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317  handled by fairly general procedures.  handled by fairly general procedures.
318  </ul>  </ul>
320  <h2>Microarrays and States of the Cell</h2>  <h2>States of the Cell</h2>
322  We wil think of a <b>regulon</b> as a set of subsystems.  A <b>state of the cell</b> is  The notion of <i>subsystem</i> was introduced as an <i>abstract machine</i> -- that is, as an
323  defined as the set of regulons that are operational at a point in time.  attempt to create a framework for understanding variations within specific celular machines via
324    a form of comparative analysis.
326    In any specific cell, sets of specific cellular machines are
327    switched on and off as units.  That is, they are <i>co-regulated</i>.  We will call such a set
328    of <i>co-regulated cellular machines</i> a <b>regulon</b> (note that a regulon is often a set containing
329    a single cellular machine).  A <b>state</b> of a cell will be defined
330    as the set of regulons that are operational at a point in time.  Thus, a state amounts to the set
331    of cellular machines that are operational at one instant.
332  <p>  <p>
333  A <b>consistent microarray</b> (for our purposes) is  If we think of a car as a bag of machines that interact to make it function, we might consider there
334  <ol>  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
335  <li> The ID of an experiment.  The experiment corresponds to two states of the cell, S1 and S2.  of some key "machines".  For example, "off" (the state in which the engine is turned off and the car is parked) and
336  <li> A list of proteins that are in in the regulons in S1, but not in those of S2.  "on" (the engine is running and the car is moving) might be viewed as a crude partitioning of the states into
337  <li> A list of proteins that are in the regulons of S2, but not in those of S1.  two "major states".
 A <b>real microarray</b> is just two sets of proteins.  We have some notion (e.g., an ID) for  
 each of two states of the cell, but no idea what regulons make up these states.  There is a  
 substantial error rate in the two lists of proteins (e.g., some of the proteins in the first list either were not in  
 S1 or they were in S2).  
338  <p>  <p>
339  The interesting research question is, given a large list of real microarrays, can you  Similarly, I believe that we should think about <i>major states of the cell</i> as being determined by the
340  attach sets of regulons to all of the state IDs, and then give a minimal set of changes to the data  functioning (or not) of a limited set of regulons.  The determination of these regulons, the major states,
341  in the real microarrays needed to convert them to consistent microarrays.  and how transitions between are managed all are now parts of the picture being filed in.
344    <h2>Microarrays</h2>
346    Microarrays are, for a given genome, two lists of genes that "changed expression levels" between two states of a
347    cell.  Basicaly, the first list contains genes that were "active" during the first state, but not the second; and the
348    second list contains genes that were "active" in the second but not the first.  If a cellular
349    machine utilizes protein <i>X</i>, and <i>X</i> is in the first list, and if <i>X</i> is used in
350    only one cellular machine, then it would be reasonable to infer that you could say that the machine was
351    active in the first state, but not the second.  If one knew the regulons for a specific cell, it would go
352    a long way to suport extraction of insights from these microarrays.  On the other hand, if one had many,
353    many microarrays, and if the specific cellular machines for the cell are known, then one could make
354    substantial progress in uncovering the exact composition of the regulons that make up the cell.

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