Adaptability and the integration of computer-based information processing into the dynamics of organizations

The structure of a system influences its adaptability. An important result of adaptability theory is that subsystem independence increases adaptability [Conrad, M., 1983. Adaptability. Plenum Press, New York]. Adaptability is essential in systems that face an uncertain environment such as biological systems and organizations. Modern organizations are the product of human design. And so it is their structure and the effect that it has on their adaptability. In this paper we explore the potential effects of computer-based information processing on the adaptability of organizations. The integration of computer-based processes into the dynamics of the functions they support and the effect it has on subsystem independence are especially relevant to our analysis.     

The brain-machine disanalogy

The comparative study of information processing in brains and machines leads to a picture in which disanalogies are more fundamental than analogies. The major dichotomy is between evolvability and programmability. Brain models, to be tenable, must pass an extended Turing test in which the capacity to self organize through the Darwinian mechanism of variation and selection is a key element. Programmable machines that simulate the type of structure-function relations that allow evolution to occur are, however, too inefficient in their use of resources for problem solving to support cognitive abilities comparable to those of biological organisms. Furthermore, real evolutionary systems are open in that it is always possible for them to tap previously unexploited physical interactions for computing. Nevertheless, computer simulation provides a powerful tool for studying brain function; and non-programmable designs that exploit the high efficiency, high adaptability domain of computing are in principle possible.     

Biological information processing: the use of information for the support of function

In biological systems, the processing and use of information has evolved out of the need for survival in the face of an uncertain environment. As a consequence, the information-function relationship in these systems is shaped by their adaptability characteristics. In contrast, the information-function relationship in man-designed, goal-oriented organizational systems depends on the ability of the information processing system to support the achievement of the organization's goals. In this paper we use results from adaptability theory in the analysis of control-related aspects of the information-function relationship in man-designed organizational systems. In particular, we use a conceptual model of organizational control to characterize features of functional and control structures and their effect on the adaptability of these systems. The concept of implicit control and a design principle for adaptability-enhancing information systems are derived for this analysis.     

The mutation buffering concept of biomolecular structure

Redundant elements in proteins and nucleic acids serve to buffer the effect of point mutations on features of conformation critical for function. Mutation buffering associated with mechanistically redundant amino acids facilitates the evolution of proteins. Such redundant amino acids accumulate by hitch-hiking along with the evolutionary advances which they facilitate. Redundancies in DNA (such as introns and repetitive DNA) prevent extraneous sequence dependent conformational effects from interfering with readout. They also facilitate regulatory evolution. According to the mutation buffering concept biological organizations are selected to facilitate evolution. As a consequence biological information processing is very different from information processing in man-made computers. The link between molecular conformation, evolutionary processes, and information processing is formulated in terms of a tradeoff principle. By utilizing mutation buffering biological systems sacrifice programmability; by achieving programmability digital computers make mutation buffering computationally expensive and hence sacrifice evolutionary adaptability.     

The brain-machine disanalogy revisited.

Michael Conrad was a pioneer in investigating biological information processing. He believed that there are fundamental lessons to be learned from the structure and behavior of biological brains that we are far from understanding or have implemented in our computers. Accumulation of advances in several fields have confirmed his views in broad outline but not necessarily in some of the strong forms he had tried to establish. For example, his assertion that programmable computers are intrinsically incapable of the brain's efficient and adaptive behavior has not received much examination. Yet, this is clearly a direction that could afford much insight into fundamental differences between brain and machine. In this paper, we pay tribute to Michael, by examining his pioneering thoughts on the brain-machine disanalogy in some depth and from the hindsight of a decade later. We argue that as long as we stay within the frame of reference of classical computation, it is not possible to confirm that programmability places a fundamental limitation on computing power, although the resources required to implement a programmable interface leave fewer resources for actual problem-solving work. However, if we abandon the classical computational frame and adopt one in which the user interacts with the system (artificial or natural) in real time, it becomes easier to examine the key attributes that Michael believed place biological brains on a higher plane of capability than artificial ones. While we then see some of these positive distinctions confirmed (e.g. the limitations of symbol manipulation systems in addressing real-world perception problems), we also see attributes in which the implementation in bioware constrains the behavior of real brains. We conclude by discussing how new insights are emerging, that look at the time-bound problem-solving constraints under which organisms have had to survive and how their so-called 'fast and frugal' faculties are tuned to the environments that coevolved with them. These directions open new paths for a multifaceted understanding of what biological brains do and what we can learn from them. We close by suggesting how the discrete event modeling and simulation paradigm offers a suitable medium for exploring these paths.     

The analysis of distributed control and information processing in adaptive systems: a biologically motivated approach.

Biological systems have evolved hierarchical, distributed control structures that greatly enhance their adaptability. Two important determinants of biological adaptability considered here are: (i) the pattern of distribution of self-control capabilities; (ii) the degree of programmability of information processing. In this paper we model organizations as goal-oriented, adaptive systems, possessing properties similar to those of biological systems. We use the notion of implicit control (defined as the capability of self-control that is embedded in a system's own dynamics) in the analysis of the impact of specific patterns of distribution of control and information processing on the adaptability of organizations. A principle of design of organizational information systems, that captures important aspects of adaptability-preserving strategies of information processing in biological systems, is stated in terms of the implicit control concept.     

Biological computation: hearts and flytraps

The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.

     

Computing an organism: on the interface between informatic and dynamic processes.

The importance of interactions between processes at diverse space and time scales in biological information processing is a recurrent theme in the work of Michael Conrad (BioSystems, 1995: 35, 157-160; BioSystems, 1999: 52, 99-100). In this paper I present some results of explicit computational models that aim to capture and exploit some of the essential features of such multi-level processes. In the model formulation, I try to minimize the explicit definition of inter-level interactions, while providing the possibility of such interactions to develop. As often argued by Conrad inter-level interactions limit programmability. Indeed, we use an evolutionary process to derive the specific models. We study morphogenesis. We show that the interplay between cell adhesion and cell differentiation provides interesting mechanisms for morphogenesis. We also show that the interplay can both reduce and enhance small random fluctuations. We show that unequal cell cleavage in the early embryo-genesis reduces inter-individual variation of the morphemes developed from the same 'genome'. Our results suggest that, during evolution, the interplay between levels is exploited while it is at the same time reduced so as to give a certain primacy to inherited information.     

Towards computing with proteins

Can proteins be used as computational devices to address difficult computational problems? In recent years there has been much interest in biological computing, that is, building a general purpose computer from biological molecules. Most of the current efforts are based on DNA because of its ability to self‐hybridize. The exquisite selectivity and specificity of complex protein‐based networks motivated us to suggest that similar principles can be used to devise biological systems that will be able to directly implement any logical circuit as a parallel asynchronous computation. Such devices, powered by ATP molecules, would be able to perform, for medical applications, digital computation with natural interface to biological input conditions. We discuss how to design protein molecules that would serve as the basic computational element by functioning as a NAND logical gate, utilizing DNA tags for recognition, and phosphorylation and exonuclease reactions for information processing. A solution of these elements could carry out effective computation. Finally, the model and its robustness to errors were tested in a computer simulation. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

Process, structure and context in relation to integrative biology.

This paper seeks to provide some integrative tools of thought regarding biological function related to ideas of process, structure, and context. The incorporation of linguistic and mathematical thinking is discussed within the context of managing thinking about natural systems as described by Robert Rosen. Examples from ecology, protein networks, and liver function are introduced to illustrate key ideas. It is hoped that these tools of thought, and the further work needed to mobilise such ideas, will continue to address a number of issues raised and pursued by Michael Conrad, such as the seed-germination model and vertical information processing.     

Bootstrapping on the adaptive landscape.

Different versions of a gene or of a multigenic system may be essentially equivalent so far as the specific function of the structures which they code for or control is concerned, but very different with respect to their amenability to evolution. The structural features which increase evolutionary amenability are a disadvantage to the organism in terms of energy. Nevertheless, they accumulate in the course of evolution as a consequence of hitchhiking along with the desirable traits whose evolution they make possible. This is the bootstrap principle of evolutionary adaptability. In terms of the adaptive landscape bootstrapping corresponds to populations evolving in such a way that they occupy regions of the landscape which are more amenable to evolutionary hill climbing. The bootstrapping idea has implications for structure-function relations in a number of complex biological information processing systems, including biochemical systems, the immune system, and the brain. Bootstrapping is also discussed in connection with the origin of information processing (the origin of life) and in connection with possible designs for macromolecular computing systems.     

Biomolecular computing systems: principles, progress and potential

The task of information processing, or computation, can be performed by natural and man-made 'devices'. Man-made computers are made from silicon chips, whereas natural 'computers', such as the brain, use cells and molecules. Computation also occurs on a much smaller scale in regulatory and signalling pathways in individual cells and even within single biomolecules. Indeed, much of what we recognize as life results from the remarkable capacity of biological building blocks to compute in highly sophisticated ways. Rational design and engineering of biological computing systems can greatly enhance our ability to study and to control biological systems. Potential applications include tissue engineering and regeneration and medical treatments. This Review introduces key concepts and discusses recent progress that has been made in biomolecular computing.     

Conduction pathways in microtubules, biological quantum computation, and consciousness.

Technological computation is entering the quantum realm, focusing attention on biomolecular information processing systems such as proteins, as presaged by the work of Michael Conrad. Protein conformational dynamics and pharmacological evidence suggest that protein conformational states-fundamental information units ('bits') in biological systems-are governed by quantum events, and are thus perhaps akin to quantum bits ('qubits') as utilized in quantum computation. 'Real time' dynamic activities within cells are regulated by the cell cytoskeleton, particularly microtubules (MTs) which are cylindrical lattice polymers of the protein tubulin. Recent evidence shows signaling, communication and conductivity in MTs, and theoretical models have predicted both classical and quantum information processing in MTs. In this paper we show conduction pathways for electron mobility and possible quantum tunneling and superconductivity among aromatic amino acids in tubulins. The pathways within tubulin match helical patterns in the microtubule lattice structure, which lend themselves to topological quantum effects resistant to decoherence. The Penrose-Hameroff 'Orch OR' model of consciousness is reviewed as an example of the possible utility of quantum computation in MTs.     

Do biomolecules process information differently than synthetic organic molecules?

This paper compares information/signal processing in synthetic and biological molecules. The role of conformation-based (shape-based) mechanisms and electrostatic interactions in molecular recognition is discussed. In biological electron transfer, the 'electron shuttle'-mediated mechanism is contrasted with the mechanism based on pre-formed 'electron wires'. While biological information processing is thought to be more distributed (less discrete), an example of molecular switch is presented: visual transduction. We further speculate that visual transduction may be implemented in the form of a switch based on electrostatic interactions. The concept of intelligent materials is discussed with the well-known Bohr effect of hemoglobin oxygenation. Based on these examples, we argue that there are no fundamental differences between synthetic and biological molecules in their mode of information processing. In the pursuit of novel paradigms of molecular information processing, we also perceive no conflicts in developing molecular devices that emulate the switching function of conventional microelectronic devices.     

“Neural” computation of decisions in optimization problems

Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing.     

DNA计算的发展现状及未来展望

随着高性能计算需求的不断增长,传统计算模式面临着前所未有的巨大挑战.在众多新兴计算技术中,DNA计算系统以其低能耗、并行化等特点而广受关注.DNA电路(DNA circuit)是实现DNA计算的基础,也是该领域重要的分子信息调控和处理技术.文中重点介绍了DNA计算的基本原理,并总结了最新的研究进展,最后讨论了基于DNA...     

Duration channels mediate human time perception

The task of deciding how long sensory events seem to last is one that the human nervous system appears to perform rapidly and, for sub-second intervals, seemingly without conscious effort. That these estimates can be performed within and between multiple sensory and motor domains suggest time perception forms one of the core, fundamental processes of our perception of the world around us. Given this significance, the current paucity in our understanding of how this process operates is surprising. One candidate mechanism for duration perception posits that duration may be mediated via a system of duration-selective 'channels', which are differentially activated depending on the match between afferent duration information and the channels' 'preferred' duration. However, this model awaits experimental validation. In the current study, we use the technique of sensory adaptation, and we present data that are well described by banks of duration channels that are limited in their bandwidth, sensory-specific, and appear to operate at a relatively early stage of visual and auditory sensory processing. Our results suggest that many of the computational principles the nervous system applies to coding visual spatial and auditory spectral information are common to its processing of temporal extent.     

Biological adaptabilities and quantum entropies.

The entropy-based theory of adaptability set forth by Michael Conrad in the early 1970s continued to appear in his work for over two decades, and was the subject of the only book he published in his lifetime. He applied this theory to a host of subjects ranging from enzyme dynamics to sociology. This paper reviews the formalism of adaptability theory, clarifying some of its mathematical and interpretive difficulties. The theory frames the computational tradeoff principle, a thesis that was the most frequently recurring claim in his work. The formulation of adaptability theory presented here allows the introduction of quantum entropy functions into the theory, revealing an interesting relationship between adaptability and another one of Conrad's deep preoccupations, the role of quantum processes in life.     

An extended model for a spiking neuron class

This paper proposes an extension to the model of a spiking neuron for information processing in artificial neural networks, developing a new approach for the dynamic threshold of the integrate-and-fire neuron. This new approach invokes characteristics of biological neurons such as the behavior of chemical synapses and the receptor field. We demonstrate how such a digital model of spiking neurons can solve complex nonlinear classification with a single neuron, performing experiments for the classical XOR problem. Compared with rate-coded networks and the classical integrate-and-fire model, the trained network demonstrated faster information processing, requiring fewer neurons and shorter learning periods. The extended model validates all the logic functions of biological neurons when such functions are necessary for the proper flow of binary codes through a neural network.