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INFORMATION OF THE CHASSIS AND INFORMATION
OF THE PROGRAM IN SYNTHETIC CELLS
© 2009 Antoine Danchin
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Open Access:
World renowned Professor Antoine Danchin is Head of the Unit, Founder of the HKU-Pasteur Research Centre, Director of the Genomes and Genetics Department (Professor IP, Research Director CNRS) |
Reference of the original publication Abstract Synthetic biology aims at reconstructing
life to put to the test the limits of our
understanding. It is based on premises similar
to those which permitted invention of computers,
where a machine, which reproduces over time,
runs a program, which replicates. The underlying
heuristics explored here is that an authentic
category of reality, information, must be
coupled with the standard categories, matter,
energy, space and time to account for what
life is. The use of this still elusive category
permits us to interact with reality via construction
of self-consistent models producing predictions
which can be instantiated into experiments.
While the present theory of information has
much to say about the program, with the creative
properties of recursivity at its heart, we
almost entirely lack a theory of the information
supporting the machine. We suggest that the
program of life codes for processes meant
to trap information which comes from the
context provided by the environment of the
machine. Keywords algorithmic complexity; logical depth; physical
entropy; self-organisation; field; context Abbreviations 3D: three-dimensional; OS: Operating System; Reconstructing life is central to synthetic
biology’s efforts, as a means to try and
understand what life is. I explore the consequences
of the model of the cell-as-a-computer, where
the “chassis” is explicitly separated from
the program, as in a computer [1]. As a heuristics,
information is viewed here as an authentic
category of reality. I organise in what follows
a tentative philosophical reflection on constraints
met by synthetic biology around four themes
which I see as a true revolution of human
thinking, the shift from a mechanistic view
of the world to an algorithmic view, with
the result that living organisms can be understood
as information traps. The modern reflection on information began
with Hilbert's problems at the beginning
of the XXth century. One of his questions was whether
arithmetics, the mathematics of whole numbers,
was simply a tautology, i.e. its conclusions
could be automatically drawn and reached
from its premises. The response brought about
by Gödel and successors after 1931 recognised
that arithmetic was incomplete, in that it
could bring about conclusions which were
understandable only when taking a point of
view from the outside of arithmetics. Arithmetics
incompleteness establishes that whole numbers
theory must be separated from its meaning for the human creator and observer. Briefly,
arithmetics is associated to two levels of
information: the self-sufficient information
carried by strings of symbols, and the information
carried by the context: language and civilisation,
or more generally, by the biological entities
we name Homo sapiens. The latter provides interpretations of
the demonstrations and theorems created by
the axioms and definitions of number theory
but the corresponding information has not
yet been theorised. Based as is arithmetics on strings of symbols,
the alphabetic metaphor of the genetic program
sits at the centre of several theses which
I try to make explicit, via a rapid travel
through history with the aim to place the
category information within synthetic biology:
1/ A thousand-year-old metaphysical/ontological
thesis, where I discuss the relationships
between shape, form and the process of in-formation;
2/ An epistemological thesis exploring how
information links models of phenomena to
reality in a situation identifying two levels
of information, the information of the model,
and the meaning of the model; 3/ The exploration
of extant theories of information as a prerequisite
to understand the concept of genetic program
in synthetic biology; 4/ A conjecture proposing
the need to create a new theory, that of
information of the chassis (machine) or,
why the brain is not a computer. Metaphysical thesis: shape, form and information When searching for life outside Earth we
look for “unusual” shapes, not commonly associated with standard chemistry
and mineralogy. We restrict our identification
of forms, looking first for the 3D architecture
of the “chassis” which compartmentalises
the living entity (see for example the beginning
of Monod’s Chance and Necessity [2]). Typically we draw aside crystalline
shapes, and look for more complex shapes
such as those of spheroidal or tubular objects.
Yet, we need more to recognise life, as drawing
our conclusions from the geometry of shapes
can be misleading (this happens when an artefact
is interpreted as a biological entity’s signature
[3]). Furthermore, the form of living organisms
does not reduce to their static shapes, it
implies dynamic processes. From the early times of philosophy, life
was identified as a phenomenon connected
to recognisable autonomous but not independent
categories. To account for all phenomena, Aristotle recognised
ten categories: An essential step in understanding reality
required construction of some entanglement
of these categories, a process which progressively
reduced them to four: matter, space, time,
and subsequently energy. A remarkable achievement
was reached when Einstein combined them together
in a surprisingly concise equation, E = mc2. Yet, it was obvious that these universal
categories do not account for many phenomena:
no one has been able, for example, to derive
the crystal lattice of a mineral as simple
as sodium chloride from the equations of
microscopic physics [4]. Imagine the challenge
for synthetic biology! Even understanding what became the modern
category matter was never simple: matter displayed itself
as an immense variety of entities (shapes,
processes, phase transitions and even transmutations…).
One could think about substance Already asked by the presocratic philosophers the question of the nature and origin of form was renewed by Aristotle. After him, the question kept developing with Greeks in Alexandria and southern Italy, arab and scholastic philosophy from the fall of the roman empire till the fourtheenth century. Between Aristotle and the present time, I retain John Scotus Eriugena because of the way he tackled the problem of creation [6]. A neo-platonist,Eriugena divided Nature into four species: (1) Nature
which creates and is not created; (2) Nature
which creates and is created; (3) Nature
which does not create and is created; (4)
Nature which neither creates nor is created.
To make a long argument short, asking questions
this way leads to five modes of opposition,
which introduce a hierarchy in natural entities,
and in particular in living beings. As in
the platonistic tradition, the material world
of our experience is composed of ideas clothed
in matter. However, Eriugena attempted to
reconcile Plato with Aristotle, discussing
Aristotle’s ten categories. Time and space
were discussed as central for human perception
of phenomena, matter is without form or limit,
but it needs an external agent to take form,
it needs to be in-formed. Interestingly,
God, as defined by scriptures, escapes all
categories except one, relatio The second name I keep in this sequence is Averroës. His commentaries of the metaphysics of Aristotle
had immediate and lasting success. I retain
sentences of his Tahafut al Tahafut: « Matter only becomes in so far as
it is combined with form. Everything that
comes into being comes into being from something
else, and this must either give rise to an
infinite regress and lead directly to infinite
matter which is impossible, even if we assume
an eternal mover, for there is no actual
infinite; or the forms must be interchangeable
in the ingenerable and incorruptible substratum,
eternally and in rotation. » [7]. That substratum, substance, was difficult
to make explicit. First split into the four
elements, fire, air, water and earth, and,
subsequently seen as atoms (which, despite
their name, recently split into further particles),
it needed association with something which
makes the root of variety in the world, form How could form combine with matter? A variety of animas (souls and spirits) were invented to account for the birth, development and conservation of movement, until energy came in. This category permitted some entanglement of matter with space and time, and long took the role of the animating principle needed to account for life (see mesmerism and its “animal magnetism” and “positive energy” in small talk or the vocabulary of sects today). Many further categories required to account for life were discussed for centuries in the western world, most often based on the assumption that reality had to fit with explicit revelation by God of its characters, as written in the Scriptures. Thomas Aquinas used Averroës' Grand Commentary
of Aristotle’s Metaphysics as his model.
In his Summa theologica he analysed the questions of Trinity and
of creation, showing that standard reasoning
tells us that creation is related with in-formation,
placing relationships between all kinds of entities (including
abstract entities) at a central position. These analyses may be condensed in the question
asked by the Pythia in Delphi: “I have a
boat made of planks of wood. The planks are
progressively replaced as they rot away.
After some time, all have been replaced,
none of the original ones remain: is it the
same boat?” [8]. To understand what life
is, we need to understand the relationships
between entities recognised as belonging
to life, whether they are material, processes,
or abstract, such as language. And we need
to try and understand the process of in-formation (which we would in the modern terms name
creation of information, noting that in the evolution of languages
redundancy is a ubiquitous trend [9]). Information links models with reality Belonging to reality we cannot behave as
outsiders contemplating the world. Understanding
information asks us to investigate the way
science is constructed. Presocratic Greek
philosophers recognised that our limitations
in understanding truth The importance of models to understand reality
triggered the creation of axiomatics, mathematics
and logics. This effort was well fitted to
the Renaissance trend to replace Aristotle
by Plato, removing the thought of the former
to the “dark centuries” of Middle Ages. At
the heart of platonistic philosophy, the
shadows of mathematical archetypes had to
be discovered by persons who were illuminated
by their truth. This attitude placed mathematics
in the world of idealities, suggesting that
mathematical certainties existed separately.
The medieval reflection on in-formation was
soon replaced by a geometrical view of combinatorial
creation of forms associated to the general
structure of space, initially studied in
one, two or three dimensions and later generalised
to all kinds of dimensions. In parallel,
and following a medieval trend of arabic
mathematics, arithmetics and number theory
slowly emerged as algebraic equations. Models
recognised as of the highest quality were
mathematical models, developing on their
own, independently of reality with their
in-built consistency (information). Trying
to match models with reality allowed scientists
to progress by producing better and better
adequation with reality [11, 12]. However,
the match between models and reality could
never be direct (a mathematical model of
an aeroplane does not fly). It rested on
interpretations (processes rooted in culture and language,
thus associated to a property that we might
name context and linked to a research programme [13, 14]). If constructing models while confronting
them to reality defines science [15], then
the effort to establish an explicit demarcation
between science and non-science is dominated
by a particular category of reality, information
again, using the word with all its fuzzy
connotations [16]. Defining what science
is emphasises two types of information, information of the (mathematical) model and information of the context. Both types place the old category, relatio, at their heart, but only the former has
yet been theorised, in the chaining of axioms
and definitions, demonstrations and theorems
[8]. The way synthetic biology is developing illuminates
these points. Starting from preconceived
biological views, it abstracts specific features into axioms and definitions,
and builds up models, whether mathematical
or experimental (e.g. engineering models)
[17]. The models unfold with their own rules
of consistency: a demonstration in mathematics, yielding a theorem, a computer output in a simulation, a genetically modified cell in an experiment…
Subsequently one goes back to reality by
proposing a concrete instantiation of the output, predicting a particular phenomenon.
This prediction is of two major types: Either
the prediction of a novel, previously unknown
or unrecognised entity (a structure, a process,
a metabolite…), or that of a particular behaviour
of reality, which should manifest itself
along lines predicted by the model (Figure).
A model is (temporarily!) valid when all
its predictions are recognised in actualisations
of reality. Typically, in synthetic biology
bacteria have been constructed which display,
as expected, some type of multistable behaviour
or oscillations [18] or phages with artificial
regulatory regions have been shown to display
the ability to grow on cells [19]. In neurosciences
the basis of neuromimetic networks rests
on a vast number of works where selective
processes play a central role [20, 21]. However, because the model is not reality,
this ideal outcome never develops for a long
time. Even when we produce a new entity not
recognised before the model's construction
— a great success, comes a time when a phenomenon
does not fit the model’s predictions. To
proceed with our example: in bacteria, bistability
is not stable in time [22]. Initial attempts
to solve the contradictions between model
predictions and observed phenomena do not immediately discard the model. The common
practice we witness in synthetic biology
is re-interpretation of the instantiation process that matched
the model to reality. Typically: “exceptions
make the rule”, or “this is not exactly what
we meant, we need to focus more on this or
that feature”… This polishing step permits
the context of the model and its associated
phenomena to be defined as accurately as
possible. It marks the moment when technically
arid efforts such as normalisation, defining
a proper nomenclature, a database data schema
have a central role. We witness this today
in synthetic biology in the standardisation effort of the community [17]. Despite all
efforts to reconcile predictions and phenomena,
the inadequacy between the model and reality
becomes insoluble. This contradiction implies
that we need to reconsider the axioms and
definitions upon which the model has been
constructed, triggering a spiral of further
models, making science as we know it. As
always with exploration, this exploratory
attitude meets resistance: most of our contemporaries
would be happy to be believers, and forget about the impossible but necessary
quest of truth [11]. This may explain both
the hype and the reluctance to accept synthetic
biology. In the subsequent inflation of models there is a hierarchy. A mathematical demonstration is perceived as the ultimate proof [16]. This justifies the huge number of mathematical models published in systems and synthetic biology. Do they result in non-trivial predictions? I am afraid that, more often than not, most models are “retrodictions”, finding what is already well known (how often do metabolic models “discover” the Krebs cycle?), rather than predictions. Indeed, assessing the interpretation of postulates which have not been expressed in a precise way has deep consequences, including in mathematics, which illustrates the importance of the category information, connecting it with the standard categories of reality (time in particular). Deep features of axiomatics were understood when we discovered that something taken for granted was overlooked. Zermelo’s axiom of choice (given any two sets, one set is in one-to-one correspondence with some subset of the other: this looks trivial, but is not) is a famous example of this situation. Similarly, and in line with the Pythagorean / Platonistic tradition, we accept synchrony in the way we use mathematics, making it independent of time: when reasoning by recurrence, if we show that something is true for n+1, knowing it is true for n, this will be valid, whatever the size of n. We take for granted very large numbers eventhough
it will be impossible to reach them in any
realistic time frame. This implies that there
is no time involved (no computation) to access
them, assuming that the nature of mathematics
does not change if n is very large. What would happen if we modified
this axiom? Non-standard analysis explores
our limitations if we accept that the behaviour
of mathematics changes for infinitely small
or infinitely large numbers (an effort that
permitted Leibniz to invent differential
equations) [23]. This is mentioned here as
another example of the fact, well established
by Ruelle [24], that mathematics do not exist
outside reality [25], but belongs to it. The present status of information in synthetic
biology Most developments of synthetic biology consider the genetic program as an algorithm, implicitly assuming that the cell behaves as a computer, a machine manipulating information. I will not repeat the argument meant to justify
the model of the cell as a Turing Machine
[1]. Suffices it to say that this implies
the existence of two entities, associated
via a read/write process. A machine is moving a device that carries a support
with a linear string of symbols written in a finite
alphabet; the data of the string of symbols, read
by the machine, triggers its future actions.
The focal point of representing the atom
of life, the cell, as a Turing Machine, assumes
the physical separation between machine (“chassis”) and data/program, represented by one or several linear strings
of symbols. The crux of the model is that
one should be able to isolate the entity
carrying the program, put it back in a recipient
host, and observe that the program in its
new location displays phenomena specific
of the information it carries. Beside experiments
showing that pieces of program can be handled
by cells (viruses and horizontal gene transfer),
experiments produce results consistent with
the model: 1/ Animal cloning [26] is now
commonplace; 2/ The genome of a Mycoplasma
species M. mycoides, was transplanted into another species,
M. capricolum, and after several rounds of reproduction
(reproduction of the machine and replication
of the program, see below) the host species
was replaced by a colony of the donor genome [27]. This latter experiment is so
important conceptually that it is essential
for synthetic biology that it is reproduced
in many laboratories. Yet, this might not
satisfy us that the model is adequate to
represent reality. Three main lines of reasoning
argue against the cell-as-a-computer model.
1/ The first counterargument explores the
concept of Operating System (OS) [28]. Because
the machine is separated from the program,
a subset of the program must be devoted to
the interaction with the machine and its
“users” (in the most general sense) [1].
If a particular routine is meant to reproduce
the machine, then a subset of the program
must be somehow linked to the architecture
of the machine. Analysis of the genes giving
bacteria their shape showed that there is
indeed an unexpected coincidence between
gene clustering in genomes and shape of bacteria
[29]. In multicellular organisms, the distribution
of control genes, the homeogenes, parallels
the body plan: changing the order of some
homeogenes in the chromosomes changed the
shape of the organism, putting organs in
the place of others [30]. Rather than an
objection, the existence of a correlation
between the organisation of the program and
the architecture of the organism fits a prediction
of the model. 2/ The second counterargument is that the
program is carried by some material structure,
bringing about contextual information. However,
this is true in computers as well: the material
support of the program has its saying in
permitting the machine to run properly. Different
machines may be driven by the same program
on different supports. Thus, even the cloning
experiment, which does not involve naked
DNA but a whole nucleus, with its envelope,
its proteins and its RNAs, is not different
from a material support of a program in a
computer. Indeed, nocturnal animals use chromatin
in the nuclei of neurons using the retina
in an extraordinary way. Their retina can
detect one unique photon. Yet, the photon
receptors are located behind neurons, which
absorb or diffuse photons rather than preciously
conserve them. When light is dimmed, the
chromatin changes transcription and reorganises
in such a way that its material behaves as
a lens, focusing photons on receptors located
behind the neurons [31]! This novel function
for DNA, which has nothing to do with its
role in carrying the genetic program, shows
that another type of information has to be
taken into account. In the same way, in many
computers the support of the OS belongs the
casing part of the chassis. 3/ A third counterargument is that many rules
prescribe the organisation of the cell soma,
reflecting a large amount of information
unrelated to the information in the program.
Quite true, but this is true again for computers
as well. The design of the interfaces, the
microprocessors and the energy supply of
the machine require much information. In summary, two types of information (coupling
of a particular form – not simply shape –
with matter, energy, space and time), information of the chassis (casing + metabolism) and information of the program are associated together in a cell [32].
A synthetic cell needs the association of
a chassis developing metabolism (not a simple 3D casing) and a program similar
to that found in computers. The conclusions
of Dyson’s argument on the double origin
of life, with reproducing metabolism predating
replication are therefore a pre-requisite
for synthesis of life [33]. This dichotomy
is visible in present synthetic biology,
with a fairly clear separation between those
who study the chassis (and are often also
interested in the origin of life) [34, 35]
and those who think that life is essentially
due to the genetic program, organising their
activity around construction of program biobricks, or even as complete genomes [36]. Information of the program The study of the genetic program as a text, applying accepted rules of the theory of information [37, 38] to its analysis [39] resulted in the emphasis placed on DNA in synthetic biology. Schneider created his famous “logo” representation of sequences [40] in a model of molecular machines based on Shannon’s information [41, 42]. His work was based on the intuition that creation of information was consuming energy [42]. Furthermore, it assumed that the data has no meaning (hence no “value”), and could be characterised purely by analysing the probability of presence of a given symbol in the sequence, generating its logo [40]. A similar trend is visible in the way information is used in the mass media. It is current writing — because all kinds of signals can be digitised — that everything has an information coded in sequences of (0,1), restricting the concept of information to that particular view of sequences of symbols, and forgetting about in-formation (creation and accumulation of information, or a value associated to an information). The common feature of this conceptualisation is dematerialisation: the corresponding information becomes an abstract entity, which can be manipulated using mathematic tools. Yet pure abstraction is obviously inaccurate in terms of what we would like to name information. Messages without meaning (random messages) are without value. "O singe fort" in German has a
meaning totally different from that in French
[43]. Can we see, even within the digitisation
(or binarisation) paradigm, whether we should
go further? The soviet school of electronics
following Andronov, Kolmogorov, and the Americans
Chaitin and Solomonoff constructed formal
models of information vs chance by considering sequences of symbols
as the result of an algorithm. Any sequence
of symbols has some algorithmic complexity: the length of the shortest program generating
the sequence. A repeated sequence of 2n bits 0101010… is coded by a simple program
of the type: BEGIN DO [1,n] PRINT 01 RETURN
END. For n large, the program is much shorter than
the sequence. In contrast, if the sequence
is random (this is proposed as a definition
of randomness), the only way to get the sequence
is BEGIN PRINT <sequence> END, i.e.
a program with a length similar to that of
the sequence. Algorithmic complexity has been related to
Shannon’s information [38] and to physical
entropy: “Algorithmic randomness provides a rigorous,
entropy-like measure of disorder of an individual,
microscopic, definite state of a physical
system. It is defined by the size (in binary
digits) of the shortest message specifying
the microstate uniquely up to the assumed
resolution. Equivalently, algorithmic randomness
can be expressed as the number of bits in
the smallest program for a universal computer
that can reproduce the state in question
(for instance, by plotting it with the assumed
accuracy). In contrast to the traditional
definitions of entropy, algorithmic randomness
can be used to measure disorder without any
recourse to probabilities” [44]. This success led many to think that we had
a final Theory of Information, which could
tell us what information is. FR"> However, we can point out a first difficulty
here. We know of an infinite set of transcendent
numbers, such a How do we have access to the information
of the genetic program? Practice of computation
is fairly old (well before al’Khawarizmi algorithms, Erastothenes’ sieve is a familiar
example) but we had to wait for Pascal's
computing machine, and for Lovelace and Babbage
Analytical Engine to reach today's situation,
with the basic concepts proposed by Turing,
von Neumann and others, coupling the machine
and the program, via an OS managing the “housekeeping”
functions of the machine. The functions coded
by the genetic program are the result of
a very long evolution. And if we keep the
algorithmic metaphor, because DNA comes from
DNA comes from DNA… in an endless replication
process, the nucleotides in the sequence
have considerable logical depth. As with computer OSs, the housekeeping program
is abstract and general, yet its concrete
implementation, resulting from billions of
years of evolution, makes that several OSs
may coexist, revealing again two kinds of
information, information of the program and
information of the context in which the program
is expressed. This has considerable consequences
for synthetic biology: cellular functions
can be general and ubiquitous, whereas there
is no reason why they should always be performed
by structurally related objects. Overall,
living cells display similar abstract features,
and the genetic code argues for universality.
Yet, Woese uncovered a significant discrepancy
between two unicellular classes, the Archaea
and the Bacteria [46]. To identify ubiquitous
functions operated by non-ubiquitous structures
one had to devise an operational strategy,
based on the concept of gene persistence (tendency of a given gene to be present
in a quorum of species) [47]. Different structural
entities with common functions in different
bacterial clades were indeed characterised
[48, 49]. A structure is therefore recruited
for a particular function, dependent on the
context in which it operates. The context creates
the function.
A way forward: the information of the machine/chassis
Emphasis on the idea of information as meaningless
strings of symbols [37], restricted our thought
to that very limited feature of reality.
In the Turing Machine, there is a machine. While its actions are explicit, nothing
is said about its innards, at least when
mathematicians analyse its behaviour. This
is no longer so when engineers build up computers.
The same is true for the chassis in synthetic
biology. Not only does one need to make a
machine that performs the actions of the
cell/Turing Machine but this machine needs
to be implemented in the real world [32].
It must be made of explicit matter, its actions
need to be energised and there must be an
Environment / Machine interaction with sensors,
transporters, adhesins, safety valves [50]…
The information of the chassis provides the
relevant context which allows it to read
the program and interpret it into actions
(including modifying the program). Even systems
which “self-organise” do not organise by
themselves, but do so only when placed in
proper context, which drives organisation
(the DNA double helix does not form in dimethylformamide)
[4]. This type of information has a huge
variety of properties: shapes, dynamics and
fluxes. It displays relationships between
components of the machine, and between the
machine and the environment. It expresses
a situation. It has characteristics which are somewhat
similar to those of a field but also of a graph. Typically, what we name epigenetics carries over chassis-type information. A
great many works dealing with the study of
the brain [21], or of cognition [51, 52]
has taken into account this type of information.
It is also at the root of much work on artificial
life, learning and memory where reproduction,
rather than replication is the explicit goal
(see e.g. [53]). But there is not yet, despite
many advances, explicit consistent theories
of the corresponding information [32]. That we might code this information after
digitisation does not place it automatically
within the realm of understandable or valuable
sequence information, as the very process
of digitisation is only efficient knowing
which type of Turing Machine would read out
the corresponding sequence. This can be shown
as follows. Algorithmic complexity was meant
to define what chance is, because chance
is the reference that permits definition
of physical information: a random sequence
displays the highest complexity [38, 44].
However this definition does not hold, as
it is context-dependent [4]. Here is an open
conjecture (a preliminary version has been
proposed in a different context by Wolfram
[54] and it is interesting to follow the
analysis of In the definition of logical depth, we have
implicitly used a property of algorithms,
recursivity. In 1931 Gödel constructed a recursive algorithm,
which, when decoded, translated into a particular
proposition, which, briefly, stated: “I am
impossible to prove”. Moving form one context
to another one, recursivity created a novel information, the statement of a fact of which the world
was previously unaware. The genetic code,
which enables nucleic acids to be translated
into proteins, which in turn manipulate nucleic
acids, behaves exactly as Gödel’s procedure
does [1, 8]. The consequence of this demonstration is
that a purely deterministic system, with
known initial conditions, may have an entirely
unpredictable outcome. By contrast with the
mechanistic philosophy, even with its more
modern appendices such as feedback and feedforward
loops, recursivity brings about novelty not
because we fail to grasp all initial conditions
of a particular phenomenon, but because it
can be only understood a posteriori, after
it has unfolded in space an time. This implies
that synthetic biology, when it takes recursivity into account, develops in a world that is totally irreducible
to the world of systems biology, which remains
an elaborate episode of the study of mechanistic
automata. An important consequence is that
what we commonly term the “genetic program”
because it unfolds through time in a consistent
manner is not a programme with an aim (we
would not be able to predict any aim) - it
is merely there, and functions because it
cannot do otherwise. We only perceive a design
because the end result is familiar to us,
and thus seems more “right” than any other
possible result [1]. If creation of information depends heavily
on the context, we must identify in living
organisms functions which permit it to accumulate,
and relate it to the material world. How
are particular structures or processes recruited
to this aim? A reflection on the coupling
between accumulation of information and energy
based on work developed by Landauer and Bennett,
showed that the relationship between energy
and information is that there exists degradative
processes which "make room" using
energy to prevent degradation of what is functional ([55,
56], detailed analysis in [1, 57], see also
a very recent development [58]). In such
a situation it is the context that determines
which gene product is functional and which
is not. The consequence is that, if the context
does not vary too rapidly, then the functions
which will be selectively retained are sculpting
an image of the environment within, creating
adaptation. This is exactly how an information
can get a meaning. In terms of synthetic
biology, this orients research towards learning
and memory, rather than towards fixed mechanical
engineering. In guise of conclusion: the brain is not
a computer, yet it manipulates information Trying to put vitalism to an end, Claude
Bernard placed biology within the realm of
physics and chemistry [59]. This led his
followers to ask the question: what are the
relevant entities (material objects and processes)
which make a cell alive? The biochemical
inventory stage started well, with the discovery
of the ribosomes, of the structure of the
DNA double helix, of the sequence of the
polypeptide chain of insulin, and, rapidly
of messenger RNA [60]. Yet, many features
of biological entities resisted the classical
analysis of chemistry and physics. This was
apparent in the laws of genetics, where linear
arrangements of the elusive genes was central
[61]. Even in biochemistry the shape of molecules
posed an enigma: La dissymétrie, c’est la vie, insisted Pasteur. But the involvement of
shape was deeper than usual: the very process
of replication placed the concept of form in a world quite different from the simple
arrangement of a particular setup in 3D as
shape would suggest. Replication shifted
the idea of a chemical as the substrate of a recognition process to that, abstract,
of a template, in this case for a duplication process
doubling the number of the initial molecule.
Subsequently, the discovery of transcription,
translation, and associated control and coding
processes, continued to shift emphasis from
shape to form in an abstract way, commonplace
in mathematics. Information — creating and manipulating form
— was essential to account for life processes.
From the world of Plato's archetypes, those
who explored the basic concepts of life resorted
to discussions which began with Aristotle
and placed form as a central category of
reality. For some time, and this is still
quite visible in systems biology as well
as in synthetic biology, living organisms
were seen as mechanistic automata, with feedback
and feedforward loops as paradigmatic entities.
The purpose of the present reflection was
to try and show that investigating the concept
of information shifts eighteenth century's
automata to modern algorithmic machines,
capable of authentic creation. This implied
replacing feedback by recursivity, a much
deeper process. Recursivity, associated to
appropriate management of energy [55, 56,
58], creates information [1]. It does so
by identifying two domains where information
must be taken into account: information of a program and information of a machine. However, while the information of the program
is fairly deeply explored by a vast community
of investigators, this is not so of the information
of the machine/chassis, which involves some
kind of measurement of the context (in terms
of implementation within the four categories,
matter, energy, space and time) [32, 58].
It is perhaps in the functioning of the brain
that we can make the latter type of information
most prominent. Indeed, while von Neumann
and others invented computers with mimicking
the brain in mind [28], the brain does not
appear to behave as a Turing Machine [21].
There is no “ghost in the machine” [52].
However, nobody would doubt that brain manages
information, and in a very efficient way
[51, 53]. To my view this is a strong indication
that the information we describe when considering
messages is a tiny part of what information
is. Because we use language, built on the
exchange of sequences of symbols, exactly
as programs are exchanged in computers, linguists
often saw the brain as a Turing Machine.
But language is deeply associated to meaning:
I had in 1974 at a meeting of the Centre
Royaumont pour une Science de l'Homme at
the MIT, a heated argument with Noam Chomsky
about other features of human languages,
such as rhythm (in the west african language
möré, a speaker may begin a rythmic sentence,
which is answered preserving rythmic rules
by somebody in the audience, triggering another
ejaculation of the speaker, with related
rules, etc) suggesting that beside grammatical
syntactic structures, there may exist a variety
of superimposed contexts which transmit information mediated by channels
that are not those usually considered [62-64].
As in Dyson’s scenario of the origin of life,
the basic functioning of the brain would
base on reproduction, while invention of
language with its linear sequences of phonemes,
when spoken, and letters when written, would
be, in Man, the transition moment when it
would begin to discover recursivity in linear
strings of symbols (phonemes) which can be
propagated from brain to brain, as programs
in a Turing Machine. In any event, in the
few cases where it might do so, it would
be an extremely slow one [65]. With this
view, Nature would have discovered twice
the importance of coding and recursivity,
in the emergence of life first, and in the
emergence of language, quite recently. Acknowledgements I wish to thank Philippe Binder for pointing out to me Wolfram’s article, Jean-Baptiste Masson for pointing out Sagawa and Ueda’s article and anonymous reviewers for very constructive comments. This work developed in the Stanislas Noria network is supported by the PROBACTYS and the TARPOL European Union programmes in Synthetic Biology (http://www.normalesup.org/~adanchin/causeries/causeries_en.html). Open Access This article is distributed under the terms
of the Creative Commons Attribution Noncommercial
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Figure legend Schematic representation of the dialogue between models and reality. Note that the context is essential in isolating postulates. Also, many models can co-exist, and, beside mathematical models approaches using analogies and simulations can behave as models. Contrary to Karl Popper’s wish there is no clearcut link between models and reality, precluding universal processes to define the exact contours of Science.
 |
. 
and the like, with all kinds of idiosynchrasies
associated to some consistency, 
not chance (which is neither a Greek nor a scientific
concept [4]), was the ultimate cause [5].
In short, the problem of creation of form, in-formation, superimposed on the general problem of
movement. In addition to phenomenology, understanding
in-formation required a process of synthesis. In this context, synthesis became a direct
exploration of the concept of creation, uncovering
a link between in-formation and creation.
This was understood soon after chemistry
was born — chemical synthesis is at the heart
of modern chemistry — and it is therefore
not unexpected that biology, where form is
apparent everywhere, should develop into
synthetic biology.
only allowed us to give our views 
on reality. “And for a certain truth, no
man has seen it nor will there ever be a
man who knows about the gods and about all
the things I mention. For if he succeeds
in the end in saying what is completely true,
he himself is nevertheless unaware of it;
and opinion is fixed by fate upon all things”
said Xenophanes [10]. Approaching truth is
to place a fragment of reality in a particular
perspective, where we can understand its
relationships with human beings. The central
tenet of science – often ignored, as many
scientists behave as priests of a revealed
religion when interacting with mass media
– is that we construct models distinct from
reality. We match models with phenomena,
expressing local instances of reality in
a particular context. Models may display
a certain degree of adequacy, if not truth,
with reality. The model/reality separation
is so significant that several concomitant
models may express our knowledge about a
particular side of reality.
whose digits are generated by fairly short
algorithms while their succession cannot
be predicted. They are therefore of limited
algorithmic complexity. Yet, they are much
more interesting than repeated sequences
with the same complexity. Knowing the exact
value of a digit placed in the digits of
