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Title:
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Description:
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Understanding the causal origins and
mechanistic principles for the behavior
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of an evolving system is one of the major
challenges of our time.
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For example, how a protein may fold to
become a functional piece in your body
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or how a drug may help against
some disease.
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A causal system can be described by an
algorithmic model evolving over time.
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The length of the shortest computer model
is called
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the algorithmic information content of
the system.
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That is how much computer code is needed
to reproduce the object itself.
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The shorter the description, the more
likely the system is causally generated.
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The longer its description, the less
likely it is to be causally generated.
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A dynamic system can usually be
represented
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as a network of interacting elements,
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such as interacting cells or interacting
genes turning on and off other genes.
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What we want is to figure out the first
principles driving a system,
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such as a genetic network representing a
cell
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or the causes for the cell to behave in
one way or another.
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Power approach consistis in finding a set
of algorithmic models that can explain
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the structure of the system.
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When the system is just a random process
and, therefore, not causally generated
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it can only be represented by a long
descriptive model.
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However, systems rich in causal content
can be represented by a short model,
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because they have a generating mechanism
that evolves
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into any observable state of the system.
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To determine the causal content of
an evolving system,
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we peform perturbations.
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In a network, for example, a perturbation
can be deleting a node or deleting a link.
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We examine the effects of that
perturbation, and evaluate how much the
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system became more or less random.
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In a random system, for example,
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a change will not have a major impact,
because no part of the system can explain
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any other part of the system, and so
the perturbation goes unnoticed.
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We can thus safely say that these
systems cannot be reprogrammed.
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But, in non-random causally generated
systems,
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some changes will render them
unrecognizable,
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causal interventions in these systems make
them reprogrammable.
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Moreover, if we remove an element and the
system gets further away from randomness,
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we can conclude that the element is not
part of the algorithmic content,
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and is unlikely part of the causal
generating mechanism of the network.
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It is thus likely to be noise or part of
another system.
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Alternatively, if we remove an element and
the system moves towards randomness,
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it means that the element is part of the
causal model that explains
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other parts of the system's evolution.
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But, if we remove an element and the
system does not approach either
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randomness nor simplicity, the deleted
element is non-essential in the
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explanation of the system and likely an
element produced by
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the normal course of its evolution.
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Returning to gene regulation, what we do
is to apply this concept
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and evaluate the contribution of
every gene and gene interaction to the
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original network, ranking the elements by
causal contribution.
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When this ranking is biologically
interpreted,
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these tools have demonstrated the ability
to pinpoint markers of
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cell function, cell differentiation, and
cell fate.
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This shows how these tools can properly
profile systems elements, and steer,
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and reprogram systems, such as biological
cells.
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If these systems have some elements that
move the network towards randomness,
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and other elements that move the network
towards simplicity,
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we say these systems are more
reprogrammable.
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But, if a system can only move in one
direction, then it is less reprogrammable.
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Stem cells will be able only to move
towards a single direction
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towards a differentiated cell,
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while completely differentiated cells will
be able only to move
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to the opposite direction.
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This causal calculus is better equipped to
tackle the general challenge of
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causal discovery and science than more
traditional tools,
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and is helping scientists better
understand, and even steer,
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biological and synthetic systems.