The previous E. coli gene regulatory
network example was mostly a static
example, although by way of original
analysis, one can think of artificially
evolving the network one step at a time
to see how the network can evolve over
time or as a result of interacting or
being perturbed by some other system from
the outside. But we can move to a more
dynamic example. In this case the network
associated to a type of cell that is
called T naive cell, which is associated
to the immune system and this is a cell
that has not yet been fully differentiated .
In this new regulatory network, we
started from a network representing this
undifferentiated state and followed it to
its differentiated state when it becomes a
differentiated TH17 cell, which is a specific
cell of the adaptive immune
system that helps keeping the organism
from getting, for example, an infection.
And it is related to all sorts of
interesting immune processes. So this cell
was analyzed in detail in paper published
in Nature, in 2013 and the data was made
available from data source. What we did
was to do exactly what we did for E coli
but now on three networks representing
the cell as a dynamical system evolving
from a non-differentiated to a differenti- ated
state. You can see on the figure A
how the algorithmic information of genes
distribute and how many of the genes are
negative and positive, meaning that they
can move the cell towards or away from
randomness. When we talk about the cell,
we are talking about its genetic network
representation, which is a good represent- ation
of the cell, as in it reminds many
if not most of its most basic processes,
leading to protein protection at metabol- ic
pathways. But of course it may not
represent every aspect of the cell. So on
figure A you can see how genes distribute
differently at every step. With the two
first cases closer to each other than its
final configuration. Now as you may
remember, we have been saying that systems that
have negative and also positive
elements are more reprogrammable because
as we found out, they allow us to move
the dynamical system qualitatively, namely
to the modified and attractor landscape by
increasing or decreasing the attractors
and therefore, the depth of such attractors. One can see how in the
differentiated state, colored in green, the cell distributes all its genes on the
positive side, meaning that it has reached some sort of, both dynamical but
also algorithmic stability, in line with all we saw in the previous unit.
Moreover, if we were doing any of this by
chance, we would not find any over representations of the genes that we
marked as negative or positive when performing a gene ontology
enrichment analysis. However, we did find
that genes related to processes identify
in this kind of immune cells were pinpointed by the algorithmic dynamics
analysis, as you can see on figure B. So in figure B, we have three plots
corresponding to each differentiating time
indicating how many genes identify as the
most positive or most negative appear in
the literature associated to the process
of differentiation. If we had not captured the right genes the distributions would
look flat as if we had chosen random genes out of all possible genes in the network.
But genes that are more positive or more negative appear with greater number in the
early stage and in the intermediate stage genes in the positive patch were highly
informative of differentiating processes.
And finally in the last stage, again, the distribution is far from uniform
indicating that those genes identified by algorithmic dynamics were involved in the
processes of differentiation of the cell.
In the next slide, we will explain how GO enrichment analyzing was done in some
detail. But in figure C, you can see that this distribution of genes that were
positive and negative and how they flipped from stage 1 and 2 and then became almost
all positive in the last stage, colored in blue.
Almost like locking the state of the cell when it reached its differentiative state.
Finally, on figure D, we can check the overall complexity of the network,
in which we see also suggestive by the algorithmic dynamics that the first
network somehow, received signals to start its differentiating process probably
captured in this increase of complexity in the second stage where the estimation of
algorithmic complexity increases accordi- ng to BDM and then decreases again once
differentiated.
Here is a Venn diagram in the form of a network, showing the genes in the square
nodes that have been identified in the literature from nine major papers shown
in black elliptic notes covering research on TH17 cells.
We had to do this because traditional gene ontology databases are not
specialized enough to look over literature specific to immune cells like
this one. Even that this is at the forefront of current research.
For the creation of this network I am choosing the literature on which it is
based. We teamed up professional immunologist.
This paper covers the majority of genes that have been associated to TH17 cells.
Linked genes in the figure are genes found in common between two or more papers.
Black lines show the number of genes found in common between every two papers
with the thickness denoting the size of the overlap that were used in the gene
enrichment analysis of the previous TH17 differentiation network.
But even more interesting to see we are doing what we are claiming to do, we
devise a third experiment after getting the expected results from highly curated
and validated gene regulatory network from E coli and then the TH17 network.
This other experiment involved taking the gene regulatory network of twenty one cell
types, mostly based on experimental results and therefore, of high quality.
We then applied not only the algorithmic information dynamic but also the
reprogrammability indexes, hoping that we would be able to tell in what stage of
differentiation every of these different cell types were.
The experiment makes a lot of sense if you think about it because all cells
ultimately come from the same very first cell we call an Embryonic stem cell,
denoted by the letters ESC, from were all other cells come from.
What we found was not only that the Embryonic stem cell was on top of
programmability measure, as you can see on both diagrams but also that another
type of stem cell called Hematopoietic stem cell, here denoted by the letters
HSPC, they are stem cells that give rise to all other blood cells, had also high
reprogrammability as it would be expected and for example, BMT cells followed
closely relatively close to each other conforming to the biological expectation
and then all other differentiated cells were on the other side of the
reprogrammability index with low algorithmic complexity.
So by taking this information, one can attempt to reconstruct an attractor
landscape, also commonly known as Waddington's energy landscape.
Where, similar to what we saw in the unit of dynamical systems, one can capture and
picture an attractor landscape of an Embryonic stem cell and place its
non-differentiated state as being on top of a hill in a stable position, where
moves can make it differentiate into different cell types by falling into the
different attractors represented by valleys that are also more stable than
the hills. So I hope you see how interesting this application of
algorithmic information dynamics is to real world, in particular to molecular
biology and genetics. In the next lectures we will see other applications to
other areas such as evolution, cognition and machine learning among other topics.