Showing papers in "Complexity in 1996"
TL;DR: An introduction to Boolean networks and their relevance to present-day experimental research is provided, bringing us closer to an understanding of complex molecular physiological processes like brain development and intractable medical problems of immediate importance.
Abstract: Molecular genetics presents an increasingly complex picture of the genome and biological function. Evidence is mounting for distributed function, redundancy, and combinatorial coding in the regulation of genes. Satisfactory explanation will require the concept of a parallel processing signaling network. Here we provide an introduction to Boolean networks and their relevance to present-day experimental research. Boolean network models exhibit global complex behavior, self-organization, stability, redundancy and periodicity, properties that deeply characterize biological systems. While the life sciences must inevitably face the issue of complexity, we may well look to cybernetics for a modeling language such as Boolean networks which can manageably describe parallel processing biological systems and provide a framework for the growing accumulation of data. We finally discuss experimental strategies and database systems that will enable mapping of genetic networks. The synthesis of these approaches holds an immense potential for new discoveries on the intimate nature of genetic networks, bringing us closer to an understanding of complex molecular physiological processes like brain development, and intractable medical problems of immediate importance, such as neurodegenerative disorders, cancer, and a variety of genetic diseases.
365 citations
TL;DR: This article defines the concept of an information measure and shows how common information measures such as entropy, Shannon information, and algorithmic information content can be combined to solve problems of characterization, inference, and learning for complex systems.
Abstract: This article defines the concept of an information measure and shows how common information measures such as entropy, Shannon information, and algorithmic information content can be combined to solve problems of characterization, inference, and learning for complex systems. Particularly useful quantities are the effective complexity, which is roughly the length of a compact description of the identified regularities of an entity, and total information, which is effective complexity plus an entropy term that measures the information required to describe the random aspects of the entity. Mathematical definitions are given for both quantities and some applications are discussed. In particular, it is pointed out that if one compares different sets of identified regularities of an entity, the ‘best’ set minimizes the total information, and then, subject to that constraint, minimizes the effective complexity; the resulting effective complexity is then in many respects independent of the observer. © 1996 John Wiley & Sons, Inc.
300 citations
99 citations
TL;DR: Any comprehensive understanding of biological phenomena requires an interpretation in evolutionary terms, as Theodosius Dobzhansky pointed out in his famous phrase: “Nothing in biology makes sense except in the light of evolution”.
Abstract: Information in biology has a quality, that distinguishes it from information in chemistry and physics. It comes in encoded form and it is processed in a way that is closely related to information technology and computer science. Biological information is essentially stored in genotypes and transferred to future generation through inheritance, and less directly through epigenetic processes. Cellular metabolism is interpreted straightforwardly as information processing. Information is closely related to complexity: more complex things require more information to build and to operate. Any comprehensive understanding of biological phenomena requires an interpretation in evolutionary terms, as Theodosius Dobzhansky [4] pointed out in his famous phrase: “Nothing in biology makes sense except in the light of evolution”. Understanding the complexity of biological systems is thus always incomplete if nothing is known about its origin.
70 citations
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TL;DR: This is the nal half of a review of selected problems in the theory of cellular automata, where selected problems are selected from the literature on automata design.
Abstract: This is the nal half of a review of selected problems in the theory of cellular automata.
TL;DR: It is shown that collective logical gates can be built in such a way that complex computation can be possible by means of the interplay between local interactions and the collective creation of a global field.
Abstract: Fluid neural networks can be used as a theoretical framework for a wide range of complex systems as social insects. In this article we show that collective logical gates can be built in such a way that complex computation can be possible by means of the interplay between local interactions and the collective creation of a global field. This is exemplified by a NOR gate. Some general implications for ant societies are outlined. © 1996 John Wiley & Sons, Inc.
TL;DR: A method for approximating a fitness landscapes as a superposition of "elementary" landscapes as well as an application to RNA free energy landscapes is presented.
Abstract: We present a method for approximating a fitness landscape as a superposition of “elementary” landscapes. Given a correlation function of the landscape in question we show that the relative amplitudes of contributions with p-ary interactions can be computed. We show an application to RNA free energy landscapes. © 1996 John Wiley & Sons, Inc.
TL;DR: A new model of large-scale evolution is presented that involves a simple ecosystem formed by N species related among them through some connections and it is shown that power-laws in the extinction patterns are obtained, as well as I/f-noise dynamics.
Abstract: A new model of large-scale evolution is presented. It involves a simple ecosystem formed by N species related among them through some connections. Random extinction and diversification are involved and it is shown that power-laws in the extinction patterns are obtained, as well as I/f-noise dynamics. Some implications for both theoretical studies and paleobiological data are discussed.
IBM1
TL;DR: A more concrete version of algorithmic information theory in which one can actually run on a computer the algorithms in the proofs of a number of key information-theoretic incompleteness theorems is presented in this article.
Abstract: We present a much more concrete version of algorithmic information theory in which one can actually run on a computer the algorithms in the proofs of a number of key information-theoretic incompleteness theorems
TL;DR: Some of the molecular events involved in initial steps of pharmacological responses are examined here as emergent properties of the triad active site-ligand-water, a complex system seldom viewed as such.
Abstract: Three partners are involved in any pharmacological phenomenon examined at the molecular level, namely a functional biomacromolecule (e.g., receptor or enzyme), a ligand molecule whose binding to the active site of the macromolecule triggers a response, and water which acts as a structural component and as a solvent. Some of the molecular events involved in initial steps of pharmacological responses are examined here as emergent properties of the triad active site-ligand-water, a complex system seldom viewed as such. For example, the exquisite selectivity and efficiency of long- and short-range recognition of ligands may rest on more than simple random encounters. The emergent property of function suggests the possibility that active sites are maintained near criticality by low-levels of endogenous ligands.
TL;DR: Several aspects of this conception of a world which is understandable by human reasoning are studied, among them the conjecture that randomness in physics can be constructively reinterpreted to correspond to uncomputability and undecidability in mathematics.
Abstract: Throughout the ups and downs of scientific world conception there has been a persistent vision of a world which is understandable by human reasoning. In a contemporary, recursion theoretic, comprehension, the term “reasoning” is interpretable as “constructive” or, more specifically, “mechanically computable.” An expression of this statement is the assumption that our universe is generated by the action of some deterministic computing agent; or, stated pointedly, that we are living in a computer-generated universe. Physics then reduces to the investigation of the intrinsic, “inner view” of a particular virtual reality which happens to be our universe. In this interpretation, formal logic, mathematics and the computer sciences are just the physical sciences of more general “virtual” realities, irrespective of whether they are “really” realized or not. We shall study several aspects of this conception, among them the conjecture that randomness in physics can be constructively reinterpreted to correspond to uncomputability and undecidability in mathematics. We shall also attack the nonconstructive feature of classical physics by showing its inconsistency. Another concern is the modeling of interfaces, i.e., the means and methods of communication between two universes. On a speculative level, this may give some clue on such notorious questions such as the occurrence of “miracles” or on the “mind-body problem.”
TL;DR: It is argued that an intermediate level of complexity, that of functional groups, exists between atoms and molecules, and is particularly noteworthy in biological macromolecules and correspond to basins of attraction resulting from a complex interplay between intramolecular and intermolecular interactions.
Abstract: The hierarchy of chemical systems is examined. It is argued that an intermediate level of complexity, that of functional groups, exists between atoms and molecules. Molecular properties have an emergent nature, e.g., the “chameleonic behavior.” Aggregates of molecules and solutions also behave as complex systems. Emergent properties are particularly noteworthy in biological macromolecules and correspond to basins of attraction resulting from a complex interplay between intramolecular and intermolecular interactions.
TL;DR: In this paper, the authors presented a lecture at the Santa Fe Institute, Santa Fe, New Mexico, which was videotaped; this lecture was an edited transcript of the lecture.
Abstract: Lecture given Friday 7 April 1995 at the Santa Fe Institute, Santa Fe, New Mexico. The lecture was videotaped; this is an edited transcript.
IBM1
TL;DR: This article explores the fundamental dynamics of the ideas set forth in Part I (Complexity Vol. 2, Issue I) through the use of numerical models with feedback.
Abstract: This article explores the fundamental dynamics of the ideas set forth in Part I (Complexity Vol. 2, Issue I) through the use of numerical models with feedback.