Computers Making Computers?
Various efforts to integrate biological knowledge into
networks of interactions have produced a lively microbial
systems biology. Putting molecular biology and computer
sciences in perspective, we review another trend in systems
biology, in which recursivity and information replace the
usual concepts of differential equations, feedback and
feedforward loops and the like. Noting that the processes of
gene expression separate the genome from the cell machinery,
we analyse the role of the separation between machine and
program in computers. However, computers do not make
computers. For cells to make cells requires a specific
organization of the genetic program, which we investigate
using available knowledge. Microbial genomes are organized
into a paleome (the name emphasizes the role of the
corresponding functions from the time of the origin of
life), comprising a constructor and a replicator, and a
cenome (emphasizing community-relevant genes), made up of
genes that permit life in a particular context. The cell
duplication process supposes rejuvenation of the machine and
replication of the program. The paleome also possesses genes
that enable information to accumulate in a ratchet-like
process down the generations. The systems biology must
include the dynamics of information creation in its future
developments.
The quantum
teleportation experiments have
demonstrated that information can be viewed as a fundamental
irreducible property of physics (informationalism).
Systems biology is moving in that same direction, as viewing
cells as computers with machinery and software makes it possible
to view information as a fundamental category of nature and all
future developments of systems biology can include this concept
when looking at cells.
There are many interesting passages in this article. A few of
these are going to be highlighted for discussion.
Historically, systems biology follows on from molecular
biology, a science based on many concepts more closely
linked to arithmetic and computation than to classical
physics or chemistry. Molecular
biology relies heavily on concepts such as ‘control’,
‘coding’ or ‘information’, which are at the heart of
arithmetic and computation. To
accept the cell as a computer conjecture first requires an
exploration of the concept of information, in relation to
the concept of genetic program.
Cellular processes are exquisitely controlled and carried out by
remarkable biomolecular machines. The software needed to
coordinate these processes is located in a fairly optimal
genetic code that is optimized
for evolution and maintains its own functional integrity.
The Austrian mathematician Kurt Godel showed that arithmetic
(the science of whole numbers) can make statements about
itself. To substantiate this remarkable claim, which implies
that just manipulating whole numbers with the rules of
arithmetic can generate novel information, G¨odel used a
simple trick. He coded the words used in Number Theory as
integers (e.g. four, which is quatre in French, vier in
German and tessera in Greek, can be coded by 4) and used the
corresponding code to translate propositions of arithmetic.
This generated a large whole number, which could be
manipulated by the rules of arithmetic, and after a sequence
of operations, this manipulation generated another whole
number. The latter could be decoded using the initial code.
Godel’s trick was to drive the sequence of operations
modifying the initial statement, to lead to a very
particular conclusion. When decoded, the manipulated
sequence translated into a particular proposition, which,
briefly, stated: ‘I am impossible to prove’. In other words,
arithmetic is incomplete, i.e. some propositions of
arithmetic can be understood as valid; yet they cannot be
proven within the frame of arithmetic. But this
‘incompleteness’ can also be seen as a positive feature; it
is what allows the creation of new information – in Godel’s
case, the statement of a fact of which the world was
previously unaware. In his book, Hofstadter showed that the
genetic code, which enables the world of nucleic acids to be
translated into the world of proteins, which in turn
manipulate nucleic acids, behaves exactly as Godel’s code
does. This implies that manipulating strings of symbols, via
a process that uses a code, can generate novel information.
Of course, in the case of nucleic acids and proteins, there
is no Godel to drive the process, and no need for one: while
Godel knew what he was aiming at, living systems will
accumulate information through recursivity, without any
design being required. We only perceive a design because the
end result is familiar to us, and thus seems more ‘right’
than any other possible result. But what we commonly term
the ‘genetic program’ because it unfolds through time in a
consistent manner is not a programme with an aim – it is
merely there, and functions because it cannot do otherwise.
Why can't the function of the program be to actively manipulate
information as a means to an end... self-replication and
preservation. Later in the article something similar to this is
actually suggested:
The reluctance of investigators to regard information as an
authentic category of Nature suggests that, at this point in
the present review of the literature, it may still be
difficult for the reader to accept that a cell could behave
as a computer. Indeed,
what would the role of computation be in the process of
evolution? We have already provided some elements of the
answer to the question: Turing showed that the consequence
of the process of computation along the lines he outlined is
that his machine would be able to perform any conceivable
operation of logic or computation by reading and writing on
a data/program tape. Stated otherwise, and in a way that is
easier to relate to biology, the machine manipulates
information and, because arithmetic is incomplete [as
illustrated in the introduction above (Hofstadter, 1979)],
it is able to create information. The machine is therefore
in essence unpredictable (Turing, 1936–1937), but not in a
random way – quite the contrary, in a very interesting way,
as lack of prediction is not due to lack of determinism, but
due to a creative action that results in novel information. If
the image is correct, then it shows that living organisms
are those material systems that are able to manipulate
information so as to produce unexpected solutions that
enable them to survive in an unpredictable future (Danchin,
2003, 2008a).
There we go, organisms can be viewed as entities that are able
to manipulate information as a means to an end. Why would it be
difficult to accept that cells to behave like computers? Yet,
cells are capable of more than computers, e.g. self-replication
and autonomous manipulation of information.
A form of endogenous adaptive mutagenesis (EAM) is also
being alluded to in the article:
Living organisms are, therefore, infinitely far removed from
the clockwork mechanicism that superficial opponents of
molecular biology associate with the widespread analytical
stance they call ‘reductionism’ (Lewontin, 1993). It is
important to emphasize here that, in the Turing machine, the
machine is not only allowed to read the program but also to
write on it. If, then, the conjecture of the cell as a
Turing machine is valid, apparent paradoxes such as the
controversial ‘adaptive mutations’ that enable the cell to
invent novel metabolic pathways should not be unexpected
(Cairns et al., 1988; Danchin, 1988b).
There is also room for drawing parallels between evolution,
memetic algorithms and designed molecular docking programs.
Finally, we must note that the algorithmic approach,
presented when considering the genetic program as an
authentic program in a Turing machine (Danchin, 2003),
identifies two completely different levels: the level of the
program and the level of the machine.
The article continues to discuss at length the parallels between
our own created information processing systems (computers) and
molecular processes fundamental to life. The article is sure to
provide information for many more interesting blog discussions.
