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<div align=center>
<h1>The Role of Bioinformatics in Interpretating Genomes of Unicellular Organisms:</h1> 
<h1>An Abstract View</h1>
<h2>by Ross Overbeek</h2>

<h2>What Is a Cell?</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.
By the term <b>compound</b> we refer to the normal notion of chemical compound. 

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.

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}).

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

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:
<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>
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.
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.
This 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:


                   *   *  *    * * ***  ***    ** *  ****  * ***  **** * ***

                *** **** **  ***     *** ** *  ****   *** ** *     * ***  * 

                   ***** ***** *** ****  * ** **  *   ** ***      ****** ***

                ********* **** ** ** **    ** *****  * ** ** ** ***   **   *

                ** ** ***   ***********         **  ****  ***    ** *  * ***

                ********   **     ***** ***** * * ** **            *   *****

                 *   **** * *********   ******* ***        * ***   ******** 

                *  ********    ***   ***  ***     * ** ** * * **   ** * ****

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

                  :*  * * :::*:* ****:**:. *:: * **:  *: *:***:*:***.:*  ***

                ***:****.***:****:: *:* ****..  *.*::.:****.: ***: .:

                .:***** **.:  :*.*** **::*:* * :**:.:*:

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?

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:
CLUSTAL W (1.83) multiple sequence alignment

                                   *.  . .      .: :.:  **..: ** .*  :  :

                       * :   : .:   :.   ..   :.. :  :         .*:   .     *

                ************.. :::..::  .    * : : :: *******:*.  .      :.:

                 :::*:.: * :*   : *:   .:  :   * ** ** :**  * *  :      * :.

                **** :*.:.*  *** *  ::      .  . .**:****:: ** ..  :***: :::

                 *.* *: . : :  . :       ::: :**:  ..*: * :  *.

                                      .    : :                :** * .  *  :

                        : * ****.** :::    :*     *  *            :  :   .:

seq3            FVSQHGNRGKPL
seq4            FMSGHLGA----
seq5            FIEKKAL-----
seq1            LMMNHQ------
seq2            YLLGK-------
                 :  :

<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.  

                          ,--------------------------------------------------- seq1
       |                  |
       |                  |
       |                  `---------------------------------------------- seq2
  |    |
  |    |             ,-------------------------------- seq3
  |    |             |
  |    |             |
  |    |-------------|
  |                  |
  |                  |
  |                  `------------------------------ seq4
  `---------------------------------------------- seq5

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.
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.

<h2>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.
<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?
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
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.
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.
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).
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.
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.
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.
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.
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.

<h2>Imposing 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.

Here is a short formulation of one way to do this:
<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>.
<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).
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.
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.
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:
<li>We need to consruct databases that implement at least the following entities:
<li>cells (i.e., each cell must have an ID and a set of attributes),
<li>functional roles,
<li>subsystems, and
<li>protein families.
<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.

<h2>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.
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".
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.


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.

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