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<div align=center>
<h1>The Role of Bioinformatics in Interpretating Genomes of Unicellular Organisms:</h1> 
<br>
<h1>An Abstract View</h1>
<h2>by Ross Overbeek</h2>
</div>
<h2>Introduction</h2>
This strange document began as a tutorial for computer scientists and mathematicians.  It was supposed
to somehow introduce them to the computational issues in genome analysis.
It was requested by an instructor in a computer class.  Overbeek in attempting to respond to this request
formulated an abstraction that he began to believe had significance beyond the tutorial.
<p>
This document is a set of working notes relating to the abstract.  It is not organized properly as
an abstraction, a tutorial, or an essay on the role of bioinformatics in support of biological research.  It is,
however, organized properly as a working document that relates to all of these goals.
<p>
It begins with a development of the abstraction.  This will be suitable for mathematicians or computer scientists.
The abstraction is developed in four steps: the basic abstraction, the enhanced abstraction needed to support
basic bioinformatics support for biologists, and finally the third step which includes suport for the notion
of regulation.  The intent throughout this discussion will be to seek a minimal set of concepts needed to
effectively capture the essence of the required data.  Unlike almost all efforts to lay a foundation
for tutorials, software or research in biology, this effort focuses on leaving out as much as possible.
While we do believe that there is an almost unlimited complexity that can be introduced, and almost all of
it is needed for some specific goals, the vast majority of tools and discussions require (we believe) relatively few
concepts.  As they say, "the proof is in the pudding."

<p>
The second section will feature a bit more tutorial comments.  It may well repeat much of what is in Part 1.
This part is offered as a way of easing a computer scientist of mathematician into the issues that need to be
considered, if they wish to try to do useful research relating to the genomics revolution.  Eventually, this part
will be dramatically expanded by giving condensed summaries of the machines of the cell broken into two broad
sets: the metabolic network and the cellular machinery not directly included in the metabolic network.  Loosely,
this separates what would be learned in a microbial biochemistry class (when they exist) from what would
be learned in a course on molecular biology.
<p>
The third part is an essay is an attempt to characterize our view on 
<ul>
<li> what the main goals should be in current efforts to advance biological knowledge via genome research,
<li> what role bioinformatics researchers have played in the past, and
<li> what role they could productively play during the coming few years.
</ul>
As such, it is undoubtedly an arrogant formulation by a group of individuals with minimal background in
biology.
<p>
The fourth section will focus on the imlications of the abstractions in software development.
This is a bit of a radical proposal that makes sense to us (and is in an area that we can
legitimately claim expertise).

<h1>Part 1: The Abstractions</h1>
<h2>The cell: a Minimal Perspective</h2>

A <b>cell</b> is a bag (i.e., a volume enclosed by a membrane) that contains three types of things: compounds, cellular machines, and a genome.
<p>
By the term <b>compound</b> we refer to the normal notion of chemical compound. 
<p>

A <b>cellular machine</b> is a set of proteins that together perform a function. Unless otherwise noted,
when we use the term <i>machine</i> we will always be speaking of a cellular machine.
Many machines
transform one set of compounds into another set.  Some machines (transport machines) 
are used to move compounds into
or out of the cell.  Later we will try to convey a more comprehensive notion of what functions are implemented
by machines that we understand.
<p>

A <b>protein</b> is a string of amino acids (i.e., a string in the  20-character alphabet {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y}).
<p>

A <b>genome</b> is a string of DNA bases (i.e., a string in the 4-character alphabet {A,C,G,T}).
<p>

A <b>gene</b> is a region in the genome that describes how to build a
protein.  The description is a sequence of 3-character codons.  Each
codon corresponds to either a single amino acid or a stop codon.
There are three stop codons: {TAA,TAG,TGA}.  The genetic code is the
table of correspondences between codons and amino acids:
<br><br>
<table border>
<tr><th>Amino Acid</th><th>Codons</th></tr>
<tr><td>A</td> <td>GCT, GCC, GCA, GCG </td></tr>
<tr><td>C</td> <td>TGT, TGC</td></tr>
<tr><td>D</td> <td>GAT, GAC</td></tr>
<tr><td>E</td> <td>GAA, GAG</td></tr>
<tr><td>F</td> <td>TTT, TTC</td></tr>
<tr><td>G</td> <td>GGT, GGC, GGA, GGG</td></tr>
<tr><td>H</td> <td>CAT, CAC</td></tr>
<tr><td>I</td> <td>ATT, ATC, ATA</td></tr>
<tr><td>K</td> <td>AAA, AAG</td></tr>
<tr><td>L</td> <td>TTA, TTG, CTT, CTC, CTA, CTG</td></tr>
<tr><td>M</td> <td>ATG</td></tr>
<tr><td>N</td> <td>AAT, AAC</td></tr>
<tr><td>P</td> <td>CCT, CCC, CCA, CCG</td></tr>
<tr><td>Q</td> <td>CAA, CAG</td></tr>
<tr><td>R</td> <td>CGT, CGC, CGA, CGG, AGA, AGG</td></tr>
<tr><td>S</td> <td>TCT, TCC, TCA, TCG, AGT, AGC</td></tr>
<tr><td>T</td> <td>ACT, ACC, ACA, ACG</td></tr>
<tr><td>V</td> <td>GTT, GTC, GTA, GTG</td></tr>
<tr><td>W</td> <td>TGG</td></tr>
<tr><td>Y</td> <td>TAT, TAC</td></tr>
<tr><td>*</td> <td>TAG, TGA, TAA  [Stop codons]</td></tr>
</table>
<br><br>
<hr>
The process of building a protein as a string of amino acids from the gene containing codons is
called <b>expressing</b> the gene.
<br>
A <b>subsystem</b> (i.e., an abstract cellular machine) is a set of functional roles.
Each protein implements one or more functional roles.  The set of functional roles
implemented by the protein is called the <b>function of the protein</b>.  The function of a  multifunctional
protein that implements {functional-role-1,functional-role-2} is normally written as
<i>functional-role-1 / functional-role-2</i>.
<br><br>
A <b>populated subsystem</b> is a subsystem with an attached spreadsheet.  Each column
in the spreadsheet corresponds to a functional role in the subsystem, and each row corresponds to
a specific genome.  Each cell in the spreadsheet contains the genes from the corresponding genome
that implement the designated functional role (there may be 0 or more such genes).
<br><br>
We do not actually know what machines are present in a cell.  We are in the midst of a grand
effort to clarify which are there and what they do.  The formulation of subsystems as abstract machines
in which each row of the subsystem describes a specific cellular machine that is believed to be present,
represents a way to maintain a collection of estimates or assertions.
<p>
A <b>protein family</b> is defined to be a set of proteins that implement the same functional roles and
are similar over the entire lengths of the proteins.
<p>
We seek a situation in which each protein occurs in one or more subsystems and in a single protein family.
<p>
In any specific cell, sets of specific cellular machines are 
switched on and off as units.  That is, they are <i>co-regulated</i>.  We will call such a set
of <i>co-regulated cellular machines</i> a <b>regulon</b> (note that a regulon is often a set containing
a single cellular machine).  A <b>state</b> of a cell will be defined
as the set of regulons that are operational at a point in time.  Thus, a state amounts to the set
of cellular machines that are operational at one instant.
<p>
Microarrays are, for a given genome, two lists of genes that "changed expression levels" between two states of a
cell.  Basicaly, the first list contains genes that were "active" during the first state, but not the second; and the
second list contains genes that were "active" in the second but not the first.  If a cellular
machine utilizes protein <i>X</i>, and <i>X</i> is in the first list, and if <i>X</i> is used in
only one cellular machine, then it would be reasonable to infer that you could say that the machine was
active in the first state, but not the second.

<h2>The cell: the Enhanced Formlation Needed to Support Bioinformatics</h2>

In the enhanced abstraction, we need to losen up some concepts.  In particular,
<ul>
<li> A <b>genome</b> is a set of strings in a 4-character alphabet.  Each of the strings
is called a <b>contig</b>.  Note that the concept as formulated covers both incomplete genomes and 
genomes with multiple replicons.

<li>The genes within a genome are of two distinct types:
<ol>
<li>those that describe how to construct a protein (i.e., prtein-encoding genes), and
<li>those that describe how to construct a string of RNA (i.e., how to construct a string in the
4-character RNA alphabet {A,C,G,U}).
</ol>
<br><br>
<li>The location of a gene is generalized to be a set of regions within the genome (that are
concatenated to form the instructions needed to construct either a protein or a string of RNA).
<li>A protein is a character in an alphabet that now includes the 20 character codes from
the basic abstraction plus a very limited set of extra codes. 
We already have cases in which <i>selenocyctein</i>  and <i>pyrrolysine</i> appear as nonstandard 
translations of codons, and there may eventually be more.

<li>Each protein-encoding gene has both a DNA sequence (by defintion) and a translation.  However,
the translation is not required to exactly match what a codon-by-codon translation of the DNA sequence
would produce.  This allows us to handle the very rare instances in which selenocystein occurs as the translatin
of TGA or pyrrolysine occurs as a translation of TAG (and others, if necessary).
</ul>

This loosened up formulation represents a very minimal set of changes.  They should be left out of the
basic tutorial for computer scientists and mathematicians.

<h2>The cell: Adding the Concepts Needed to Discuss Transcriptional Regulation</h2>

In the final version of the abstraction, we add the minimal set of notions needed to support
analysis of transcriptional regulation.  An <b>operon</b> is a set of contiguous genes that are all 
on the same strand and are all co-regulated.  We consider a gene that is not co-regulated with any adjacent genes
to be an operon composed of just itself.  A <b>binding site</b> is a small region of DNA (normally
occurring a short space ahead of an operon) that acts as a switch turning the operon "on" or "off". When
a specific protein or expressed RNA called a <b>transcriptional regulator</b> binds the site, it flips the switch.  One or more 
specific transcriptional regulators can bind a specific site (i.e., sets of
 sites are associated with each specific transcriptional regulator).  The effect of a regulator binding at a site
always has the same effect (either activating or deactivating the operon), but which effect depends on
the site-regulator pair.

<h1>Part 1: Tutorial Notes</h1>

<h2>Notes for The Basic Abstraction</h2>

We will be speaking about organisms that are a single cell.  At some point life began on earth.
The single-celled organisms that we know of replicate producing copies of themselves that have
genomes which usually have very, very similar content to that of the parent cell.  <b>Evolution</b> is the
process in which cells replicate with some alterations in their genomes, are subjected to
<i>selective pressure</i>, and survive or not depending on many somewhat random factors.  The makeup of
cells (i.e., the genomes they contain and the machines that define what they are capable of doing)
changes gradually (and sometimes not so gradually) as time passes.
<p>
The original life forms that existed billions of years ago have evolved into three broad categories of
life forms.  That is, the evolutinary process led to early divisions, and these led to three main
categories of single-celled organisms.  We call these three forms the <b>archaea</b>, 
the <b>bacteria</b>, and the <b>eukaryotes</b>.
A majority of the organisms for which we have acquired complete genomes are from the bacteria, 
although the
numbers are rapidly growing for all three domains.
<p>
The minimal notion of a cell is enough to explain some of the basic
problems in bioinformatics:

<h3>Identify the genes within a genome</h3>

If we are to understand the contents of genomes, we will need to
locate the genes that occur in each genome.  This problem simply involves taking a genome (a
string of DNA) and locating the set of genes it contains.  
In the case of bacteria and archaea, we know pretty well how to
locate the genes.  
Once we
have identified instances from many genomes, it becomes possible to
recognize the genes in a new genome by just looking for things similar
to those we already understand.  The following problem is At the heart of reconizing when two
genes are "similar".

<h3>Given two genes. "align" them in a way that minimizes some edit function.  </h3>

For example, here is what you see when you align two genes from distinct organisms:

<pre>


gene1           ATGGCTGATTTATTCGCATTGACCGAAGAAGCGTTGGCGGGCATGGGCATCGAGTTGGTC
gene2           ---GTGCAACTGACGGAACTGATAGAAACTACGGTCACGGGGCTCGGCTACGAGCTCGTC
                   *   *  *    * * ***  ***    ** *  ****  * ***  **** * ***

gene1           GATGTCGAACGTGCCGCCTTAGGCTTGTTGCGCGTGACCATAGACCGTGAGGACGGTGTT
gene2           GATCTCGAGCGCACCGGGCGCGGCATGGTCTGCGTCTACATCGATCAGCCCGCCGGCATC
                *** **** **  ***     *** ** *  ****   *** ** *     * ***  * 

gene1           CGCATCGAAGATTGTGAGCAGGTGTCCCGGCAATTGTCGCGCGTCTACGAGGTCGAGAAC
gene2           ACGATCGACGATTGCGAGAAGGTCACGCGTCAGCTCCAGCACGTACTGACGGTCGAAAAC
                   ***** ***** *** ****  * ** **  *   ** ***      ****** ***

gene1           ATCGATTACAAACGTCTGGAAGTTGGCTCGCCGGGCGTGGATCGCCCCTTGCGCAACGAG
gene2           ATCGATTACGAACGGCTCGAGGTCTCGTCACCGGGGCTCGACCGGCCGTTGAAGAAGCTG
                ********* **** ** ** **    ** *****  * ** ** ** ***   **   *

gene1           GCGGAATTCCGTCGTTTCGCGGGTGAACGTATCGAGATCAAGCTGCGTGAGGCAGTCGAC
gene2           GCTGACTTCACGCGTTTCGCGGGCAGCGAGGCCGTCATCACCCTGAAAAAGCCGTTGGAC
                ** ** ***   ***********         **  ****  ***    ** *  * ***

gene1           GGGCGCAAAGTGTTTACCGGCATCCTGCAAGAGGCGGACACGTCTGCTGACGATAAGACG
gene2           GGGCGCAAGACGTACCGGGGCATTCTGCACGCGCCGAAC------------GGCGAGACG
                ********   **     ***** ***** * * ** **            *   *****

gene1           GTGTTCGGTCTCGAATTTGAGGCAAAGAAGGACGATATTCAGGTACTGAGCTTCACGCTC
gene2           AT---CGGTTTGGAATTTGAGAGGAAGAAGGGCGAGGCGGCCATGCTGGATTTCACGCTG
                 *   **** * *********   ******* ***        * ***   ******** 

gene1           GATGACATCGAGCGCGCCAAGCTGGATCCCGTTCTGGATTTCAAGGGCAAAAAGCGATGA
gene2           GCGGACATCGACAAGGCCCGCCTGATTCCGCACGTTGACTTTAGGAGCCGCAAACAATGA
                *  ********    ***   ***  ***     * ** ** * * **   ** * ****
</pre>
<hr>

The sequences are recognizably similar, and in fact implement exactly the same function
in the two cells.  If we align the protein sequences corresponding to these two
genes, we get

<pre>
gene1           MADLFALTEEALAGMGIELVDVERAALGLLRVTIDREDGVRIEDCEQVSRQLSRVYEVEN
gene2           -VQLTELIETTVTGLGYELVDLERTGRGMVCVYIDQPAGITIDDCEKVTRQLQHVLTVEN
                  :*  * * :::*:* ****:**:. *:: * **:  *: *:***:*:***.:*  ***

gene1           IDYKRLEVGSPGVDRPLRNEAEFRRFAGERIEIKLREAVDGRKVFTGILQEADTSADDKT
gene2           IDYERLEVSSPGLDRPLKKLADFTRFAGSEAVITLKKPLDGRKTYRGILHAPNG-----E
                ***:****.***:****:: *:* ****..  *.*::.:****.: ***: .:

gene1           VFGLEFEAKKDDIQVLSFTLDDIERAKLDPVLDFKGKKR
gene2           TIGLEFERKKGEAAMLDFTLADIDKARLIPHVDFRSRKQ
                .:***** **.:  :*.*** **::*:* * :**:.:*:
</pre>

There is a great deal of work relating to recognizing when two sequences are
similar and whether or not they had a common ancestor.  Understanding why
selective pressure conserves sections of sequences, but not others, will yield
important clues.  Can you reason out why some sections might be conserved, while
others vary wildly?
<p>

Comparing sets of sequences that have retained the same function is
at the heart of understanding cellular machines and the proteins that implement them.  
We find that looking at sets (often with more than two sequences) and aligning them
is important.


<h3> Given a set of sequences, align them in a way that minimizes some edit function.</h3>

Here is an example of a multiple sequence alignment:
<br>
<br>
<pre>
CLUSTAL W (1.83) multiple sequence alignment


seq3            -------------------MRYISTRGQAPALNFEDVLLAGLASDGGLYVPENLPRFTLE
seq4            -------------------MRYISTRGSAPTLSFEEVLLTGLASDGGLYVPESLPSFTSA
seq5            -------------------MNYISTRGAIAPIGFKDAVMMGLATDGGLLLPETIPALGRN
seq1            -------------------MKLYNLKDHNEQVSFAQAVTQGLGKNQGLFFPHDLPEFSLT
seq2            MKIRVICGAPTPKPFIKIPMKYYSTNKQAPLASLEEAVVKGLASDKGLFMPMTIKPLPQE
                                   *.  . .      .: :.:  **..: ** .*  :  :

seq3            EIASWVGLPYHELAFRVMRPFVAGSIADADFKKILEETYGVFAHDAVAPLRQLNGNEWVL
seq4            ELEAMASLDYPSLAHRILLPFVEEAFTGEELREIIDDTYAVFRHSAVAPLVQLDHNQWVL
seq5            TLESWQSLSYQDLAFNVIS-LFADDIPAQDLKDLIDRSYATFSHPEITPVVEKDG-VYIL
seq1            EIDEMLKLDFVTRSAKILSAFIGDEIPQEILEERVRAAFAFP-----APVANVESDVGCL
seq2            FYDEIENLSFREIAYRVADAFFGEDVPAETLKEIVYDTLNFD-----VPLVPVKENIYSL
                       * :   : .:   :.   ..   :.. :  :         .*:   .     *

seq3            ELFHGPTLAFKDFALQLLGRLLDHVLAKRGER-VVIMGATSGDTGSAAIEGCRRCDNVDI
seq4            ELFHGPTLAFKDFALQLLGRLLDAILKRRGEK-VVIMGATSGDTGSAAIAGCERCENIDI
seq5            ELFHGPTLAFKDVALQLLGNLFEYLLKERGEK-MNIVGATSGDTGSAAIYGVRGKDKINI
seq1            ELFHGPTLAFKDFGGRFMAQMLTHIA---GDKPVTILTATSGDTGAAVAHAFYGLPNVKV
seq2            ELFHGPTLAFKDVGGRFMARLLGYFIRKEGRKQVNVLVATSGDTGSAVANGFLGVEGIHV
                ************.. :::..::  .    * : : :: *******:*.  .      :.:

seq3            FIMHPHNRVSEVQRRQMTTILGDNIHNIAIEGNFDDCQEMVKASFADQGFLK-GTRLVAV
seq4            FILHPHGRVSEVQRRQMTTLSAPTIHNLAIEGNFDDCQAMVKASFRDQSFLPDGRRLVAV
seq5            FILHPHGKTSPVQALQMTTVLDPNVHNIAARGTFDDCQNIVKSLFSDLPFKE-KYSLGAV
seq1            VILYPRGKISPLQEKLFCTLGG-NIETVAIDGDFDACQALVKQAFDDEELKV-ALGLNSA
seq2            YVLYPKGKVSEIQEKQFTTLGR-NITALEVDGTFDDCQALVKAAFMDQELNE-QLLLTSA
                 :::*:.: * :*   : *:   .:  :   * ** ** :**  * *  :      * :.

seq3            NSINWARIMAQIVYYFHAALQLG-APH-RSVAFSVPTGNFGDIFAGYLARNMGLPVSQLI
seq4            NSINWARIMAQIVYYFYAGLRLG-APH-RAAAYSVPTGNFGDIFAGYLASKMGLPVAQLM
seq5            NSINWARVLAQVVYYFYAYFRVA-ALFGQEVVFSVPTGNFGDIFAGYVAKRMGLPIRRLI
seq1            NSINISRLLAQICYYFEAVAQLPQETRNQ-LVVSVPSGNFGDLTAGLLAKSLGLPVKRFI
seq2            NSINVARFLPQAFYYFYAYAQLKKAGRAENVVICVPSGNFGNITAGLFGKKMGLPVRRFI
                **** :*.:.*  *** *  ::      .  . .**:****:: ** ..  :***: :::

seq3            VATNRNDILHRFMSGNRYDKDTLHPSLSPSMDIMVSSNFERLLFDLHGRNGKAVAELLDA
seq4            IATNRNDVLHRLLSTGDYARQTLEHTLSPSMDISVSSNFERLMFDLYERDGAAIASLMAA
seq5            LATNENNILSRFINGGDYSLGDVVATVSPSMDIQLASNFERYVYYLFGENPARVREAFAA
seq1            AATNVNDTVPRFLHDGQWSPKATQATLSNAMDVSQPNNWPR-VEELFR------------
seq2            AANNKNDIFYQYLQTGQYNPRPSVATIANAMDVGDPSNFAR-VLDLYGGS----------
                 *.* *: . : :  . :       ::: :**:  ..*: * :  *.

seq3            FKASGKLSVEDQRWTEARKLFDSLAVSDEQTCETIAEVYRSCGELLDPHTAIGVRAAREC
seq4            FDD-GDITLSDAAMEKARQLFASHRVDDAQTLACIADVWGRTEYLLDPHSAIGYAAATQP
seq5            LPTKGRIDFTEAEMEKVRDEFLSRSVNEDETIATIAAFHRETGYILDPHTAVGVKAALEL
seq1            -------------RKIWQLKELGYAAVDDETTQQTMRELKELGYTSEPHAAVAYRALRDQ
seq2            -------------HAAIAAEISGTTYTDEQIRESVKACWQQTGYLLDPHGACGYRALEEG
                                      .    : :                :** * .  *  :

seq3            RRSLSVPMVTLGTAHPVKFPEAVEKAGIGQAPALPAHLADLFEREERCTVLPNELAKVQA
seq4            GANTQTPWVTLATAHPAKFPDAIKASAVGTTAQLPVHLADLFERSEHFDVLPNDIAAVQR
seq5            VQDG-TPAVCLATAHPAKFAEAVVR-AVGFEPSRPTSLEGIEALPSRCDVLDADRDAIKA
seq1            LNPG-EYGLFLGTAHPAKFKESVEA-ILGETLDLPKELAERADLPLLSHNLPADFAALRK
seq2            LQPG-ETGVFLETAHPAKFLQTVES-IIGTEVEIPAKLRAFMKGEKKSLPMTKEFADFKS
                        : * ****.** :::    :*     *  *            :  :   .:

seq3            FVSQHGNRGKPL
seq4            FMSGHLGA----
seq5            FIEKKAL-----
seq1            LMMNHQ------
seq2            YLLGK-------
                 :  :
</pre>

<h3> Given a multiple sequence alignment, determine the most likely evolutionary history of the sequences (i.e., construct a phylogenetic tree).</h3>

From the extant five sequences that are similar and displayed in the previous alignment, we can construct
a tree that depicts the "phylogenetic history" of the sequences.
Here is one reasonable tree for the last 5 sequences.  

<pre>
                          ,--------------------------------------------------- seq1
                          |
                          |
       ,------------------|
       |                  |
       |                  |
       |                  `---------------------------------------------- seq2
       |
       |
       |
  ,----|
  |    |
  |    |             ,-------------------------------- seq3
  |    |             |
  |    |             |
  |    |-------------|
  |                  |
  |                  |
  |                  `------------------------------ seq4
  |
  |
  `---------------------------------------------- seq5
</pre>

The tree suggests that at some point an ancestral
cell replicated.  One copy led (through a chain of descendants) to <b>seq5</b>, while the remaining sequences descend 
from the ther copy.
<p>
Note that we now have alignments that
contain thousands of sequences, and even displaying such trees is nontrivial.
Because evolution plays such a central role in the phenomena we study, the construction of alignments
and trees in order to compare extant versions of proteins and gain insight into their historical origins
is considered basic to the task at hand.

<h3>Some Random Facts that You Should Absorb</h2>

Most genomes of bacteria contain between 400,000 and 12,000,000 characters.  
Normally, the genes in a genome
cover abut 90% of the genome.
Normally, there is about one gene per 1000 characters in a bacterial genome.
<p>
So, 
<ul>
<li> What is the length of the average protein sequence?  
<li>How many genes do these
genomes have?  
<li>What is the average length of a gene?
</ul>
<br>
It is worth spending just a short bit of time thinking about what types of
machines must exist in each cell.  Here are a few thoughts to start with
<ul>
<li>
There must be one or more machines that support replication of the cell.  You would
need something to copy the genome, and you would need something that could build the DNA
bases that represent the characters (i.e., you will need machines to build the molecules
corresponding to each of the four characters in the alphabet of DNA bases.
<li>
As we mentioned, you have transport machines that take things into and out of the cell.  Many
cells can import food in the form of sugar molecules.  For example, many cells can import
<i>glucose</i> a six-carbon compound.  As the compound gets broken down into smaller compounds,
energy is salvaged from the broken bonds to power the machines in the cell.  The smaller compounds
are used as building blocks for other needs.
<li>
There must be one or more machines involved in building proteins from the descriptions in te genes.
In particular, we will need a machine for each of the amino acids (unless the cell can import some
of them).
<li>
There must be mechanisms for sensing what is going on in the environment and allowing the cell
to react to it.  For example, many cells can "swim" towards food.
</ul>
Those were just a few examples.  For any cell, we have many, many machines, and we still
do not even understand what some of them do.  Later, we will try to offer a more structured
estimate of what is already known.
<p>
About 50-60% of the genes occur within 5000 characters of another gene such that
the two genes encode proteins that are part of the same cellular machine.  This fact 
suggests that just having a large number of genomes would enable a person to group
the genes into the machines they implement, without the person understanding the functions
of the machines or the roles played by each protein.
<p>
Occasionally, proteins that are usually distinct in most cells are fused into a single protein in
a few cells.  In these cases, the fused gene is (by definition) part of a single machine, and
in most cells in which the proteins are not fused, the two distinct proteins are separate components
of a single machine.  This, too, offers clues to support analysis of which proteins go with which machines.
<p>
Biologists have figured out the roles of about 50% of the genes.  That is, they can
place the gene in a cellular machine, they know what the machine does, and they know
the specific role of the gene in sustaining the functionality of the machine.
<br><br>

<h23Imposing a Structure on Characterizing the Inventory</h2>

One central goal of bioinformatics is to support an accurate characterization of the cellular
machinery for each cell.  It is of major importance to biologsts that we be able to support
comparative analysis of cells.  Perhaps, the most important aspect of understanding cells relates to
their origin in an evolutionary process.  Cells have a long evolutionary history dating back billions of
years.  The machines we see in cells today arose in the past, so we expect to see many current cells
using machinery that resembles what turns up in other cells.  When we compare machines from different
cells they often look remarkably similar.  On the other hand, those that had a common origin in a cell that existed 
billions of years in the past may now have versions that are not very similar.  Modifications, optimizations,
and insignificant alterations all combine to explore the space of operational possibilities for
each type of machine.  Hence, we need a framework for studying similarities and differences in the
cellular machines and the proteins that implement them.
<p>

Here is a short formulation of one way to do this:
<br><br>
<ul>
<li>A <b>subsystem</b> (i.e., an abstract cellular machine) is a set of functional roles.
<li>Each protein implements one or more functional roles.  The set of functional roles
implemented by the protein is called the <b>function of the protein</b>.  The function of a  multifunctional
protein that implements {functional-role-1,functional-role-2} is normally written as
<i>functional-role-1 / functional-role-2</i>.
<br><br>
<li>A <b>populated subsystem</b> is a subsystem with an attached spreadsheet.  Each column
in the spreadsheet corresponds to a functional role in the subsystem, and each row corresponds to
a specific genome.  Each cell in the spreadsheet contains the genes from the corresponding genome
that implement the designated functional role (there may be 0 or more such genes).
</ul>
<br><br>
We do not actually know what machines are present in a cell.  We are in the midst of a grand
effort to clarify which are there and what they do.  The formulation of subsystems as abstract machines
in which each row of the subsystem describes a specific cellular machine that is believed to be present,
represents a way to maintain a collection of estimates or assertions.
<p>
A <b>protein family</b> is defined to be a set of proteins that implement the same functional roles and
are similar over the entire lengths of the proteins.
<p>
We seek a situation in which each protein occurs in one or more subsystems and in a single protein family.
The computational tasks imposed by such a goal are obvious:
<ul>
<li>We need to consruct databases that implement at least the following entities:
<ol>
<li>cells (i.e., each cell must have an ID and a set of attributes),
<li>genomes,
<li>genes,
<li>proteins,
<li>functional roles,
<li>subsystems, and
<li>protein families.
</ol>
<li> We need to add support for developing clues to function by integrating data
from sources like proximity within the genome, fusions, etc.
<li>We need to support a framework for the development of populated subsystems.
<li>We need to construct decision procedures for membership in protein families.  Some 
of these procedures will be quite complex, although the majority of cases can be
handled by fairly general procedures.
</ul>

<h3>States of the Cell</h2>

The notion of <i>subsystem</i> was introduced as an <i>abstract machine</i> -- that is, as an
attempt to create a framework for understanding variations within specific celular machines via
a form of comparative analysis.  

In any specific cell, sets of specific cellular machines are 
switched on and off as units.  That is, they are <i>co-regulated</i>.  We will call such a set
of <i>co-regulated cellular machines</i> a <b>regulon</b> (note that a regulon is often a set containing
a single cellular machine).  A <b>state</b> of a cell will be defined
as the set of regulons that are operational at a point in time.  Thus, a state amounts to the set
of cellular machines that are operational at one instant.
<p>
If we think of a car as a bag of machines that interact to make it function, we might consider there
to be a huge number of states.  There are many very minor "machines" like the arm rest (or the radio, r the night light) that can be on or off.  However, we can divide the states of a car into major groupings based on the status
of some key "machines".  For example, "off" (the state in which the engine is turned off and the car is parked) and
"on" (the engine is running and the car is moving) might be viewed as a crude partitioning of the states into
two "major states".
<p>
Similarly, I believe that we should think about <i>major states of the cell</i> as being determined by the 
functioning (or not) of a limited set of regulons.  The determination of these regulons, the major states,
and how transitions between are managed all are now parts of the picture being filed in.


<h3>Microarrays</h2>

Microarrays are, for a given genome, two lists of genes that "changed expression levels" between two states of a
cell.  Basicaly, the first list contains genes that were "active" during the first state, but not the second; and the
second list contains genes that were "active" in the second but not the first.  If a cellular
machine utilizes protein <i>X</i>, and <i>X</i> is in the first list, and if <i>X</i> is used in
only one cellular machine, then it would be reasonable to infer that you could say that the machine was
active in the first state, but not the second.  If one knew the regulons for a specific cell, it would go
a long way to suport extraction of insights from these microarrays.  On the other hand, if one had many,
many microarrays, and if the specific cellular machines for the cell are known, then one could make
substantial progress in uncovering the exact composition of the regulons that make up the cell.

<h2>Notes for the Enhanced Abstraction</h2>

The process of <b>expressing a gene</b> amounts to using the gene to produce the functional component of
a machine (a protein for a protein-encoding gene, and an RNA for an RNA-encoding gene).
The process of expressing a protein-encoding gene takes a gene (a string of DNA formed by concatenating a sequence of
regions from contigs) and producing a protein is normally thought of as taking place in two steps.
<b>Transcription</b> is the process of a specific machine moving along the contig and making a copy of the
gene as RNA.  This string of RNA is then <b>translated</b> by a separate machine.  The machine that performs
the copying of the gene into a string of RNA is called an <b>RNA polymerase</b>.  The machine to translate
the RNA into a protein, the <b>ribosome</b>, is made up of both proteins and RNA components.
<p>
Machines can be made up of both protein and RNA components, although most machines are built from
just proteins. Some of the most fundamental questions in biology relate to how life started and the steps
required to gradually enrich the basic machinery to the point where this magnificent information storage and
maintenance system based on DNA, RNA and proteins could have arisen.  There is much that can be inferred by
reasoning back from what we now observe and reasoning forward from the relatively little we know of 
what the early earth was like. One possible set of goals would be to first understand in detail the inventory
of components we now see in life forms, composing something analogous to a CAD/CAM system describing life forms.
Then, as a second step, to understand the sequence of transformations that led from some initial raw components
to initial life forms to those we have seen and characterized.
<p>
The need to allow occasional "nonstandard" characters in protein sequences and a loosening of the corespondence
between a gene and characters in the protein sequence it can be used to build results from the fact that
evolution has produced the existing genetic codes and they continue to evolve (either converging or diverging
depending on the outcome of basically random processes operating under selective pressure).
<br>

<h2>Notes on the Abstraction Extended to Support Regulation</h2>

There are two basically different regulatory mechanisms in the cell.  In one, you have a metabolic
network in which fluxes are tightly controlled by positive and negative feeback loops. This <b>metabolic
regulation</b> occurs very rapidly.  <b>Transcriptional regulation</b> occurs orders of magnitude more 
slowly.  It is just this transcriptional regulation that we consider in this extension.
<p>

As the cell changes state, regulons are activated or de-activated by
transcriptional regulators (either protein or RNA) binding to specific
sites in the DNA.  This model has the redeeming characteristic of
simplicity.  It is certainly the case that there are innumerable
important issues that it disregards (e.g., regulation based on DNA
packaging, due to small RNAs binding the RNAs produced by
transcription, etc.).  In forming any clear notion of transcriptional
regulation and how it is achieved, we will need to carefully separate
these different mechanisms, since they have fundamentally different
modes of control and operation.  We are arguing that the notion of a
protein or RNA being used to flip regulons on and off by binding to
control sites within the genome is a major form of regulation and
probably the right place to start any effort to formulate a useful
abstraction.

<h1>The Role of Bioinformatics in Supporting the Genomic Revolution</h1>

Within the growing genomics revolution, one can easily divide developments and
goals into those relating to advances in medicine and agricultue from those relating to
pure science.  Here we consider only issues relating to pushing advances in basic research.
Here is an overview of our perspective:
<ol>
<li> The different life forms that now exist were produced by an evolutionary process,
which leads to our view that comparative analysis is the key to understanding.  Biological
machines that exist in complex forms will often also still exist in simpler forms (usually
in simpler organisms).
<li> Unravelling exactly how a machine works is more easily done in simpler organisms.  They
are easier to work with, and it is easier to gather the data needed to support comparative analysis.

<li> This leads to the view that we should try to understand single-celled organisms to lay
the foundation for analysis of multicelluar organisms.

<li> The characterization of unicellular life will require access to orders of magnitude
more data than exist now (we have more-or-less complete genomes for about 1000 genomes, but
that represents a small fraction of a percent of extant single-celled life forms).

<li> The immediate basic steps that are taking place are roughly:
<ol>
<li> Attempt to formulate a growing list of abstract machines that correspond
to the many specific machines that implement te same goal.  These abstract machines (subsystems)
represent the basic units that make up life forms.

<li> Create protein and RNA families in which the members are all homologous (share a common ancestor),
remain similar over almost all of the sequence, and all implement a common function.

<li> Build alignments for each protein family, along with phylogenetic trees that represent
an estimate of the history of how these specific sequences evolved.

<li>Provide a computational framework to support continued maintenance and development of these
basic data types.
</ol>

<li> A limited number of groups have progressed to the point where they can create models of 
an organism that display predictive capabilities.  There are many forms of modeling.  In our view
it is important that we reach the state where we can routinely model states of the cell, transitions
between states, and metabolic characteristics of the cell.  We believe that it is now possible
to create fairly comprehensive representations of the metabolic networks of some bacteria. 
In these cases, we have substantial amounts of physiological data, the number of abstract machines
in the cell is fairly limited, and it is possible to do compare the predictions against observed results.


</ol>


<br><br>
We do not actually know what machines are present in a cell.  We are in the midst of a grand
effort to clarify which are there and what they do.  Reaching a point where we have a near
complete overview of the basic inventory is arguably the highest priority at this point (we ignore
the medical revolution and numerous other wonderful advances, but...).  

The formulation of subsystems as abstract machines
in which each row of the subsystem describes a specific cellular machine that is believed to be present,
represents a way to maintain a collection of estimates or assertions.
<p>
A <b>protein family</b> is defined to be a set of proteins that implement the same functional roles and
are similar over the entire lengths of the proteins.
<p>
We seek a situation in which each protein occurs in one or more subsystems and in a single protein family.
The computational tasks imposed by such a goal are obvious:
<ul>
<li>We need to consruct databases that implement at least the following entities:
<ol>
<li>cells (i.e., each cell must have an ID and a set of attributes),
<li>genomes,
<li>genes,
<li>proteins,
<li>functional roles,
<li>subsystems, and
<li>protein families.
</ol>
<li> We need to add support for developing clues to function by integrating data
from sources like proximity within the genome, fusions, etc.
<li>We need to support a framework for the development of populated subsystems.
<li>We need to construct decision procedures for membership in protein families.  Some 
of these procedures will be quite complex, although the majority of cases can be
handled by fairly general procedures.
</ul>


<h1> The Role of Abstraction in Setting the Stage for Software Development and Modeling</h1>

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