Functional Information: Towards
Synthesis of Biosemiotics and Cybernetics
ISSN 1099-4300 www.mdpi.com/journal/entropy
Alexei A. Sharov
National Institute on Aging,
Received: 10 March 2010; in revised form: 6 April 2010 / Accepted: 21
April 2010 / Published: 27 April 2010
Abstract: Biosemiotics and cybernetics
are closely related, yet they are separated by the boundary between life and
non-life: biosemiotics is focused on living
organisms, whereas cybernetics is applied mostly to non-living artificial
devices. However, both classes of systems are agents that perform functions
necessary for reaching their goals. I propose to shift the focus of biosemiotics from living organisms to agents in general,
which all belong to a pragmasphere or functional
universe. Agents should be considered in the context of their hierarchy and
origin because their semiosis can be inherited or
induced by higher-level agents. To preserve and disseminate their functions,
agents use functional information - a set of signs that encode and control
their functions. It includes stable memory signs, transient messengers, and
natural signs. The origin and evolution of functional information is discussed
in terms of transitions between vegetative, animal, and social levels of semiosis, defined by Kull. Vegetative semiosis
differs substantially from higher levels of semiosis,
because signs are recognized and interpreted via direct code-based matching and
are not associated with ideal representations of objects. Thus, I consider a
separate classification of signs at the vegetative level that includes
proto-icons, proto-indexes, and proto-symbols. Animal and social semiosis are based on classification, and modeling of
objects, which represent the knowledge of agents about their body (Innenwelt) and environment (Umwelt).
Keywords: functional information; biosemiotics;
cybernetics; evolution; agent; origin of life; Umwelt;
learning; biological complexity; biological signal processing
PACS Codes: 87.18.-h, 87.85.Ng
1. Introduction
One of the major revelations of the 20th century is that life is intrinsically
related to information processing and communication. The existence of each
species depends on the transfer of genetic messages from parents to offspring
[1]. Also, the survival and reproduction of individual organisms depends on
their ability to model their environment and use these models to optimize their
functions [2]. Understanding of the informational nature of life has led to the
emergence of two new disciplines, cybernetics and biosemiotics,
which approached the same problem from different angles. Cybernetics was
conceived to study communication and control in machines and living organisms
in the light of the information theory [3,4]. However,
because living organisms are too complex and we still have very limited means
to control them, cybernetics has much stronger links with technology (i.e.,
communication industry, computers, and robots) than with biology. Computers can
process user-supplied information without ―understanding‖ the
problem that they help to solve. Thus, the algorithms of information processing
appeared more important for cybernetics than the meaning of information. In
contrast, biosemiotics is focused on studying living
organisms and their unique ability to generate and interpret meaningful
information [5]. Thus, biosemiotics and cybernetics
appeared to be separated by the boundary between life and non-life.
But both disciplines can benefit from their integration in our
development of technology. As artificial agents become more complex, they
resemble living organisms and require novel principles for their design and
analysis that go beyond traditional cybernetic approaches (e.g., goal oriented
feedback, attractors, and computation). In particular, to achieve their goals,
complex agents need efficient internal communication (between autonomous subagents, and with their future states) as well as external
communication with other agents. Communication includes production and
interpretation of signs, therefore analysis of complex
agents requires semiotic approaches. Combined cybernetic-biosemiotic
methodology can be applied also to the management of human knowledge, which now
evolves towards multiplicity of underlying models and integration of
heterogeneous data [6]. Analysis of cellular information processing networks
can help to design flexible multi-purpose knowledge repositories.
This paper follows previous attempts to bridge the gap between biosemiotics and cybernetics, including second-order
cybernetics [7,8], theory of autopoiesis
[9], artificial life [10], evolutionary semiotics [11,12], organic codes [13],
and cybersemiotics [14]. As one reason for his
construction of a cybersemiotics Brier argues that
information (as it is understood in cybernetics) is not enough to explain the
phenomena of experience, communication, and knowledge [14]. He suggested to
complement it with the semiotic theory and philosophy of Charles Peirce [15],
which is based on the distinction between three basic categories: Firstness (chaos, potentiality, pure feeling and potential qualia), Secondness (force,
will), and Thirdness (habits, symbols).
However, many obstacles remain on the path of integration. The notion
that natural and artificial agents should be analyzed within the same logical
framework is still not fully accepted in biosemiotics.
The idea that life and semiosis are coextensive [5,12] is often interpreted literally, so that artificial
agents appear non-semiotic. Another challenge comes from molecular biology
which studies complex molecular machines involved in DNA replication,
transcription, and protein synthesis. Molecular machines have no learning
capacity, and in this respect they are similar to human machines. However, it
is not logical to exclude them from the consideration of biosemiotics
because they participate in
cellular semiosis
[13]. All agents are externally programmed (e.g., by genome, family, culture);
and these external programs that carry historical and evolutionary roots appear
more important for agents than the presence of individually acquired programs. Learning
often requires longer times than the life span of individual agents, thus it
should be viewed as an optional rather than necessary feature of agents. The
role of agents in generating and processing of information is not fully
recognized within the field of cybernetics, which is another obstacle for a
synthesis between cybernetics and biosemiotics [11]. Information
is often considered as a physical property of material objects [16], as a
method in the theory of probability [17], or as input and output of computation
[18]. Finally, analysis of agents requires an update for the philosophy of
science. Agents are subjective beings and therefore we need to find a place for
subjectivity within the scientific worldview. Based on Peirce's semiotic
philosophy Brier considers that the universe has some very basic elements of
consciousness such as pure feelings, wills, and a tendency to take habits [14].
However, his approach is not compatible with biology as a science. Development
of habits in living organisms requires heredity or long-term memory which has
not been found in the physical universe, and feelings require complex heritable
sensors which are also not found outside of life and its products.
In this paper I follow a functional-evolutionary approach to agents in
general, which are defined as systems with goal-directed programmed behavior. Agents
are either living organisms or their products because only these systems are
known to pursue their goals. Agents are interconnected horizontally,
hierarchically, and genealogically; they often include subagents and are always
produced by other agents of comparable or higher complexity. Agents can be well
individuated or diffused (―swarm agents‖), autopoietic,
autotrophic, with or without learning capacity. What unites them,
is their ability to perform functions for the purpose of reaching certain
goals. Functions of agents are encoded and controlled by a set of signs which I
call functional information. These include stable memory signs, transient
messengers, and natural signs. Agents always receive some of their functional
information from parental/recruiter agents and often follow parental/recruiter
goals. This induced semiosis is common for living
organisms and artificial devices. In contrast to the cybernetic understanding
of agents with its emphasis on control, feedback loops, and attractors, my
approach is focused on the origin, evolution, functionality, and communication
of agents.
The origin and evolution of functional information is discussed in terms
of transitions between vegetative, animal, and social levels of semiosis [19]. Vegetative semiosis
is based on code-based mapping of sign molecules, which encode and control
basic cellular functions. The origin of life is seen as the emergence of
autocatalytic molecules that can encode properties of a larger host system in a
way that enhances autocatalysis. Such a system is an agent because it can be
described in terms of encoded goal-directed actions, although an alternative
mechanistic description is also possible. But mechanistic models cannot fully
capture the dynamics of complex agents; instead they can be applied only to
their simple components. Agents represent a new cross-disciplinary ontological
entity because we can describe them in terms of actions, signs, goals, and benefits,
which do not belong to the vocabulary of physics. Simple agents belong to the
―gray‖ transitional zone as they can be described both semiotically and mechanically. Higher levels of semiosis (animal and social) are grounded in the vegetative
level and include classification and modeling of objects, which represent the
knowledge of an agent about itself and its environment, i.e., Innenwelt and Umwelt [2]. In
addition, advanced agents develop logic
that helps them to produce new sequences of actions
and new models of the world that are more likely to be successful than random
actions and random models. Because most artificial devices are not yet capable
of learning and evolution, their functional information has human origin. However,
future artificial agents may have increased abilities to generate their own
functional information, and some of them (synthetic organisms) may be capable
of autonomous adaptive evolution.
2. Agents
Contemporary biology is based mostly on the material understanding of life:
cells and organisms are described in terms of parts (e.g., proteins, lipids,
nucleic acids, carbohydrates) and their interactions. An alternative
―relational‖ approach, is to define living systems on the basis of
their functions rather than composition [20,21]. If an
artificial device performs the same (or similar) functions as a living
organism, then, according to relational biology, it is alive. However, it would
be confusing to apply the term ―living organism‖ to artificial
devices. Instead, it is better to use the term ―agent‖ which
equally fits to living organisms and artificial devices. Here I consider an
agent as a system with spontaneous activity whose actions are programmed for
reaching certain goals. Goals are considered in a broad sense, including both
achievable events (e.g., capturing a resource or producing an offspring), and
sustained values (e.g., survival, energy balance, and attention). Goals of autopoietic systems include production of functional body
parts during development, survival, and reproduction [9]. The notion of agent
is more complex than anything studied in physics and cannot be defined solely
in terms of composition and relations. Thus, its definition includes notions of
―action‖ and ―goal‖, which do not belong to the vocabulary
of physics.
Being an agent is not a physical property, which implies that agents are
not universally identifiable. We can learn to recognize specific kinds of
agents, but there is no rule to identify any kind of agent. Humans and other
animals learn that agents are unusual and often dangerous beings. They are
unpredictable, adaptive, and can be aggressive. But humans often make mistakes
in identification of agents. Celestial objects were erroneously thought to be
agents (gods). Also, people never expected that their body is made of smaller
sub-agents until the emergence of cellular biology. Scientists still do not
have a consensus on whether viruses and individual genes are agents or not. The
following three criteria can help us to distinguish agents: (1) agents select
specific actions out of multiple options, (2) these
selected actions are useful in a sense that they help agents to reach their
goals, and (3) agents do not emerge by chance, they are produced only by other
agents of comparable or higher level of functional complexity [22]. These
criteria, however, are difficult to apply. Agents may remain dormant for a long
time; thus, actions are not instantly detectable. Actions may occur at the
molecular level and therefore go unnoticed if we don’t use molecular
sensors. Evaluation of benefits is also not trivial because we do not know the
goals of agents. We usually assume that goals of living organisms include
survival and reproduction, a notion that comes from the theory of genetic
selection. However, an organism may follow a goal of a larger agent (e.g.,
family or population) and sacrifice its life for the benefits of the
super-agent. Actions may include multiple steps and only the last step brings
benefits; thus, it is difficult to understand the benefits of previous steps. Because
of these obstacles, it may be difficult to prove that some system in NOT an
agent. Thus, I suggest following a conservative scientific approach and apply
the term ―agent‖ only to those systems that are well proven to have
reproducible
goal-directed activities. In particular, I
do not consider the existence of non-material agents (gods) and potential
agency in immortal systems (e.g., in the universe). Mortality implies that
agents are either self-reproducing systems or products of other
self-reproducing systems. This is a testable necessary condition which helps to
narrow down the set of systems that can be agents.
Goals may emerge within the agent, or alternatively can be set by
parental agents or higher-level agents. Primitive organisms mostly follow
genetically inherited goals, thus, their individual contribution to these goals
is relatively small. In contrast, higher animals are capable to develop novel
individual goals, especially those that are related to short-term needs (e.g.,
fast response, ability to find resources and avoid enemies). In human
populations, goals are communicated and propagated through the cultures. Goals
of artificial agents are usually set by their designers; however, advanced
robots can learn and develop new lower-level goals that help to reach
externally supplied goal of the higher level. Learning capacity is optional for
agents, but non-learning agents always originate from learning agents and are
supplied with previously developed tools and programs that are necessary for
performing their functions.
Agents often have a hierarchical structure and contain subagents. For
example, multicellular organisms are made of cells
and products of their activity, and cells are made of smaller subagents like organelles
and chromosomes. Hoffmeyer referred to this nested
structure as ―boundaries within boundaries‖ [23]. Some agents have
no boundary at all and are represented by free subagents united by
communication or common origin (e.g., families, dems,
populations, species, and communities). These agents can be called swarm
agents, following the terminology of [23]. Subagents in swarms may interact and
exchange information, however it is conceivable that
some swarms are united solely by their common origin (i.e., common
information and goals).
Because agents do not emerge by chance, they persist in the world only
via continuous production of other agents. All agents are artifacts because
they are manufactured by other agents, which matches
the notion that ―life is artifact making‖ [24]. Living agents are
capable of self-repair and self-reproduction, a property known as autopoiesis [9]. But autopoietic
agents may still depend on the environment and available resources. For
example, predators need prey to survive, parasites need host organisms, and
computer viruses propagate only within a network of interconnected computers. Only
autotrophic agents can make all necessary parts from resources that are not
produced by other agents, and therefore they are fully independent. All
autotrophic agents are autopoietic, but not all autopoietic agents are autotrophic. Agents can be semi-autopoietic if they repair only certain components of their
body but not capable to self-reproduction. Worker bees and mules are examples
of semi-autopoietic agents; they can repair their
cells and tissues but they cannot reproduce. Similarly, somatic cells within a multicellular living organism are not fully autopoietic. They have lost their ability of infinite
self-reproduction, and some even lost their nucleus (erythrocytes). In
contrast, germ cells (gametes and their progenitors) are fully autopoietic and potentially immortal.
Functional complexity of new agents is always comparable or lower than
the complexity of parental agents because new functions appear only by
modifications of already existing functions. Thus, the complexity can increase
in lineages of agents only slowly by gradual modification of already existing
functions. As a result, the rate of progressive evolution is limited, whereas
regressive evolution can be fast. This limitation on the rate of progressive
evolution is known as a principle of gradualism in
evolution [22]. Gradualism was
criticized based on the facts from genetics and paleontology. Many mutations
produce instant big changes of the phenotype. Paleontological records also show
rapid changes of phenotypes in numerous lineages during very short transitional
times between stable epochs, which prompted Gould and Eldredge
to develop the concept of punctuated equilibrium [25]. Dawkins argued that the
discreteness of paleontological records is compatible with gradualism because
it resulted mostly from migration and propagation of already existing species,
rather than from suddenly accelerated evolutionary process as claimed by Gould
[26]. However, the existence of macro-mutations and increased variation
following environmental changes are still difficult to reconcile with
gradualism. To avoid criticism, the principle of gradualism was reformulated in
terms of complexity rather than morphology [22]. Morphology is the tip of the
iceberg of evolutionary changes, which mostly occur at the molecular level. Species
have developed molecular mechanisms that can support rapid morphological
changes in stress conditions. These modifications do not change the total
complexity of the system, they simply convert
physiological and molecular complexity into new morphology. According to this
new understanding of gradualism, the total functional complexity can increase
only gradually, whereas certain morphological and physiological characteristics
of organisms may change much faster [22].
The principle of gradualism does not contradict to the idea that complex
systems show emergent behaviors, which is an important property of life [27]. It
only restricts the rate of emergence by stating that new functions do not
appear instantly but emerge gradually via modification of already existing
functions. Simple modifications can be done fast enough but multi-component
modifications take longer times. The more complex are the agents, the faster is
their rate of developing novel functions because of several positive feedback
mechanisms [28]. First, functions cooperate, so that better performance in one
function may help to develop another function. Second, functions duplicate and
get specialized. Finally, existing functions include manufacturing of specific
tools or organs which need maintenance and recycling, therefore they create
niches for novel maintenance functions. In evaluating the rate of emergence, it
is important to distinguish the appearance of true novel functions from the
activation of already existing silent functions. For example, a fertilized egg
has very few active functions, however it carries the
encoded capacity to perform many other functions that are going to be activated
at certain developmental stages. Thus, the principle of gradualism refers to
the full functional capacity of an agent, rather than to the set of currently
active functions.
Because living systems are substantially more complex than non-living
natural systems, the statement ―life from life‖ follows from the
principle of gradualism. In particular, complex systems cannot originate by
pure chance, and life gradually emerged from very simple primordial systems. The
statement ―life from life‖ can be further generalized as
―agents from agents‖ because the principle of gradualism works for
all kinds of agents. Because living organisms appeared before human-made
agents, all agents originate from life. Thus, the world of all agents, which I
propose to call pragmasphere, is an extension of the
biosphere. The distinction between natural and artificial appears less
important than the distinction between agents and non-agents. The notion of pragmasphere thus becomes the meeting point of cybernetics
and biosemiotics. However, the synthesis of these
disciplines would require a revision of terminology and basic assumptions. It
should be recognized that any agent is more than a physical body but a link
between its parental agents and potential future products.
Programmed artificial devices may be not enough smart and lack learning
abilities, but they are manufactured by humans and produce useful things. Thus,
they are components of human functional cycles and evolve together with human
knowledge. To facilitate the synthesis of biosemiotics
and cybernetics we need to apply principles of biosemiotics
to all agents and not to restrict them to living organisms.
3. Functions
Function is a reproducible sequence of actions, which is beneficial for
the agent. Reproducibility implies that functions are always encoded and
controlled by signs, as discussed in section 4. Living organisms have specific
sets of functions at each level of their organization. The most simple are
molecular functions, which are well described by the Gene Ontology [29]. The
simplest ones usually fall into categories of binding and catalysis. Molecular
functions are performed by specialized molecular sub-agents. For example, the
DNA-polymerase complex duplicates the DNA molecule; and ribosomes
synthesize proteins on the basis of the input mRNA sequence. Proteins often
have several domains specialized for different functions, which are thus
combined into a complex function with multiple actions. For example, the P300
protein is responsible for activation of transcription via acetylation
of histones in the promoter region and possible
interactions with the transcription initiation complex [30]. This protein has
at least 8 domains, including a HAT domain to modify histones,
a bromodomain to recognize histone
modifications, a KIX domain to interact with CREB transcription factor,
zinc-finger domains for DNA binding, and a coactivator
of nuclear receptors. Obviously, P300 plays the role of an information hub,
where multiple molecular signals are integrated to control the expression of
the gene.
Input-output relations of molecular agents are often described as
―information processing‖ because DNA replication is similar to
string copying, and protein synthesis resembles computation. However, this
metaphor may be misleading. Processes of DNA replication and protein synthesis
taken alone (e.g., in vitro) have nothing to do with information because
molecules of nucleic acids are processed as material objects irrespective of
their meaning. However, in the context of a cell, these actions indeed
represent information processing. Reductionism attempts to attribute all the
functions of organisms to the agents at the lowest hierarchical level (i.e.,
to molecular complexes). This approach ignores the benefits of functions, which
usually appear only at higher hierarchical levels. For example, DNA repair and
proofreading are performed by certain protein complexes within a cell, but the
benefits from these processes go far beyond the life of one organism. These
benefits can be fully accounted for only in long lineages that include
thousands of generations.
Functions at the cellular level include resource capturing, growth,
metabolism, modification of the cytoskeleton, and surface properties, sensing
of external conditions, cell cycle, and control of major internal processes. Each
of these functions requires thousands and millions of molecular interactions. Multicellular organisms have even more complex sets of
functions related to differentiation of cells. Each type of cells becomes
specialized in performing unique functions that are necessary for the entire
organism. Functions of the whole organism include eating, digestion, excretion,
sensing, movement, mating, and reproduction. Finally, there are functions at
the level of super-organisms (e.g., families, populations, species, and
ecosystems). Functions of a colony include communication (bee dances),
construction of nests, and defense
(soldier ants). Ecosystems also can be viewed as agents, but their integrity is
rather weak because many populations can easily migrate in and out. Examples of
ecosystem functions are carbon circulation and recycling of dead organisms.
Agents often outsource their functions to server agents, which can be
either manufactured or recruited. Sever agents have induced semiosis
because their goals and functional information (defined in the next section)
are modified or reset by master agents. The set of all server agents represents
a functional envelope for master agents. Human functional envelope includes
manufactured machines as well as recruited living organisms (cultivated plants
and domesticated animals). Making functional envelopes is not specific to humans, it is rather a standard strategy for all agents. Animal
body (somatic cells) is a mortal functional envelope for immortal germ cells. Similarly,
a bacterial cell can be viewed as a functional envelope for the DNA molecule. Production
of worker bees by a bee queen is another example of a functional envelope. Recruitment
of other agents is also a widespread strategy in nature. Fungi in lichens
recruit unicellular algae for photosynthesis and production of nutrients [31]. Leaf-cutting
ants and bark beetles are farming fungus [32,33].
Development of functional envelopes is more than just increasing the number of
agents; it creates super-agents that belongs to a new
level of agent hierarchy. This process was called ―metasystem
transition‖ by Turchin [34],
and ―major transition‖ by Maynard Smith and Szathmáry
[35].
4. Functional Information
Any function of an agent has to be reproducible, which means that agents
should be able to repeat corresponding actions with a certain
fidelity to ensure the same beneficial result. The only way to ensure this
reproducibility over long evolutionary times is to manufacture, preserve, and
replicate signs that control actions; thus, every function is encoded and
controlled by signs. Storing signs in memory (e.g., genetic, epigenetic, or
neural) can be interpreted as self-communication, because memory is a message
sent by an agent to its own future state. The purpose of self-communication is to
transfer the ability to perform functions, so that the agent preserves its
functionality. The genome can be seen as a long-term ―memory‖,
which extends over many generations [19,36]. Transfer
of the genome to the progeny is a form of self-communication because progeny is
a continuation of the parent self [36]. Because signs deteriorate with time,
they can persist indefinitely only by replication (copying, duplication). Thus,
long-term memory signs have to be both copied and interpreted, which is known
as a principle of sign duality [37]. Besides stable memory-signs, functions are
also controlled by transient messengers generated by sensors or logical
devices. Messengers are produced in response to certain external or internal
conditions and help to optimize the timing of actions.
Signs are often defined based on their semantics, i.e., the
ability to stand for something else for somebody [38]. Similarly, Peirce
considered sign as a triadic relation between a sign vehicle, an object which
it stands for, and interpretant which is induced by
the sign in the interpreter [15]. Because I am interested in the role of signs
in supporting the functions of agents, I prefer to emphasizes
the pragmatic aspect of signs, i.e., their ability to specify or modify
actions of agents in a beneficial way [39]. In other words, agents use signs to
organize their activity. Activity of agents may result in the production of
other signs (e.g., mRNA is synthesized using DNA as a template), or in the
production of agent components (e.g., resources, structural elements, or
sub-agents). Being a sign is not a physical
property, instead it is a semiotic
property. For example, a DNA molecule taken alone is just a chemical; but it
can play the role of a sign for some living organism. Theoretically, any
material object can be a sign for some agent who can interpret it in functional
terms. However, most important are those signs that are produced by agents for
communication purposes (including self-communication).
Semiotics and cybernetics historically use different terminology. The
central notion of semiotics is ―sign‖ whereas cybernetics uses the
term ―information‖. The term ―information‖ is often
used in a narrow sense as a degree of non-randomness or negentropy
[17] or as a sequence of characters in the text or in DNA [24], however discussion of these meanings will carry us away
from the topic of the paper. Instead, I will follow the definition of Bateson
that ―information—the elementary unit of information—is a
difference which makes a difference‖ [40]. This understanding of
―information‖ brings it close to the notion of ―sign‖, however these terms are still not synonymous. Besides
other differences in their meanings, I would like to emphasize the aggregative
nature of information which unites many signs used together by an agent. In
human semiotics, the notion of ―sign‖ is often applied to
individual words or sentences, for which the meaning can be clearly specified,
but not to a dictionary, library, or database. In contrast, the term
―information‖ is relevant for large and heterogeneous sets of
signs. Thus, I consider information as a sign or set of signs used together by
agents. To emphasize the functional role of information I proposed the term
―functional information‖ which is a set of signs that encode
the functions of the organism [36]. In this paper I want to widen the meaning
of this term and include also those signs that control the functions. Functional
information of organisms includes their genome, epigenome,
internal messengers (e.g., mRNA, miRNA, transcription
factors, kinases, and phosphatases),
external messengers (e.g., pheromones), and natural signs (e.g., temperature
and salinity of water). For comparison, Barbieri
proposed the term ―organic information‖ which is applied in a
narrow sense to replicated sequences only [24]. The notion of ―functional
information‖ can be easily extended from organisms to artificial agents. Then,
semiosis can be defined as a set of processes by
which functional information is interpreted, duplicated, modified, and
disseminated by agents for their own benefits.
5. Vegetative Semiosis: Emergence and Early
Evolution of Functional Information
Functional information evolved in parallel with living organisms
starting from the origin of life, and primitive agents differ substantially
from advanced agents in the level of complexity of their semiotic processes. Thus
it is important to delineate threshold zones that separate primitive levels of semiosis from advanced levels [19]. To separate semiotic
systems from non-semiotic, Kull used the functional cycle of Uexküll as a basic model of semiosis.
Then he proposed to distinguish between vegetative, animal, and cultural levels
of semiosis, ordered by increasing complexity. He
also argued that these three levels of semiosis can
be associated with three types of signs defined by Peirce: icons, indexes, and
symbols, respectively. Although I agree with Kull on the distinction between
vegetative, animal, and cultural levels of semiosis
as major transitions in the evolution of life, it is debatable if these levels
can be associated with icons, indexes, and symbols, respectively. Instead, I
apply Peirce’s terminology only for animal and cultural semiotic levels,
and use a modified classification of signs for vegetative semiosis
(Figure 1). Another attempt to differentiate between levels of semiosis is the pragmatic scale of ―consciousness‖
(ConsScale) which was applied to both living and
artificial agents [41]. Based on
the relationship between the body, receptors, action
machinery, sensorimotor coordination, and memory,
agents were classified as reactive, adaptive, attentional,
executive, emotional, self-conscious, empathyc,
social, human-like, and super-conscious. Animal semiosis,
as defined by Kull, apparently starts from the attentional
level. The weakness of ConsScale is that it does not
consider the semiotic functions of agents (production, dissemination, and
interpretation of signs), and agent behaviors are limited to short time scales.
As a result, silent functions (e.g., adult functions that are already encoded
in the oocyte), embryogenesis, and evolution are not
viewed as agent properties. Also, it is questionable if the term
―consciousness‖ can be applied at the level of vegetative semiosis.
Figure 1. Major transitions in the
evolution of functional information, its components (middle column) and signs
(right column).
Vegetative semiosis operates at the molecular
level and differs substantially from higher levels of semiosis
because molecules are recognized and interpreted via direct chemical
interactions (e.g., by binding and/or catalysis) rather than by association
with ideal representations of objects. The difference is so profound that
Umberto Eco decided to ―exclude from semiotic consideration neurophysiological and genetic phenomena‖ [42]. However,
incorporation of molecular processes into semiotics opens new horizons in our
understanding of the origin and evolution of life [13,39,43];
thus, it cannot be easily dismissed. Moreover, the analysis of molecular signs
can help us to understand the origin and nature of human signs.
The key feature of vegetative semiosis is
code-based mapping [19]. Most common examples of code-based mapping involve
adaptors, such as tRNA in protein synthesis and
surface receptors in signal transduction [19,24]. In
both examples, adaptors connect two entirely unrelated kinds of molecules. The
notion of code-based mapping can be extended even to reflexive relations (e.g.,
polymerization of actin or tubulin)
and binary relations (e.g., specific catalysis). It may be argued that
reflexive and binary relations lack the arbitrariness/contingency of the
adaptor-mediated code. However, actin is a large
molecule and it includes an ―internal adaptor‖
specialized for the function of polymerization. Actin
is not a ―natural molecule‖ but a product of adaptive evolution,
and therefore it is contingent. Nucleotides are much smaller than proteins, but
the mapping of nucleotides A-T and
C-G in the DNA still can be viewed as a code-based binary relation. Random
chemical synthesis can generate a wide variety of nucleobase-like
molecules. Yet, only 4 nucleobases are found in the
DNA, which indicates that they were selected based on their functionality.
However, code-based mapping alone is not semiosis
yet, and therefore, the code-model of semiosis
proposed recently by [13,24] seems incomplete. To
become semiosis, this code-based mapping needs to be
embedded in a functional agent and support the functions of this larger system.
Thus, semiosis requires interaction between systems
of two (or more) hierarchical levels, which can be taken as an important clue
for reconstructing the origin of life. Autocatalysis alone is not semiosis and not life because there is no second level. To
become a coding system, autocatalytic molecules should be able to modify
(encode) the properties of a their local environment
so that the environment becomes more favorable for autocatalysis [44]. This
functional linkage is a necessary condition for cooperation between multiple autocatalytic
components if they happen to share their local environment. In contrast,
unlinked autocatalytic systems can only compete but not cooperate.
Life did not start from heteropolymers like
nucleic acids because monomers were not available in sufficient quantities as
resources [22]. Even if several nucleotides appeared in close proximity due to
a once-in-a-universe lucky coincidence and are then used to synthesize a
complimentary RNA strain in a hypothetical protoorganism,
there would be no nucleotides left for the reproduction of the next generation.
It is also unlikely that life started from large autocatalytic sets of
peptides, as proposed by Kauffman [45], because there is no sufficient supply
of monomers (i.e., amino acids) to make peptides. The model of random
catalysis does not work even if applied to simple organics molecules due to the
following problem: most abundant organic molecules (e.g., saturated
hydrocarbons) are inert. According to the alternative ―coenzyme world‖
model of the origin of life, small coenzyme-like molecules became autocatalytic
on the surface of oil (i.e., hydrocarbon) microspheres in water and
modified surface properties of microspheres in a beneficial way [44]. These
molecules were called ―coding elements‖ because they replicate via
autocatalysis and encode surface properties of microspheres, which together
make a heritable metabolism. Such simple systems can further evolve by
accumulation of additional coding elements that can help in capturing energy
and other resources. Because various kinds of coding elements were not
connected, they were transferred to offspring systems in different
combinations. Despite random transfer, such combinatorial heredity can be
stable because (1) coding elements are present in multiple copies and therefore
each offspring has a high probability to get the full set, and (2) natural
selection preserves preferentially organisms with a full set of coding
elements. The efficiency of the later mechanism was shown in a "stochastic
corrector model" [46]. New types of coding elements can be added by (1)
recruitment of entirely new molecules from the environment, (2) modification of
existing coding elements and (3) polymerization of coding elements or their
products [44].
The transition from the ―coenzyme world‖ to the ―RNA
world‖ can be seen through the invention of template-based replication. Initial
steps of primordial evolution were slow and inefficient because there was no
universal rule for producing new coding elements. Some improvement was likely
achieved by transformation of old coding elements into novel ones via
modification of functional groups or polymerization. However, there has been no
streamlined procedure for making new coding elements. Such a procedure was
invented in the form of a template-based (or digital) replication. It is a
special
case of autocatalysis, where each coding element is
a linear sequence made of a few kinds of monomers, and copying is done
sequentially via predefined actions applied to each monomer [46]. Digital replication
makes the coding system universal because the algorithm works for any sequence;
hence, there is no need to invent recipes for copying modified coding
molecules. The starting point for the origin of template-based replication is
the existence of polymers with either random or repetitive sequence [44]. Polymers
may initially stick to each other to perform some other functions, e.g., to
increase stability and facilitate polymerization. The shorter strand of the
paired sequence can then be elongated by adding monomers that weakly match to
the overhanging longer strand. Then natural selection would have supported the
increase of specificity of this process and helped to produce better copies of
existing polymers. Some coding elements became specialized in assisting the
process of template-based replication playing the role of polymerase; as a
result, the replication process became passive. Invention of digital
replication, therefore, may have been the turning point in the origin of life,
which supported unlimited hereditary potential [44,47,48]
and caused a rapid increase in the abundance and complexity of coding elements.
Although the rate of processes in primordial organisms depended on the
environment, these effects initially were not regulated by organisms. Regulation
requires sensors, which are adaptor molecules that mediate the effects of
environment on organism functions. In a simple case, sensors interact directly
with functional modules. For example, many bacterial transcription factors are
adaptors connecting the promoter of a regulated gene and a ligand
(i.e., signaling molecule) [49]. In a more complex case, sensors
generate small short-lived molecules (messengers) after ligand
binding, and then messengers travel to functional modules (e.g., promoters) and
modify their activity. Production of sensors have to
be a heritable function before any sensing occurs. Thus, we can hypothesize
that heredity was the most ancient function of primordial signs, and sensing
appeared later when organisms mastered the production of complex molecules
which can play the role of sensors.
As the number of sensors/adaptors increased in the evolution of
organisms, they became integrated into sensory networks via logic gates. For
example, transcription of a gene is often activated by simultaneous binding of
two or more transcription factors to neighboring DNA motifs in the promoter. The
combination of multiple DNA motifs makes a composite sensor, which works as the
―AND‖ logic gate. The mRNA molecule is another example of the
―AND‖ logic gate because it combines multiple sets of nucleotide
triplets. These triplets bind sequentially to tRNA
adapters within the ribosome and control the order of amino acids in the
synthesized protein. The ―OR‖ gate is simply a sensor that can
respond to multiple external signs. By using logic gates, organisms can
generate messengers that represent complex conditions. Logic gates are encoded
either as direct prescriptions for signal processing (e.g., cis-regulatory
modules in promoters), or as prescriptions for making proteins that play the
role of gates. This innate logic evolves via genetic selection only; thus, it
cannot be modified within the life span of an organism if to some reason it
stops producing beneficial effects. To overcome this limitation, organisms
developed epigenetic mechanisms for modifying logic gates on demand. For
example, a gene with multiple cis-regulatory modules
in its promoter may initially carry open chromatin at all locations. However,
after some ―memory triggering‖ event, the chromatin becomes
condensed at all modules except the one that was functional at the time of the
event. This can be viewed as a primitive mechanism of learning [19,40].
According to Kull, vegetative semiosis is
based solely on iconic sign relations because code-based matching (or
―recognition‖) is analogous to visual recognition of icons [19]. Semiotic
notions of Peirce are highly generic, and binding of signal molecules may
indeed match his definition of icon: ―An icon is a sign fit to be used as
such because it possesses the quality signified.‖ [15]. However, it is
important to keep in mind a tremendous gap that separates human icons (e.g.,
visual shapes or sound tunes) from molecular interactions that lack any
elements of perception or classification. Human icons are associated with ideal
representations of objects, which are not yet present at the molecular level. Because
vegetative semiosis is so different from animal and
social semiosis, I suggest to use
prefix ―proto‖ for semiotic terms applied at the vegetative level. Examples
of proto-icons include both simple molecular bindings (e.g., binding of cAMP to a CAP receptor) and complex multi-component
bindings (e.g., complementary binding of nucleic acids in DNA replication,
transcription, and miRNA binding).
Besides proto-icons, vegetative semiosis
includes proto-indexes and proto-symbols. Here I disagree with the idea of Kull
[19] that vegetative semiosis is solely iconic. For
example, an adaptor molecule is a proto-index because it has two binding
surfaces for interaction with two different kinds of molecules, which are
physically connected. Proto-indexes often form chains of signal transduction
which transfer signals detected at the cell membrane to the nucleus. Proto-symbols
are conventional sign relations established for vertical and/or horizontal
communication. For example, DNA plays the role of a proto-symbol in a sequence
of processes that start from mRNA synthesis on the DNA template and ending with
protein synthesis on the mRNA template. Protein synthesis follows the rule of
the genetic code which is a heritable ―natural convention‖ based on
a set of molecular adapters (tRNAs
and acyl-transferases) [13].
Proto-symbols are components of the genetic proto-language which
appeared much earlier in evolution than animal and human languages. This
proto-language gradually changed along evolutionary lineages. The structure of
the mammalian genome is entirely different from the bacterial genome, although
some features are highly conserved and remained almost unchanged through
billions of years (e.g., the genetic code table). The major changes appeared in
the transitions from prokaryotes to eukaryotes. These include the development
of additional code systems: the chromatin code, splicing code, miRNA code, and others [24]. The transition from
unicellular to multicellular organisms was associated
with the emergence of complex signaling between cells. Cis-regulatory
regions in promoters evolved from simple combinations of transcription factor
binding motifs to complex ensembles that control the function of enhanceosomes.
6. Animal and Social Semiosis
As the number of perceived signals increased in evolution, agents learned
how to integrate them into meaningful categories representing various objects
and situations (e.g., food items, partner agents, and enemies) and predict
events using models. These classifications and models represent the knowledge
of an agent about itself and its environment. Following the terminology of Uexküll, this knowledge is the Innenwelt
and Umwelt of the organism [2]. Although Kull assumes
that Umwelt may exist even at the vegetative level of
semiosis [19,50], I prefer
to limit the use of this term to the higher levels of semiosis.
At the vegetative level, signs are mere prescriptions of actions and do not
carry knowledge. In contrast, signs at the animal and social levels of semiosis are linked with ideal
representations of objects, situations, and
actions. However, higher levels of semiosis are
grounded at the vegetative level because all activities of an organism (e.g.,
sensing, movement, and interpretation) are apparently supported by certain
molecular functions, although we still know very little about molecular
mechanisms of neural processes. Information processed by animal and social semiosis does not necessary induce physical actions,
however it still can be called ―functional information‖ because:
(1) it involves mental functions (e.g., accumulation of knowledge) and (2) may
affect future physical actions.
Animal semiosis include icons (e.g., visual
images and sound patterns), and indexes, which are associations between classes
of objects, as well as between classes of objects and actions [19]. The
knowledge about objects, their associations, and possible modifications (Umwelt) is stored in the individual memory but it is not
transmitted to other animals because animals do not have the language capacity.
Only humans fully crossed the transition zone from animal to social semiosis and developed symbolic languages for efficient
horizontal communication [19]. Birds and mammals can use a few symbols, but
these seem to be limited to a small number of biological functions (e.g.,
danger warnings and sexual courtship).
Individual memory is not heritable, and thus, the Umwelt
cannot be transferred genetically to the next generation. However, development
of Umwelt can be strongly constraint by heritable
features of the body. Because organs are animal tools, this body-mind link is
analogous to the law of the instrument, which states that ―When the only
tool you have is a hammer, it is tempting to treat everything as if it were a
nail‖ [51]. In addition to effectors (legs, tails, mouth), sense organs
are highly important as constraints for the developing Umwelt.
The structure, sensitivity, and resolution of senses determines
what patterns an animal will be able to learn in its individual life. The size
of the body, life span, and movement speed also contribute to the perception
and interpretation of the world. Thus, animals of different sizes (e.g., a cow
and ant) perceive the same environment (meadow) in entirely different ways [2].
Because of these heritable constraints, Umwelten of
the progeny appear very similar to the Umwelten of
their parents (if they remain in the same habitat) even in the absence of
inter-generational communication. The ability of animals to classify and model
external objects emerges through recursive sensory-motor operations, and therefore,
stable cognitive states can be characterized as eigenvectors or eigenbehaviors [8,52]. The
cognitive dynamic system depends on heritable neural architecture; thus eigenbehaviors can be well reproduced between generations. But
environmental factors also affect the dynamics both directly during the
morphogenesis of the brain and indirectly through the signals from sense
organs. As a result, eigenbehaviors are flexible
enough and can be adjusted to the changing environment.
In contrast to Umwelt, the Innenwelt
has a substantial heritable component in organisms. Organisms need robust
methods for producing body parts, organs, and tissues, and these methods should
remain functional in variable environments as well as in variable conditions
within the body over long evolutionary times. Adaptive evolutionary changes in
one organ should not affect the methods for generating other organs, because
otherwise the species may lose its adaptability. High robustness can be
achieved only by functional modularity, which means isolating a given
functional sub-network from external effects [53]. This can be achieved by
passive isolation (breaking signaling links) as well as by active compensatory
responses within the sub-network. Functional modules can be compared with
encapsulated objects in object-oriented
programming languages (e.g., C++ or Java). Programmed objects have
pre-determined inputs and outputs and cannot crash even if other parts of the
code contain errors. Similarly, developmental modules are mostly independent
from other processes but can be initiated by pre-determined specific factors
(e.g., growth factors or genes). Manipulations of these factors may induce
organs in unusual places. For example, the antennapedia
mutation of Drosophila transforms antennae into legs [54]. Developmental
modules can be viewed as an ideal representations of
future organs because the organ is already ―drafted‖ and it takes a
single switch to initiate its construction. These ideal representations are
similar to mental representations; and thus, I view developmental modules as
components of animal semiosis rather than vegetative semiosis.
Considering levels of vegetative, animal, and social semiosis,
where can we place the semiosis of programmed
artificial devices? As components of human semiosis,
they belong to the social symbolic level. However, if taken alone artificial
devices can operate only at vegetative and animal level. Because most machines
cannot learn and evolve, their functional information has human origin. Even computer
viruses, which is the only class of autopoietic artificial systems, are not capable of adaptive
evolution. Machines can process icons and indexes supplied by humans, but most
of them do not develop their own icons and indexes. However, there are trainable
computers which can learn to classify objects or optimize actions for reaching
certain goals [11]. They can find non-trivial solutions and adapt to situations
unforeseen by designers. If such machines are supplied with both sensors and
effectors then, theoretically, they can start developing new sequences of
actions for reaching certain goals. However humans have not made any artificial
agents capable of autonomous adaptive evolution. Possibly, this kind of agents
will be created by imitating living organisms, a strategy known as synthetic
biology [55,56]. A major step towards creation of
synthetic organisms was the synthesis of a bacterial chromosome by Venter [57].
Cybernetics and biosemiotics deviate substantially
from the old paradigm of science, which is based on objective observations and
experiments. Agents are subjective beings, and it is not possible to understand
them well in pure objective notions. Our knowledge on the goals, perceptions,
and possible actions of agents is limited (except for simple human-made
agents), thus our predictions are uncertain, and the value of any control is
limited. Objectivism’s axiom of identity (A = A) does not hold with
agents because they evolve, learn, and change before our eyes. Thus, if we want
to include agents into the sphere of science we need to update the philosophy
of science.
An alternative approach to the traditional objectivist epistemology was
developed within theories of pragmatism and constructivism. Pragmatism starts
the argument from subjective goals and values of agents: ―truth is that
which works‖ [58]. In particular, knowledge of an agent is ―true‖
if it is useful (i.e., helps to achieve goals). Constructivism is
similar to pragmatism because it emphasizes the usefulness of knowledge,
however it does not consider ―truth‖ and ―usefulness‖
as equivalents. It can be best described by the words attributed to George Box:
―all models are wrong but some are useful‖. According to radical
constructivism, knowledge is constructed rather than discovered from the world
[14]. Different organisms and species develop their own models of reality;
thus, according to Maturana,
the universe turns into a multiverse
[59]. This notion of multiverse is consistent with
the idea of Uexküll that organisms develop and
use different models of their environment (Umwelten)
[2].
However, both pragmatism and constructivism have a serious flaw: they
underestimate or totally ignore the role of logic as a creative force. Pragmatism
has reduced truth to usefulness, and radical constructuvism
does not view truth as a useful concept. This prompted Brier to return to the
objective idealism of Charles Peirce which included ―synechism,
an evolutionary perspective, and a pragmatic(istic)
epistemology‖ [14]. Chance (potentiality) and laws (habits of the
universe) are assumed to be objective components of the world (its Firstness and Secondness,
respectively), and new laws emerge as the universe evolves. Finally, the world
includes observers/agents who integrate chances and laws into useful
representations and habits (Thirdness). Brier
interprets Firstness as ―pure feeling‖
and as a chance in quantum field theory; Secondness
is represented by classical kinematics, thermodynamics, resistance, and will;
and Thirdness corresponds to chemical sciences. On
the top of these three levels of ontology Brier proposed to consider two
additional levels: the fourth level corresponds to living organisms capable of
semiotic interactions, and the fifth level belongs to human language and
rationality [14].
Objective idealism provides the comfort of objectivity of human
knowledge, however it has some problems. First, physical laws are models of
nature developed by agents (as it is assumed by constructivism), rather that
parts of the nature itself. Models reflect real regularities in nature, but
they are not equivalent to these regularities. Models are proposed, tested, and
get accepted by agents if they provide useful and reliable results. However,
they are discarded if better models (i.e., simpler, more accurate, and
applicable in a broader area) become available. Also, new models have almost
nothing in common with previous models [60]. For example, the string theory has
no similarity to the
An alternative way of combining constructivism and pragmatism with logic
is to view logic as a useful tool that facilitates creative evolution of
communicating agents [36]. In organisms, logic is used to derive novel
behaviors that are more likely to be successful than behaviors selected by
chance. At the level of knowledge, logic is used to derive new statements that
are more likely to be true than randomly assembled statements. In mathematical
logic, derived statements are always true if they are
based on true initial statements (axioms). Self-organization
and eigenbehavior are examples of internal logic in
agents. Self-organization rules determine what modifications of organs are
possible within short-term individual development or long-term evolution. For
example, certain rules in the evolution of plant leaves were identified by Meyen [61]. Similarly, not all possible distinctions in the
environment can be detected by autonomous agents, but only those that
correspond to eigenbehaviors [62].
The usefulness of logic belongs to the meta-level compared to the
usefulness of actions (i.e., it represents adaptability rather than
adaptation), and it is selected at the time scales of macroevolution. In
particular, rules of self-organization and eigenbehavior
are subject to selection at the level of lineages rather than individual
organisms because some of these rules yield higher adaptability and broader
diversification within certain lineages [63,64]. The
usefulness of logic is more related to its internal organization rather than to
its interface with actions. Thus, humans often use aesthetic criteria to select
logical systems based on their universality, simplicity, and richness.
In contrast to utility-oriented pragmatism and logic-oriented logical
positivism, I assume that the evolution of semiotic systems is driven by two
interdependent factors: utility and logic [36]. Logic determines possible or
preferred directions of change, and utility helps to find working solution
among these possibilities. However, the usefulness of logical rules is limited
to the set of routinely communicating agents, which I called
―communication system‖ [36], and therefore it provides only local
objectivity. The world has certain regularities,
however each kind of agents perceives different sets of these regularities. Two
communication systems may have partially overlapping ontologies,
and this overlap allows them to establish limited communications. But it is
hardly possible to develop a universal ontology that covers all regularities of
the world because such ontology would be infinite, and hence, not operational
(because of infinite processing time). This worldview can be called
―local realism‖ because the knowledge about the world is shared
locally within a certain communication system. In rare cases, it can be
transferred to another communication system with similar logic and ontology. But
there is no way to transfer knowledge between entirely unrelated communication
systems (e.g., we cannot explain to an ant that ―the snow is white‖).
According to this worldview, scientific knowledge is objective locally, i.e.,
in relation to the communication system of science with its current rules of
experiment design, statistical inference, and theoretical generalization. However,
these rules evolve as science expands, and any change prompts for revisions in
the whole volume of scientific knowledge. Local realism is compatible with the
scientific method, but it also allows the existence of alternative ontologies in other communication systems (e.g., in
religions and local cultures).
8. Conclusions
The synthesis of biosemiotics and cybernetics
is seen as integration of their efforts in the study of agents, a new
cross-disciplinary ontological entity. First, the focus of biosemiotics
should be shifted from living organisms to agents in general, which all belong
to the pragmasphere or functional universe. Second,
agents need to be considered in the context of their hierarchy and origin
because their semiosis can be inherited from parental
agents or induced by higher-level agents. In particular, the absence of
learning in isolated individual agents does not mean that they are not
semiotic. Third, the cybernetics needs to shift from the computational paradigm
to the functional paradigm. I believe that the notion of functional
information, which is a set of signs used by agents to encode and control their
functions, can be a starting point for
this transition. And fourth, following the evolutionary principles, multiple
levels of semiosis should be distinguished to
classify molecular agents, cells, plants, animals, humans, and machines. The
lowest vegetative levels of semiosis is the most
intriguing, as it provides clues for the origin of life, and also serves as a
basis for the emergence of animal, and social levels of semiosis.
Vegetative semiosis differs substantially from higher
levels of semiosis because signs are recognized and
interpreted via direct code-based matching and are not associated with ideal
representations (models and classifications) of objects. Due of these
differences, I consider a separate classification of signs at the vegetative
level, which includes proto-icons, proto-indexes, and proto-symbols. Higher
levels of semiosis which include classifications,
models, and logic are grounded in the vegetative semiosis.
Acknowledgements
I would like to thank Kalevi Kull,
References and Notes
1. Schrödinger, E. What is life? The Physical Aspect of the
Living Cell;
2. Uexküll, J. The
theory of meaning. Semiotica 1982,
42, 25–82.
3. Wiener, N. Cybernetics or Control and Communication in the Animal
and the Machine; Wiley & Sons Inc.:
4. Shannon, C.E. A mathematical theory of
communication. Bell Syst. Tech. J. 1948, 27,
379–423, 623–656.
5. Sebeok, T.A. Biosemiotics:
its roots, proliferation and prospects. Semiotica
2001, 134, 61–78.
6. Brazhnik, O.; Jones, J.F. Anatomy of data
integration. J. Biomed. Inform. 2007, 40, 252–269.
7. Heylighen, F.; Joslyn,
C. Cybernetics and second-order cybernetics. In: Encyclopedia of Physical
Science and Technology; Meyers, R.A., Ed.; Academic Press:
8. von Foerster, H. Understanding: Essays
on Cybernetics and Cognition; Springer:
9. Maturana, H.; Varela, F. Autopoiesis and Cognition: the Realization of the
Living; D. Reidel Publishing Co.:
10. Emmeche, C. The Garden in the Machine:
The Emerging Science of Artificial Life;
11. Cariani, P. Towards an evolutionary
semiotics: the emergence of new sign-functions in organisms and devices. In
Evolutionary Systems; Vijver, G.V.; Salthe, S.; Delpos, M., Eds.; Kluwer:
12. Sharov, A.A. Biosemiotics:
functional-evolutionary approach to the problem of the sense of information. In Biosemiotics.
The Semiotic Web 1991; Sebeok, T.A., Umiker-Sebeok, J., Eds.; Mouton de Gruyter:
13. Barbieri, M. The Organic Codes: an
Introduction to Semantic Biology;
14. Brier, S. Cybersemiotics: Why
Information Is Not Enough;
15. Peirce, C.S. The Essential Peirce: Selected Philosophical
Writings;
16. Collier, J. Causation is the transfer of Information. In Causation
and Laws of Nature; Sankey, H., Ed.; Kluwer:
17. Brillouin, L. Science and Information
Theory. Academic Press:
18. Burgin, M. Super-recursive Algorithms; Springer:
19. Kull, K. Vegetative, animal, and cultural semiosis:
the semiotic threshold zones. Cogn. Semiotic. 2009,
4, 8–27.
20. Rosen, R. Dynamical System Theory in Biology; Wiley-Interscience:
21. Rashevsky, N. Mathematical Biophysics;
22. Sharov, A.A. Genetic gradualism and the
extraterrestrial origin of life. J. Cosmol. 2009,
5, 833–842.
23. Hoffmeyer, J. Biosemiotics:
an Examination into the Signs of Life and the Life of Signs;
24. Barbieri, M. Is the cell a semiotic
system? In Introduction to Biosemiotics.
The New Biological Synthesis; Barbieri, M., Ed.;
Springer:
25. Gould, S.J.; Eldredge, N. Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology 1977, 3, 115–151.
26. Dawkins, R. The Blind Watchmaker; Norton:
27. Weber, B.H. On the emergence of living systems.
Biosemiotics 2009, 2,
343–359.
28. Sharov, A.A. Genome increase as a clock
for the origin and evolution of life. Biol. Direct.
2006, 1, 17.
29. Consortium, G.O. The Gene Ontology project in
2008. Nucleic. Acids.
Res. 2008, 36,
D440–D444.
30. Vo, N.; Goodman, R.H. CREB-binding protein
and p300 in transcriptional regulation. J. Biol. Chem. 2001, 276,
13505–13508.
31. Ahmadjian, V. Tribouxia:
Reflections on a perplexing and controversial lichen photobiont.
In Symbiosis. Mechanisms and Model Systems;
Seckbach, J., Ed.; Kluwer:
32. Weber, N.A. Gardening Ants, the Attines;
American Philosophical Society:
33. Klepzig, K.D.; Moseri,
J.C.; Lombarder, F.J.; Hofstetter,
R.W.; Ayres, M.P. Symbiosis and competition: complex interactions among
beetles, fungi and mites. Symbiosis 2001, 30, 83–96.
34. Turchin, V.F. The
Phenomenon of Science.
35. Smith, J.M.; Szathmáry, E. The
Major Transitions in Evolution; W.H. Freeman/Spektrum,
36. Sharov, A.A. Role of utility and inference
in the evolution of functional information. Biosemiotics
2009, 2, 101–115.
37. Hoffmeyer, J.; Emmeche,
C. Code-duality and the semiotics of nature. In On Semiotic Modeling;
38. Danesi, M.; Perron,
P. Analyzing Cultures: an Introduction and Handbook;
39. Witzany, G. Biocommunication
and Natural Genome Editing; Springer:
40. Bateson, G. Steps to an Ecology of Mind; Collected Essays in
Anthropology, Psychiatry, Evolution, and Epistemology;
41. Arrabales, R.; Ledezma,
A.; Sanchis, A. ConsScale:
A pragmatic scale for measuring the level of consciousness in artificial
agents. J. Conscious. Stud. 2010, 17, 131–164.
42. Eco, U. A Theory of Semiotics;
43. Pattee, H.H. How Does a Molecule Become a
Message? Dev. Biol. Supp. 1969, 3, 1–16.
44. Sharov, A.A. Coenzyme autocatalytic
network on the surface of oil microspheres as a model for the origin of life. Int.
J. Mol. Sci. 2009, 10, 1838–1852.
45.
46. Szathmáry, E. The
first replicators. In Levels of Selection in Evolution; Keller,
L., Ed.;
47. Jablonka, E.; Szathmáry,
E. The evolution of information storage and heredity. Trends Ecol. Evol. 1995,
10, 206–211.
48. Szathmáry, E. The
origin of replicators and reproducers. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2006, 361,
1761–1776.
49. Rigali, S.; Schlicht,
M.; Hoskisson, P.; Nothaft,
H.; Merzbacher, M.; Joris,
B.; Titgemeyer, F. Extending the classification of
bacterial transcription factors beyond the helix-turn-helix motif as an
alternative approach to discover new cis/trans
relationships. Nucleic. Acids.
Res. 2004, 32, 3418–3426.
50. Kull, K. Umwelt. In The Routledge Companion to Semiotics; Cobley,
P., Ed.; Routledge: Ney
51. Maslow, A.H. Psychology of Science. A
Reconnaissance. Harper:
52. Rocha, L.M. Eigenbehavior and symbols. Syst.
Res. 1996, 13, 371–384.
53. Schlosser, G.; Wagner, G.P. Introduction: The modularity concept in
developmental and evolutionary biology. In Modularity in Development and
Evolution; Schlosser, G., Wagner, G.P., Eds.;
54. Gibson, G. Insect evolution: Redesigning the fruitfly.
Curr.
Biol. 1999, 9,
R86–R89.
55. Cao, H.; Romero-Campero, F.J.; Heeb, S.; Camara, M.; Krasnogor, N. Evolving cell models for systems and
synthetic biology. Syst. Synth. Biol. 2010,
4, 55–84.
56. Basu, S.; Gerchman,
Y.; Collins, C.H.;
57. Gibson, D.G.; Benders, G.A.; Andrews-Pfannkoch,
C.; Denisova, E.A.; Baden-Tillson,
H.; Zaveri, J.; Stockwell,
T.B.; Brownley, A.; Thomas, D.W.; Algire,
M.A.; Merryman, C.; Young, L.; Noskov,
V.N.; Glass, J.I.; Venter, J.C.; Hutchison, C.A.; Smith, H.O.; Complete
chemical synthesis, assembly, and cloning of a Mycoplasma
genitalium genome. Science 2008, 319,
1215–1220.
58. James, W. Pragmatism, A New Name for
Some
59. Maturana, H.R. The
biological foundation of self-consciousness and the physical domain of
existence. In Physics of Cognitive Process; Caianiello,
E.R., Ed.; World Scientific:
60. Kuhn, T.S. The Structure of Scientific
Revolutions.
61. Meyen, S.V. Fundamentals of Palaeobotany. Chapman and Hall:
62. Rocha, L.M. Selected self-organization. In Evolutionary Systems:
Biological and Epistemological Perspectives on Selection and Self-organization;
Salthe, S., Van de Vijver,
G., Delpos, M., Eds.; Kluwer
Academic Publishers: Dordrecth, The Netherlands,
1998; pp. 341–358.
63. Eldredge, N. Unfinished Synthesis:
Biological Hierarchies and Modern Evolutionary Thought;
64. Depew, D.J.; Weber, B.H. Darwinism Evolving: Systems Dynamics and
the Genealogy of Natural Selection; MIT Press:
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