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\begin{document}

\title{{\color{blue} Science of Information Initiative} }
\date{}

%Department of Computer Science\\
%Purdue University\\
%W. Lafayette, IN 47907}


\maketitle

\vspace*{0.5in}
\begin{center}
Wojciech Szpankowski and Ananth Grama
\end{center}

\vspace{1.0in}
$\ldots$ to integrate {\color{red} research and teaching} activities
aimed at investigating the
role of {\bf {\color{OliveGreen} information}} from various viewpoints:
from \red{fundamental theoretical} underpinnings of \green{information} to 
diverse applications in
{\color{magenta} life sciences, communication networks, economics, 
and complex systems}.


\baselineskip=0.3in

\foilhead{Outline}

\begin{enumerate}
\item {\color{blue} What is Information?}
\item {\color{red} Beyond Shannon Information}
\item {\color{blue} Today's Challenges}
\item \red{Some Examples}
\end{enumerate}

\foilhead{Interpretation of Information $\ldots$}
\parsec
{\bf {\color{OliveGreen} C. F. Von Weizs\"{a}cker}}:
\parsec
$
\begin{array}{l}
\includegraphics[width=1in]{images/weizsacker.ps}
\end{array} \begin{array}{l}
\mbox{``{\color{red} Information} is only that which 
{\color{blue} produces information}"} \\
\mbox{ ({\color{OliveGreen} relativity}).}\\
\mbox{``{\color{red} Information} is only that which
{\color{blue} is understood}'' ({\color{OliveGreen} rationality})}\\
\mbox{``{\color{red} Information} has {\color{blue} no absolute meaning}''.}
\end{array}
$
\parsec
{\bf {\color{OliveGreen} R. Feynman}}:
\parsec
$
\begin{array}{l}
\includegraphics[width=1in]{images/feynman.ps}
\end{array} \begin{array}{l}
\mbox{``$\ldots$ {\color{red} Information} is as much a property of your own
}\\
\mbox{knowledge as anything in the message.}\\
\mbox{$\ldots$ {\color{red} Information} is not simply a physical property}\\
\mbox{ of a message:}\\
\mbox{ it is a property of the message and your}\\
\mbox{{\color{blue} knowledge about it}.''}
\end{array}
$

\foilhead{Interpretation of Information $\ldots$}
\parsec
%{\bf {\color{OliveGreen} J. Wheeler}}:
%\parsec
$
\begin{array}{l}
\includegraphics[width=1in]{images/wheeler.ps}
\end{array} \begin{array}{l}
\mbox{{\bf {\color{OliveGreen} J. Wheeler}}:}\\
\mbox{``{\color{red} It from Bit}'' ({\color{OliveGreen} 
Information is physical}.)}
\end{array}
$
\parsec
%{\bf {\color{OliveGreen} C. Shannon}}:
%\parsec
$
\begin{array}{l}
\includegraphics[width=1in]{images/shannon.ps}
\end{array} \begin{array}{l}
\mbox{{\color{OliveGreen} C. Shannon}:}\\
\mbox{``These {\color{red} semantic} aspects of communication
are}\\
\mbox{{\color{red} irrelevant} $\ldots$''}
\end{array}
$


\foilhead{Structural and Biological Information}
\parsec
{\bf {\color{OliveGreen} F. Brooks, jr.}} (JACM, 50, 2003,
``{\color{blue} Three Great Challenges for $\ldots$ CS }''):
\parsec
%\begin{array}{l}
%\includegraphics[width=1in]{images/brooks.ps}
%\end{array}
%\begin{array}{l}
``{\color{blue} Shannon} $\ldots$ gave us 
a definition of {\color{OliveGreen} Information}
and a metric for {\color{OliveGreen} Information} as 
{\color{blue} communicated} from place
to place. We have {\bf {\color{red} no theory}} however that gives
us a metric for the {\color{OliveGreen} Information} embodied in 
{\color{red} structure} $\ldots$
this is the most {\color{red} fundamental gap} in the
theoretical underpinning of {\color{OliveGreen} Information} 
and computer science.
$\ldots$ A young information theory scholar
willing to spend years on a {\bf {\color{blue} deeply fundamental problem}}
need look no further.''

\foilhead{Structural and Biological Information}
\parsec
{\bf {\color{blue} M. Eigen}}
\parsec
`The \red{differentiable characteristic} of the 
\red{living systems} is
{\bf {\color{OliveGreen} Information}}.
{\color{OliveGreen} Information} assures the \blue{controlled
reproduction} of all constituents,
thereby ensuring \blue{conservation of viability} $\ldots$ .
{\color{OliveGreen} Information theory},
pioneered by {\color{blue} Claude Shannon},
{\color{red} cannot} answer this
question \ldots  %in principle, the answer was formulated}
%130 years ago by
%{\bf {\color{red} Charles Darwin}}.}
in principle, the answer was formulated 130 years ago by
{\color{red} Charles Darwin}.


\begin{comment}
\foilhead{Example: Distributed Information}
\parsec
{\bf \green{1.}} 
\green{Example 2}: In an \blue{$N$-threshold} \red{secret sharing} 
scheme, \blue{$N$ subkeys} of the \blue{decryption key} 
roam among \blue{$A\times A$} 
stations.
\\
$
\begin{array}{l}
\mbox{{\bf {\color{OliveGreen} 2}}. By protocol $P$ 
a station has \red{access}:}\\
\mbox{$\bullet$ only it \red{sees all} \blue{$N$ subkeys}.}\\
\mbox{$\bullet$ it is \red{within a distance $D$} from all 
\blue{subkeys}.}
%Let $N$ subkeys move at \blue{random} 
%among}\\
%\mbox{\blue{$A\times A$ stations} regularly spaced within}\\
%\mbox{a square area. A station can improve} \\
%\mbox{its \red{objective} (access to the secret if it is )}\\ 
%\mbox{\red{within a distance $D$} from each of the subkeys}. 
\end{array} \begin{array}{ll}
& 
\tiny
\renewcommand{\arraystretch}{.75} 
\begin{tabular}{c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c@{\hspace{0.15in}}c}
 . & . & . & . & . & . & . & . & . & . & x & . & . & . & . & .
& . & . \\ 
. & . & . & . & . & . & . & x  & x & x & x & x & x & x & . & . 
& . & . \\ 
. & . & . & . & . & x & x & x & x & x & x & x & x & x & x & . 
& . & . \\
. & . & . & . & x & x & x & x & x & x & x & x & x & x & x & .
& . & . \\ 
. & . & . & . & x & x & x & x & x & x & x & x & x
& x & x & . & . & . \\
. & . & . & x & x & x & x & \red{{\boldmath $\star$}} & x &
\red{{\boldmath $\star$}} & x & x & x & x & x & x & . & . \\
. & . & . & x & x & x & x
& x & x & x & x & x & x & x & x & . & . & . \\ 
. & . & . & x & x & x & x & x & x & x & x & x & x & x & x & . 
& . & . \\ 
.  & . & x & x & x & x & x & x & x & x & \red{{\boldmath $\star$}} 
& x & x & x & x & . & . & . \\
. & . & . & x & x & x & x & x & x & x & x & x & x & x & x & .
& . & . \\ 
. & . & . & x & x & x & x & x & x & x & x & x & x
& x & x & . & . & . \\ 
. & . & . & . & x & x & x & x & x & x & x & x & x & . & . & . & . 
& . \\ 
. & . & . & . & . & . & x & x & x & x & x & . & . & . & . & . 
& . & . \\ 
. & . & . & .  & . & . & . & .  & . & . & . & . & . & . & . 
& . & . & . \\ 
.  & . & . & . & . & . & . & .  & . & . & . & . & . & . & . & . 
& . & . 
\end{tabular}
\normalsize
\renewcommand{\arraystretch}{1}

\end{array}
$
\parsec
$
\begin{array}{l}
\mbox{{\bf {\color{OliveGreen} 3}}. Assume that the \blue{larger $N$},}\\ 
\mbox{the \blue{more valuable the secrets}.} \\
\mbox{We define the \green{amount of information} as}\\
\\
\mbox{\red{info}= $\red{N}  \times \{\blue{\#~
{\rm of~stations~having~access}}\}$ .}
\end{array} \begin{array}{ll}
& \includegraphics[clip,trim=-10 0 0 0,width=2.4in]{figures/info-fig-keys.epsi}
\end{array}
$
\end{comment}

\foilhead{Shannon Information $\ldots$}
\parsec
In 1948 {\color{red} C. Shannon} created a powerful theory of
{\bf {\color{OliveGreen} information}} that served as 
the backbone to a
now classical paradigm of {\color{blue} digital communication}.
\parsec
In our setting, {\color{OliveGreen} Shannon defined}:\\
{\color{red} objective}: \hspace{0.5in}
 {\color{blue} statistical ignorance} of the recipient;
\vspace{-1.0em}
\parsec
\hspace{2.0in} {\color{blue} statistical uncertainty} of the recipient.
\parsec
{\color{red} cost}: \hspace{1.25in} 
{\color{blue} \# binary decisions} to \red{describe} $E$; 
\vspace{-1.0em}
\parsec
\hspace{2.0in} = {\color{blue} $-\log P(E)$}; ~ ~ {\color{magenta} $P(E)$ 
being the probability of $E$}.
\vspace{-1.0em}
\parsec
{\color{red} Context}: \hspace{.8in}
the {\color{blue} semantics} of data is \red{irrelevant}~$\ldots$
\parsec\parsec
{\color{blue} Self-information for $E_i$}:  ${\color{red} \ \ \ \ \ \  
\ \ \ \ \ \ \info(E_i)}= -\log P(E_i)$.\\
{\color{blue} Average information}: {${\color{red} \ \ \ \ \ \ \ \ \ \ \ \ \  
 H(P)}= -\sum_i P(E_i) \log P(E_i)$\\ 
%\hspace{1.0in} {\color{blue} Entropy of $X=\{E_1, \ldots\}$}: \ \ \ \ \ 
%{${\color{red} H(X)}= -\sum_i P(E_i) \log P(E_i)$ \\
%{\color{blue} Mutual Information}: ${\color{red} \ \ \ \ \ \ \ \ \ \ \ \ \
%I(X;Y)}=H(Y)-H(Y|X)$},  {\color{OliveGreen} (faulty channel)}. 
\parsec
{\bf {\color{OliveGreen} Shannon's statistical information}} tells us
how much a {\color{blue} recipient} of data can reduce their
{\color{blue} statistical uncertainty} by observing data.
%\parsec
%{\bf {\color{OliveGreen} Shannon's information}} is {\color{red} not absolute
%information} since {\color{blue} $P(E_i)$} (prior knowledge)  is a
%{\color{blue} subjective property of the recipient}.

%\parsec
%A wide spread application of
%information theory to {\color{blue} economics, biology, chemistry, life science} and
%{\color{blue} networks} is {\bf {\color{magenta} still awaiting}} us
%(Shannon: $\ldots$ {\color{OliveGreen} the semantics of data is irrelevant} $\ldots$)


\foilhead{Beyond Shannon}
\vspace{-0.5in}
%\parsec
%Participants of the {\bf {\color{OliveGreen} 2005 Information Beyond Shannon}} 
%workshop realize:
\parsec
{\bf {\color{red} Delay}}: 
In many \blue{communication problems}, when
\green{information} is transmitted over a ({\color{magenta} biological or 
computer}) \blue{network},
the amount of \red{delay} incurred is important 
\small
(e.g., {\color{OliveGreen} complete information arriving late maybe useless}).
%\small
%{This is not a question of understanding the classical 
%delay-rate trade-off, but a complex 
%issue involving our choice of how and what to transmit, as well as the 
%actually utility of the information being transmitted}.
\normalsize
\parsec
{\bf {\color{red} Space}}: In networks the {\color{blue} spatially distributed}
components raise fundamental issues of limitations in 
{\bf \green{information exchange}} since the available resources must 
be \red{shared}
\small
(e.g., \green{spatial localization} is at the
heart of \blue{cell dynamics}).
\normalsize
\parsec
{\bf {\color{red} Information and Control}}: 
{\bf {\color{OliveGreen} Information}} is
{\color{magenta} exchanged} in {\color{red} space and time}
for {\color{magenta} decision making}, thus {\color{blue} timeliness} of 
information delivery along with {\color{magenta} reliability} and 
\blue{complexity} constitute the \blue{basic objective}
\small
(e.g., \green{robust, and self stabilizing} system \red{dynamics} are key to 
\blue{signaling and regulation in biology}). 
\normalsize

\foilhead{Beyond Shannon}
\parsec
{\bf {\color{red} Dynamic information}}:  In a {\color{blue} complex network}
in a {\color{red} space-time-control} environment
information is not simply communicated but also \red{processed} and   
\red{generated} along the way
( e.g., {\color{OliveGreen} human brain}). 
%How such considerations of \blue{dynamical
%sources} be incorporated into the \green{information-theoretic model}?
\parsec
{\bf \red{Limited Computational Resources}}: Often 
\green{information} is limited by available \blue{computational resources} 
\small
(e.g., cell phone, living cells, weirdness of quantum world). 
\normalsize
\parsec
{\bf {\color{red} Semantics vs Syntax}}: 
\red{Semantics} of higher order processes are often needed,
\blue{without precise knowledge} of underlying components. How can these
\red{semantics} be \blue{inferred and coded} in a 
\green{context-specific manner}?

\begin{comment}
\foilhead{Some Things to Think About $\ldots$}
\parsec
Here is a short list of ``toy problems'' to think about:
\begin{itemize}
\item {\color{red} Temporal Capacity} (e.g., assign
{\color{OliveGreen} transmission time} to
each symbol or a block of symbols).
\\
\item {\color{blue} Spatial Capacity} (e.g., destination may be in
different {\color{OliveGreen} locations}).
\\
\item {\color{red} Darwin Channel} that models
flow of \green{genetic information} (e.g., a
combination of a {\color{blue} deletion/insertion} channel
and {\color{blue} constrained} channel).
\\
\item {\color{OliveGreen} Distributed Information} (information
{\color{blue} here/local} and {\color{blue} there/distributed}
is not the same?)
\\
\item {\color{red} Speed of Information} (how {\color{OliveGreen} fast}
information can be spread out?)
\\
\item {\color{blue} Entropy of a structure} (e.g., graph entropy)
\\
\item \red{Semantic vs Syntactic} measure of \green{information}.
\end{itemize}
\end{comment}


%Slide
\stitle{Information in the Physical Sciences}

\begin{itemize}
\item
Connections between information and the real world have intruigued
researchers for centuries.
\item
Maxwell's thought experiment (1867) in relation to the 2nd Law
of Thermodynamics provides an elegant nexus.
\centerline{
\psfig{file=maxwell.ps,height=2.3in}
}
\begin{center}
Maxwell's demon was one of the first observations
relating energy to information.
\end{center}
\item
Szilard and Brillouin subsequently used this to equate one bit of information
with $K_BT \ln 2$ joules of energy.
\end{itemize}

\stitle{Information and the Sciences -- a Brief History}

Maxwell's Demon Realized [David Leigh, Nature, 2007]

\centerline{
\psfig{file=leigh.ps,height=4.0in}
}
\begin{center}
Irradiation of rotaxane 1 at 350 nm in CD$_3$OD at 298 K interconverts
the three diastereomers of 1 and, in the presence of benzil, drives the
ring distribution away from the thermodynamic minimum, increasing the
free energy of the molecular system without ever changing the binding
strengths of the macrocycle or ammonium binding sites.
\end{center}


\stitle{Information and the Sciences -- a Brief History}

\begin{itemize}
\item
As the science of information developed, so did interest in correspondence
between information theoretic and scientific measures.
\item
Among the more intuitive and well studied is the correspondence between
thermodynamic (Boltzmann-Gibbs) entropy and information theoretic
(Shannon-Hartley) entropy.
\item
Drawing on these, two questions arise:
\begin{itemize}
\item
Can we draw on information theoretic formalisms to address foundational
questions in scientific disciplines?
\item
Can we draw on physical principles to address basic questions in
computing?
\end{itemize}
\end{itemize}

%\end{comment}

\stitle{Role of Information in Scientific Discovery}

\noindent
{\em What is the ``informative'' component of XRCC1, BRCA-1, and
H. sapiens DNA ligase III?}

\centerline{
\psfig{file=BRCT1.ps,height=3.5in}
}
\begin{center}
BRCT domain, from T.Thermophilus DNA Ligase.
\end{center}

These are inferred using a variety of information correlation and
extraction techniques (sequence analysis, Markov models), and 
experimentally validated.

\stitle{Role of Information in Scientific Discovery}

\noindent
{\em What is the ``infomative'' component of a set of PPI
networks?}

\begin{itemize}
\bigitem{PPI networks for \blue{9 eukaryotic organisms} derived from BIND and DIP}
{\item {\sl A. thaliania, O. sativa, S. cerevisiae, C. elegans, D. melanogaster,
            H. sapiens, B. taurus, M. musculus, R. norvegicus}
 \item \# of \green{proteins} ranges from 288 ({\sl Arabidopsis}) to 8577 ({\sl fruit fly})
 \item \# of \green{interactions} ranges from 340 ({\sl rice}) to 28829 ({\sl fruit fly})
}
\bigitem{Ortholog contraction}
{\item Group proteins according to existing \blue{COG} ortholog clusters
 \item Merge \blue{Homologene} groups into COG clusters
 \item Cluster remaining proteins via \blue{BLASTCLUST}
 \item Ortholog-contracted {\sl fruit fly} network contains 11088 \green{interactions}
 between 2849 \green{ortholog groups}
}

\item {\sc Mule} is available at \\
\blue{\url{http://www.cs.purdue.edu/pdsl/}}
\end{itemize}

\stitle{Conserved Protein Interaction Patterns}

\vspace{1in}
\centerline{
\begin{psfrags}
\scriptsize
\psfrag{Pattern}[c][c]{{\sl Pattern}}
\psfrag{CE}[lt][lt]{{\sl C. elegans}}
\psfrag{SC}[l][l]{{\sl S. cerevisiae}}
\psfrag{DM}[ct][ct]{{\sl D. melanogaster}}
\leavevmode
\epsfig{file=ribon.eps, width=4.5in}
\end{psfrags}
}
\vspace{0.5in}
\centerline{\blue{Small nuclear ribonucleoprotein complex} (\green{$p < 2e-43$})}

\stitle{Conserved Protein Interaction Patterns}

\vspace{1in}
\centerline{
\begin{psfrags}
\scriptsize
\psfrag{Pattern}[c][c]{{\sl Pattern}}
\psfrag{HS}[c][c]{{\sl H. sapiens}}
\psfrag{SC}[c][c]{{\sl S. cerevisiae}}
\psfrag{BT}[c][c]{{\sl B. taurus}}
\leavevmode
\epsfig{file=arp23.eps, width=4.5in}
\end{psfrags}
}
\vspace{0.5in}
\centerline{\blue{Actin-related protein Arp2/3 complex} (\green{$p < 9e-11$})}

\stitle{Conserved Protein Interaction Patterns}

\vspace{1in}
\centerline{
\begin{psfrags}
\scriptsize
\psfrag{Pattern}[c][c]{{\sl Pattern}}
\psfrag{HS}[c][c]{{\sl H. sapiens}}
\psfrag{DM}[c][c]{{\sl S. cerevisiae}}
\psfrag{SC}[l][l]{{\sl D. melanogaster}}
\leavevmode
\epsfig{file=vac.eps, width=6in}
\end{psfrags}
}
\vspace{0.5in}
\centerline{\blue{Endosomal sorting} (\green{$p < 1e-78$})}


\stitle{Role of Information in Scientific Discovery}

\noindent
{\em What is the ''informative'' component shared by two given
PPI networks (Yeast and Fruit Fly)?}

\begin{center}
\begin{small}
\begin{tabular}{lcccccc}
\hline\hline
\blue{Rank} & \green{Score} & \red{$z$-score} & \blue{\# Proteins} & \green{\# Matches} & \red{\# Mismatches} & \blue{\# Dups.}\\
\hline\hline
1 & 15.97 & 6.6 & 18 (16, 5) & 28 & 6 & (4, 0)\\
 & \multicolumn{6}{l}{protein amino acid phosphorylation (69\%)}\\
  &  \multicolumn{6}{l}{JAK-STAT cascade (40\%)}\\
\hline
2 & 13.93 & 3.7 & 13 (8, 7) & 25 & 7 & (3, 1)\\
 & \multicolumn{6}{l}{endocytosis (50\%) / calcium-mediated signaling (50\%)}\\
\hline
5 & 8.22 & 13.5 & 9 (5, 3) & 19 & 11 & (1, 0)\\
 & \multicolumn{6}{l}{invasive growth (sensu Saccharomyces) (100\%)}\\
 &  \multicolumn{6}{l}{oxygen and reactive oxygen species metabolism (33\%)}\\
\hline
6 & 8.05 & 7.6 & 8 (5, 3) & 12 & 2 & (0, 1)\\
 & \multicolumn{6}{l}{ubiquitin-dependent protein catabolism (100\%)}\\
 &  \multicolumn{6}{l}{mitosis (67\%)}\\
\hline
21 & 4.36 & 6.2 & 9 (5, 4) & 18 & 13 & (0, 5)\\
 & \multicolumn{6}{l}{cytokinesis (100\%, 50\%)}\\
\hline
30 & 3.76 & 39.6 & 6 (3, 5) & 5 & 1 & (0, 6)\\
 & \multicolumn{6}{l}{DNA replication initiation (100\%, 80\%)}\\
\hline\hline
\end{tabular}
\end{small}
\end{center}

\stitle{Subnets Conserved in Yeast and Fruit Fly}

\vspace{0.5in}
\centerline{Proteosome regulatory particle subnet}
\vspace{0.2in}
\begin{psfrags}
\begin{scriptsize}
\psfrag{SC}[c][c]{{\sl S.Cerevisiae}}
\psfrag{DM}[c][c]{{\sl D.Melanogaster}}
\centerline{\epsfig{file=4932_7227_6.eps, width=5.0in}}
\end{scriptsize}
\end{psfrags}

\vspace{0.5in}
\centerline{Calcium-dependent stress-activated signaling pathway}
\vspace{0.2in}
\begin{psfrags}
\begin{scriptsize}
\psfrag{SC}[c][c]{{\sl S.Cerevisiae}}
\psfrag{DM}[c][c]{{\sl D.Melanogaster}}
\centerline{\epsfig{file=4932_7227_2.eps, width=5.0in}}
\end{scriptsize}
\end{psfrags}


\stitle{Role of Information in Scientific Discovery: Examples}

\noindent
{\em What are statistically significant functional pathways in
gene regulatory networks?}

\centerline{
\psfig{file=gene-ontology.ps,height=2.3in}
\hspace*{1.0in}
\psfig{file=gnom.ps,height=2.3in}
}

\centerline{
\psfig{file=sigMap.eps,height=3.2in}
}



\stitle{Role of Information in Scientific Discovery: Examples}



\noindent
{\footnotesize
\begin{tabular}{lll}
        \hline
        Frequency & $p$-value & Pathway\\
        \hline
        276 & 5E-94 & metabolic process $\dashv$ flagellum biogenesis $\rightarrow$ transport\\
        136 & 3.1E-71 & regulation of translation $\dashv$ DNA recombination $\rightarrow$ transport\\
        38 & 4.9E-47 & response to stimulus $\dashv$ transcription $\rightarrow$ cell motility\\
        36 & 6.6E-35 & flagellum biogenesis $\rightarrow$ ciliary or flagellar motility\\
        56 & 1.4E-24 & regulation of translation $\dashv$ transcription $\rightarrow$ carboxylic acid metabolism\\
        178 & 8.3E-21 & signal transduction $\dashv$ transcription $\rightarrow$ transport \\
        14 & 8.6E-20 & phosphate transport $\rightarrow$ transcription $\rightarrow$ phosphonate transport\\
        16 & 2E-16 & SOS response $\dashv$ regulation of transcription $\dashv$ DNA repair\\
        501 & 1.2E-13 & regulation of transcription, DNA-dependent $\rightarrow$ transport\\
        12 & 3.6E-10 & proteolysis $\dashv$ regulation of transcription $\dashv$ response to external stimulus \\
        15 & 3.8E-7 & nitrate assimilation $\dashv$ cytochrome complex assembly\\
        10 & 1.4E-6 & cell morphogenesis $\dashv$ protein secretion\\
        178 & 3.8E-4 & transcription $\rightarrow$ carbohydrate metabolic process\\
\hline
\end{tabular}
}



\stitle{Information Sciences in Life Sciences}

Characterizing phenotype and disease in networks.

\centerline{\psfig{file=refrence.ps,width=10cm}
            \psfig{file=alzheimers_human.ps,width=13cm}}
\begin{center}
Detecting ``informative'' parts of networks is an essential
aspect of understanding disease and remediation (Alzheimers).
\end{center}


\stitle{Possibilities Abound}

\end{document}

