, constructed from the top-ranked genes in the information flow analysis that are annotated with the transcription initiation GO term. Different functional subunits are marked and color-coded appropriately.
\begin{center}
	\includegraphics[height=.6\textheight]{figures/TransInit}\\	
\end{center}
\end{frame}


\begin{frame}{Enrichment plot for Rapamycin-treatment dataset}
\scriptsize Enrichment score as a function of the score percentage. Computations are based on the set of differentially expressed genes in response to Rapamycin treatment. The peak of plot occurs at around
top 15\% of scores, resulting in the minimum hypergeometric (mHG) score  of $\sim 1e-22$. The exact p-value for this score is computed, using dynamic programing, to be $3.3e-19$.

\begin{center}
	\includegraphics[height=.55\textheight]{figures/EnrichmentPlot}\\	
\end{center}
\end{frame}


\begin{frame}{TORC1-dependent regulation of Gap1}
\scriptsize The schematic diagram is based on literature evidence for the interactions. Each node in the signaling pathway is annotated with the rank of its information flow score from TORC1. Ranking of nodes based on their information flow scores respect our prior knowledge on the structure of this pathway.
\begin{center}
	\includegraphics[height=.6\textheight]{figures/Gap1}\\	
\end{center}
\end{frame}




\section{Phase 2: Projecting yeast aging pathways to human tissues}
\subsection{Motivation}
\begin{frame}{Comparative Network Analysis}{Traditional approach}

\begin{block}{Basic Idea}
  \begin{columns}[c]
    \column{.35\textwidth}
	\includegraphics[width=\columnwidth]{figures/eur-vitruvian_man}

    \column{.1\textwidth}
	\centering \huge
	$\Longleftrightarrow$
			  
    \column{.35\textwidth}
	\includegraphics[width=\columnwidth]{figures/yeast_yellow}

  \end{columns}  
\end{block}
\centering
  To project functional pathways from a well-studied organism, such as yeast, back to a higher-order organism, such as humans.
\end{frame}


\begin{frame}{New outlook}{Tissue-specific analysis}
			Different human cell types inherit a similar genetic code, but exhibit unique characteristics and functions.
  \begin{columns}[c]
    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/tissue_types}\\
    \end{center}
	  
    \column{.6\textwidth}
		\begin{itemize}
			\item How does regulation of different genes contribute to functional differences in human tissues?
			\item How does a uni-cellular organism, such as yeast, contributes to the biological understanding of higher-order, multi-cellular organisms, such as humans?
		\end{itemize}
  \end{columns}  
\end{frame}


\subsection{Datasets}
\begin{frame}{Tissue-specific gene expression}
The GNF Gene Atlas dataset:
  \begin{columns}[c]
    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/BioGPS}\\
    \end{center}
	  
    \column{.6\textwidth}
		\begin{itemize}
			\item 79 different tissues
			\item 44,775 human transcripts
			\item Platforms:
			\begin{enumerate}
				\item Affymetrix HG-U133A.
				\item Custom GNF1H array.
			\end{enumerate}
		\end{itemize}
	\end{columns}		
\end{frame}


\begin{frame}{Tissue-specific interactomes}
	\begin{itemize}
		\item Vertex-induced sub-graphs of the human interactome
		\item Based on the GNF Gene Atlas dataset
		\begin{itemize}
			\item[$\Rightarrow$] A gene is considered as present in a tissue, if its normalized expression level is $> 200$ (average difference between match-mismatch pairs).
		\end{itemize}
	\end{itemize}
	
    \begin{center}
		\includegraphics[width=.8\textwidth]{figures/bossi}\\
		\tiny{Adopted from Bossi et al., 2009}
    \end{center}
\end{frame}



\begin{frame}{Sequence similarity of protein pairs}
\begin{itemize}
\item Protein sequences are downloaded from Ensembl database, release 69.
\item Reference genomes:
\begin{itemize}[label=$\triangleright$]
\item \textbf{Human}: GRCh37
\item \textbf{Yeast}: EF4
\end{itemize}
\item Number of sequences:
\begin{itemize}[label=$\triangleright$]
\item \textbf{Human}: 101,075
\item \textbf{Yeast}: 6,692
\end{itemize}
\item Low-complexity regions are masked using \textbf{pseg}
\item Smith-Waterman algorithm is used to compute local alignments.
\end{itemize}
\end{frame}


\subsection{Results}
\begin{frame}{Gene expression signature}{A unique identifier for similar tissues}
\scriptsize
\begin{itemize}
\item Each row corresponds to a unique ORF.
\item Each col corresponds to a unique tissue.
\item Coloring is based on the normalized expression of each gene across different tissues.
\end{itemize}
\begin{center}
\includegraphics[height=.6\textheight]{figures/clustergram}
\end{center}
\end{frame}


\begin{frame}{Tissue-Tissue Similarity Network (TTSN)}
\begin{itemize}
\item First of its kind
\item Based on similarity of expression signatures
\item Differentiates between similar and dissimilar tissues
\end{itemize}

\begin{center}
\includegraphics[height=.6\textheight]{figures/TTSN_stringent}\\
\tiny{\textit{TTSN with stringent threshold (1.96 < Z-score)}}
\end{center}
\end{frame}

\begin{frame}{Reconstructing the differentiation tree of immune cells}
  \begin{columns}[c]
    \column{.45\textwidth}
    \begin{center}
		\includegraphics[height = .75\textheight]{figures/Hematopoiesis}\\
		\tiny{Adopted from BioLegend}
    \end{center}

    \column{.55\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth,height = .75\textheight]{figures/ImmuneTree2}\\
    \end{center}
	\end{columns}		
\end{frame}


\begin{frame}{Similarity of different tissues to yeast}{Summary of methods}
	\begin{itemize}
		\item Align each tissue-specific network to yeast using \textit{Belief Propagation (BP)} algorithm.
		\item For each alignment, compute:
		\begin{itemize}
			\item Number of conserved edges.
			\item Sequence similarity of the aligned proteins.	
		\end{itemize}
		\item Create and ensemble of random, \textit{pseudo tissues} seeded around housekeeping proteins (proteins that are expressed in all tissues).
		\item Align random tissues with yeast and compute p-values of original alignment.
	\end{itemize}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
  \frametitle{Sidebar: Network Alignment}
\small{
  \begin{columns}[h]
    \begin{column}{3.8cm}
      \includegraphics[width=\textwidth]{figures/match-graphs.pdf}
    \end{column}
    \begin{column}{5.8cm}
      \begin{itemize}
      \item
        \textbf{Node similarity:} Two nodes are similar if they are linked by other similar node pairs. By pairing similar nodes, the two graphs become \emph{aligned}.
      \end{itemize}
    \end{column}
  \end{columns}
\begin{itemize}
\item
Let $\tilde{A}$ and $\tilde{B}$ be the normalized adjacency matrices of the graphs (normalized by columns),
$H_{ij}$ be the independently known similarity scores (preferences matrix) of nodes $i \in V_B$ and
$j \in V_A$, and $\mu$ be the fractional contribution of topological similarity.
\item To compute $X$, IsoRank iterates:
$$
X \leftarrow \mu \tilde{B} X {\tilde{A}}^{T} + (1-\mu) H
$$
\end{itemize}
}
\end{frame}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{Network Similarity Decomposition (NSD) 
[Kollias, Mohammadi, AG, TKDE'12]}
\small{
{\bf Network Similarity Decomposition (NSD)}
\begin{itemize}
\item
In $n$ steps of we reach
$X^{(n)} = (1-\mu) \sum_{k=0}^{n-1} \mu^{k} \tilde{B}^{k} H (\tilde{A}^{T})^{k} + \mu^{n} \tilde{B}^{n} H (\tilde{A}^{T})^{n}$ 
\item
Assume that $H=u v^T$ ($1$ component). Two phases for $X$:
\begin{enumerate}
\item
  $u^{(k)} = \tilde{B}^{k} u$ and $v^{(k)} = \tilde{A}^{k} v$ \emph{(preprocess/compute iterates)}
\item
  $X^{(n)} = (1-\mu) \sum_{k=0}^{n-1} \mu^{k} u^{(k)}{v^{(k)}}^{T} + \mu^{n}  u^{(n)}{v^{(n)}}^{T}$ \emph{(construct $X$)} 
\end{enumerate} 
This idea extends to $s$ components, $H \sim \sum_{i=1}^{s} w_i z_i^{T}$.
\item
NSD computes matrix-vector iterates and builds $X$ as a sum of outer products; these are much cheaper than triple matrix products.
\end{itemize} 

We can then apply Primal-Dual or Greedy Matching (1/2 approximation) to extract the actual node pairs.
}
\end{frame}



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{NSD: Performance 
[Kollias, Madan, Mohammadi, AG, BMC RN'12]}
{\tiny
\begin{columns}[c]
\begin{column}{4cm}
\begin{center}
\begin{tabular}[h]{lll}
\hline
\textbf{Species}	& \textbf{Nodes} & \textbf{Edges} \\ \hline
celeg (worm) 	& 2805	 & 4572 \\
dmela (fly) 	& 7518	 & 25830 \\
ecoli (bacterium)	& 1821	 & 6849 \\
hpylo (bacterium)	& 706	 & 1414 \\
hsapi (human)	& 9633	 & 36386 \\
mmusc (mouse)	& 290	 & 254 \\
scere (yeast) & 5499	 & 31898 \\
\end{tabular}
\end{center}
\end{column}

\begin{column}{7.2cm}
\begin{center}
\begin{tabular}[h]{|l|p{0.5cm}|p{0.6cm}|p{0.6cm}|p{0.6cm}|p{1cm}|}
\hline
\textbf{Species pair}	& \textbf{NSD (secs)} &   \textbf{PDM (secs)} & \textbf{GM (secs)} &  \textbf{IsoRank (secs)} \\ \hline
celeg-dmela &	 \textbf{3.15}           &    152.12      &   7.29      &	783.48  \\ \hline	
celeg-hsapi &    \textbf{3.28}           &    163.05      &   9.54      &	1209.28	\\ \hline
celeg-scere &    \textbf{1.97}           &    127.70      &   4.16      &	949.58	\\ \hline
dmela-ecoli &    \textbf{1.86}           &    86.80       &   4.78      &	807.93	\\ \hline
dmela-hsapi &    \textbf{8.61}           &    590.16      &   28.10     &	7840.00	\\ \hline
dmela-scere &    \textbf{4.79}           &    182.91      &   12.97     &	4905.00	\\ \hline
ecoli-hsapi &    \textbf{2.41}           &    79.23       &   4.76      &	2029.56	\\ \hline
ecoli-scere &    \textbf{1.49}           &    69.88       &   2.60      &	1264.24	\\ \hline
hsapi-scere &    \textbf{6.09}           &    181.17      &   15.56     &	6714.00	\\ \hline
\end{tabular}
\end{center}
\end{column}
\end{columns}
}
{\footnotesize
\begin{itemize}
\item
We compute similarity matrices $X$ for various pairs of species using Protein-Protein Interaction (PPI)
networks. $\mu = 0.80$, uniform initial conditions (outer product of suitably normalized $\mathrm{1}$'s
for each pair), $20$ iterations, one component. 
\item We then extract node matches using PDM and GM.
\item
\emph{Three orders of magnitude speedup} from \textsf{NSD}-based approaches compared to \textsf{IsoRank}. 
\end{itemize}
}
\end{frame}

\begin{frame}
\frametitle{NSD: Parallelization [KKG JPDC'13, Submitted, KMSAG ParCo'13 Submitted]}

\textbf{Parallelization:}
NSD has been ported to parallel and distributed platforms.
\begin{itemize}
\item We have aligned up to million-node graph instances using over 3K cores.
\item We process graph pairs of over a billion nodes and twenty billion edges each (!), on MapReduce-based
distributed platforms.
\end{itemize}

\end{frame}



\begin{frame}{Similarity of different tissues to yeast}{preliminary p-value results}
    \begin{center}
		\includegraphics[height=.5\textheight]{figures/TissueSimNet}
    \end{center}
	\begin{itemize}
		\item Green nodes show tissues with significant similarity to yeast, while red nodes show dissimilar tissues.
		\item Similar tissues tend to have consistent p-values.
	\end{itemize}
\end{frame}

\begin{frame}{Similarity of different tissues to yeast}{Alignment consistency of housekeeping proteins}
    \begin{center}
		\includegraphics[height=.5\textheight]{figures/HK_consistency_plot}
    \end{center}
	\begin{itemize}
		\item Approximately $75\%$ of housekeeping proteins are aligned with yeast proteins.
		\item Among aligned HK proteins, $25\%$ of them consistently aligned across all 79 tissues.
	\end{itemize}
\end{frame}

%
\section*{Summary}

\begin{frame}{Summary}
\scriptsize
  \begin{itemize}
  \item Aging is the primary risk factor for a number of human diseases.
  \item Emerging evidence supports the hypothesis that large classes of age-related pathologies share their underlying biology
  \item Constructing a comprehensive map of aging pathways is a critical step towards
deciphering key lifespan mediators, their crosstalk, and systems-level organization. 

	\item Tissue-specificity analysis is needed to precisely model aging in both proliferating and post-mitotic cells. 
  \end{itemize}
  
  % The following outlook is optional.
  \vskip0pt plus.5fill
  \begin{itemize}
  \item
    Future works:
    \begin{itemize}
    \item Construction of comprehensive, yet reliable tissue-specific networks by integrating various available high-throughput datasets.
    \item Devising a \textit{biased} information flow method for targeting specific subsets of TOR effectors.
    \end{itemize}
  \end{itemize}
\end{frame}



\appendix
\section<presentation>*{\appendixname}
\subsection<presentation>*{For Further Reading}

\begin{frame}[allowframebreaks]
  \frametitle<presentation>{For Further Reading}
    
  \begin{thebibliography}{10}
    
%  \beamertemplatebookbibitems
 
  \beamertemplatearticlebibitems
  
  \bibitem{Johnson}
	SC.~Johnson et al.
    \newblock mTOR is a key modulator of ageing and age-related disease
	\newblock \emph{Nature}, 493(7432):338--45, 2013.

  \bibitem{Naylor}
	RM.~Naylor et al.
    \newblock Senescent cells: a novel therapeutic target for aging and age-related diseases
    \newblock {\em linical pharmacology and therapeutics}, 93:105--116, 2013.

  \bibitem{Campisi}
	J~Campisi et al.
    \newblock Cellular senescence: a link between cancer and age-related degenerative disease?
    \newblock {\em Seminars in cancer biology}, 21: 354--359, 2012.

%  \bibitem{Bossi}
%    A.~Bossi et al.
%    \newblock Tissue specificity and the human protein interaction network
%    \newblock {\em Molecular Systems Biology (MSB)}, 5:260, 2009.
%        
  \end{thebibliography}
\end{frame}

\end{document}


