Stefan Artmann

Cells as semantic systems.

BACKGROUND We consider cells as biological systems that process information by means of molecular codes. Many studies analyze cellular information processing exclusively in syntactic terms (e.g., by measuring Shannon entropy of sets of macromolecules), and abstract completely from semantic aspects that are related to the meaning of molecular information. METHODS This mini-review focuses on semantic aspects of molecular information, particularly on codes that organize the semantic dimension of molecular information. First, a general conceptual framework for describing molecular information is proposed. Second, some examples of molecular codes are presented. Third, a mathematical approach that makes the identification of molecular codes in reaction networks possible, is developed. RESULTS By combining a systematic conceptual framework for describing molecular information and a mathematical approach to identify molecular codes, it is possible to give a formally consistent and empirically adequate model of the code-based semantics of molecular information in cells. GENERAL SIGNIFICANCE Research on the semantics of molecular information is of great importance particularly to systems biology since molecular codes embedded in systems of interrelated codes govern main traits of cells. Describing cells as semantic systems may thus trigger new experiments and generate new insights into the fundamental processes of cellular information processing. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

The Phi-Bot: A Robot Controlled by a Slime Mould

Information processing in natural systems radically differs from current information technology. This difference is particularly apparent in the area of robotics, where both organisms and artificial devices face a similar challenge: the need to act in real time in a complex environment and to do so with computing resources severely limited by their size and power consumption. Biological systems evolved enviable computing capabilities to cope with noisy and harsh environments and to compete with rivalling life forms. Information processing in biological systems, from single-cell organisms to brains, directly utilises the physical and chemical processes of cellular and intracellular dynamics, whereas that in artificial systems is in principle independent of any physical implementation. The formidable gap between artificial and natural systems in terms of information processing capability [1] motivates research into biological modes of information processing. Hybrid artifacts, for example, try to overcome the theoretic and physical limits of information processing in solid-state realisations of digital von Neumann machines by exploiting the self-organisation of naturally evolved systems in engineered environments [2, 3]. This chapter presents a particular unconventional computing system, the Φbot, whose control is based on the behaviour of the true slime mould Physarum polycephalum. The second section gives a short introduction to the informationprocessing capabilities of this organism. The third section describes the two generations of the Φ-bot built so far. To discuss information-theoretic aspects of this robot it is useful to sketch the concept of bounded computability that

Basic Semiosis as Code-Based Control

Though the formal coherence and empirical utility of Marcello Barbieri’s concept of organic code have been starting to become established, a general conception of how the semantics of organic codes is related to the pragmatics of their use is still missing. Barbieri took a first step towards such a conception by distinguishing three types of semiosis in living systems: manufacturing, signalling, and interpretive semiosis. This paper integrates Barbieri’s distinction into Roman Jakobson’s systematization of possible functions of messages in order to propose a general conception of possible types of semiosis in living systems. As a result, Barbieri’s thesis that manufacturing and signalling semiosis are basic types of semiosis, can be confirmed and completed communication-theoretically.

Computing Codes Versus Interpreting Life

One of the most important aims of biosemiotics is to provide a conceptual framework for synthesizing the biological knowledge of living systems. This goal can be assumed achievable in (at least) two alternative ways, both of which are actually followed by working biosemioticians. Either biosemiotics is considered a philosophical revolution in biological thought that will change biology into an art of interpreting life, i.e. into an appreciation of organisms as semiotic beings whose structure, function, and behaviour is to be understood by participating in the communicative web of life. Or biosemiotics is considered from a model-theoretical point of view as an application of abstract semiotic concepts to a field of empirical research that is foreign to semiotics only for an anthropocentric understanding of signs. This paper sketches both biosemiotic ways of synthesizing biological knowledge and shows how they mirror a fundamental opposition, which has been intensely investigated by Jaakko Hintikka, between two answers to the question whether it is possible that language can be scientifically explained by linguistic means. It is argued that biosemiotics should postulate, as a starting-point of its research programme, a positive answer to this question. A model-theoretical sketch of how to develop a precise definition of organic codes, shall show that this postulate allows to elucidate the inner structure of biosemiotic concepts

A structuralistic approach to ontologies

It is still an open question how the relation between ontologies and their domains can be fixed. We try to give an account of semantic and pragmatic aspects of formal knowledge by describing ontologies in terms of a particular school in philosophy of science, namely structuralism. We reconstruct ontologies as empirical theories and interpret expressions of an ontology language by semantic structures of a theory. It turns out that there are relevant aspects of theories which cannot as yet be taken into consideration in knowledge representation. We thus provide the basis for extending the concept of ontology to a theory of the use of a language in a community.

Four Principles of Evolutionary Pragmatics in Jacob's Philosophy of Modern Biology

The French molecular biologist François Jacob outlined a theory of evolution as tinkering. From a methodological point of view, his approach can be seen as a biologic specification of the relation between laws, describing coherently the dynamics of a system, and contingent boundary conditions on this dynamics. From a semiotic perspective, tinkering is a pragmatic concept well-known from the information-theoretic anthropology of Claude Lévi-Strauss. In idealized contrast to an engineer, the tinkerer has to accept the concrete restrictions on his material resources as only gradually changeable constraints on his projects. Jacobian biopragmatics examines evolution as a biologic analogue to human tinkering devoid of any projecting subjectivity. To validate this analogy, four basic principles concerning main formal aspects of evolutionary objects, agents, histories, and consistency criteria are proposed.

Pragmatism and the Evolution of Semantic Systems

This paper discusses two classical pragmatists and their theories about the relation of semantics and pragmatics: Peirce’s evolutionary pragmatism and its naturalistic reconstruction by Dewey. Two questions are addressed: how do these philosophers understand the origin and development of meaning and how might their theories contribute to an explanation of the evolution of semantic systems? Pragmatism could play a very important role in our understanding of the dynamics of meaning if it integrated theories and models of structural science. This would lead to a new version of pragmatist thought, ‘structural pragmatism.’

Divergence versus Convergence of Intelligent Systems: Contrasting Artificial Intelligence with Cognitive Psychology

Artificial Intelligence (AI) and Cognitive Psychology (CP) are two sciences of intelligent systems that share many features. If we want, nevertheless, to contrast AI with CP, we must investigate differences between the strategies they follow in exploring intelligence. To do so, I transform the Turing Test into a more adequate intelligence test based on a necessary condition for intelligence, namely that intensions of second-order intentional predicates are observable in a system (sect. 2). I then contrast CP and AI by their criteria for progress in research on this necessary condition for intelligence (sect. 3).

A Philosophical Foundation for Ontology Alignments – The Structuralistic Approach

Together with formal ontologies, ontology alignments are utilized to interconnect distributed information systems. Despite their widespread use, it is not trivial to specify a formal semantics for ontology alignments—due to the disparity of the respective domains of the involved ontologies. There are already approaches to tackle this problem, but each is relying on different presumptions. In this paper we propose an interpretation of alignments in terms of theories as they are understood in the philosophy of science following the structuralistic notion of ontological reduction. We use this framework to identify an account of the necessary presumptions of every alignment semantics. These assumptions are then used as a basis for the comparison of existing alignment semantics.

Behavioural congruence in turing test-like human-computer interaction

Intensions of higher-order intentional predicates must be observed in a system if intelligence shall be ascribable to it. To give an operational definition of such predicates, the Turing Test is changed into the McCarthy Test. This transformation can be used to distinguish degrees of behavioural congruence of systems engaged in conversational interaction.