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← Steering and Reprogramming Life

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Showing Revision 5 created 01/15/2019 by Paulo Henrique Ferreira.

  1. Understanding the causal origins and
    mechanistic principles for the behavior
  2. of an evolving system is one of the major
    challenges of our time.
  3. For example, how a protein may fold to
    become a functional piece in your body
  4. or how a drug may help against
    some disease.
  5. A causal system can be described by an
    algorithmic model evolving over time.
  6. The length of the shortest computer model
    is called
  7. the algorithmic information content of
    the system.
  8. That is how much computer code is needed
    to reproduce the object itself.
  9. The shorter the description, the more
    likely the system is causally generated.
  10. The longer its description, the less
    likely it is to be causally generated.
  11. A dynamic system can usually be
  12. as a network of interacting elements,
  13. such as interacting cells or interacting
    genes turning on and off other genes.
  14. What we want is to figure out the first
    principles driving a system,
  15. such as a genetic network representing a
  16. or the causes for the cell to behave in
    one way or another.
  17. Power approach consistis in finding a set
    of algorithmic models that can explain
  18. the structure of the system.
  19. When the system is just a random process
    and, therefore, not causally generated
  20. it can only be represented by a long
    descriptive model.
  21. However, systems rich in causal content
    can be represented by a short model,
  22. because they have a generating mechanism
    that evolves
  23. into any observable state of the system.
  24. To determine the causal content of
    an evolving system,
  25. we peform perturbations.
  26. In a network, for example, a perturbation
    can be deleting a node or deleting a link.
  27. We examine the effects of that
    perturbation, and evaluate how much the
  28. system became more or less random.
  29. In a random system, for example,
  30. a change will not have a major impact,
    because no part of the system can explain
  31. any other part of the system, and so
    the perturbation goes unnoticed.
  32. We can thus safely say that these
    systems cannot be reprogrammed.
  33. But, in non-random causally generated
  34. some changes will render them
  35. causal interventions in these systems make
    them reprogrammable.
  36. Moreover, if we remove an element and the
    system gets further away from randomness,
  37. we can conclude that the element is not
    part of the algorithmic content,
  38. and is unlikely part of the causal
    generating mechanism of the network.
  39. It is thus likely to be noise or part of
    another system.
  40. Alternatively, if we remove an element and
    the system moves towards randomness,
  41. it means that the element is part of the
    causal model that explains
  42. other parts of the system's evolution.
  43. But, if we remove an element and the
    system does not approach either
  44. randomness nor simplicity, the deleted
    element is non-essential in the
  45. explanation of the system and likely an
    element produced by
  46. the normal course of its evolution.
  47. Returning to gene regulation, what we do
    is to apply this concept
  48. and evaluate the contribution of
    every gene and gene interaction to the
  49. original network, ranking the elements by
    causal contribution.
  50. When this ranking is biologically
  51. these tools have demonstrated the ability
    to pinpoint markers of
  52. cell function, cell differentiation, and
    cell fate.
  53. This shows how these tools can properly
    profile systems elements, and steer,
  54. and reprogram systems, such as biological
  55. If these systems have some elements that
    move the network towards randomness,
  56. and other elements that move the network
    towards simplicity,
  57. we say these systems are more
  58. But, if a system can only move in one
    direction, then it is less reprogrammable.
  59. Stem cells will be able only to move
    towards a single direction
  60. towards a differentiated cell,
  61. while completely differentiated cells will
    be able only to move
  62. to the opposite direction.
  63. This causal calculus is better equipped to
    tackle the general challenge of
  64. causal discovery and science than more
    traditional tools,
  65. and is helping scientists better
    understand, and even steer,
  66. biological and synthetic systems.