R. C. Paton

Is there a biology of quantum information

This paper briefly considers the notion of a biology of quantum information from a number of complementary points of view. We begin with a very brief look at some of the biomolecular systems that are thought to exploit quantum mechanical effects and then turn to the issue of measurement in these systems and the concomitant generation of information. This leads us to look at the internalist stance and the exchange interaction of quantum particles. We suggest that exchange interaction can also be viewed using ecological ideas related to apparatus-object. This can also help develop the important notion of complementarity in biosystems in relation to the nature and generation of information at the microphysical scale.

INTRACELLULAR SIGNALLING PROTEINS AS 'SMART' AGENTS IN PARALLEL DISTRIBUTED PROCESSES

In eucaryotic organisms, responses to external signals are mediated by a repertoire of intracellular signalling pathways that ultimately bring about the activation/inactivation of protein kinases and/or protein phosphatases. Until relatively recently, little thought had been given to the intracellular distribution of the components of these signalling pathways. However, experimental evidence from a diverse range of organisms indicates that rather than being freely distributed, many of the protein components of signalling cascades show a significant degree of spatial organisation. Here, we briefly review the roles of anchor scaffold and adaptor proteins in the organisation and functioning of intracellular signalling pathways. We then consider some of the parallel distributed processing capacities of these adaptive systems. We focus on signalling proteins-both as individual devices (agents) and as networks (ecologies) of parallel processes. Signalling proteins are described as smart thermodynamic machines which satisfy gluing (functorial) roles in the information economy of the cell. This combines two information-processing views of signalling proteins. Individually, they show cognitive capacities and collectively they integrate (cohere) cellular processes. We exploit these views by drawing comparisons between signalling proteins and verbs. This text/dialogical metaphor also helps refine our view of signalling proteins as context-sensitive information processing agents.

Spatio-logical processes in intracellular signalling

Classical models of intracellular signalling describe how small changes in a cells external environment can bring about major changes in cellular activity. Recent findings from experimental biology indicate that many intracellular signalling systems show a high level of spatial organisation. This permits the modification, by protein kinase or protein phosphatase action, of specific subsets of intracellular proteins - an attribute that is not addressed in classical signalling models. Here we use ideas and concepts from computer science to describe the information processing nature of intracellular signalling pathways and the impact of spatial heterogeneity of their components (e.g. protein kinases and protein phosphatases) on signalling activity. We argue that it is useful to view the signalling ecology as a vast parallel distributed processing network of agents operating in heterogeneous microenvironments, and we conclude with an overview of the mathematical and semantic methodologies that might help clarify this analogy between biological and computational systems.

An Examination of Some Metaphorical Contexts for Biologically Motivated Computing

Biologically motivated computing seeks to transfer ideas from the biosciences to computer science. In seeking to make transfers it is helpful to be able to appreciate the metaphors which people use. This is because metaphors provide the context through which analogies and similes are made and by which many scientific models are constructed. As such, it is important for any rapidly evolving domain of knowledge to have developments accounted for in these terms. This paper seeks to provide one overview of the process of modelling and shows how it can be used to account for a variety of biologically motivated computational models. Certain key ideas are identified in the subsequent analysis of biological sources, notably, systemic metaphors. Three important aspects of biological thinking are then considered in the light of computer science applications: biological organization, the cell, and models of evolution. The analysis throughout the paper is descriptive rather than formalized so that a large variety of potential applications may be considered.

Individual-Based Modelling of Bacterial Ecologies and Evolution

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined.

A Model of Bacterial Adaptability Based on Multiple Scales of Interaction : COSMIC

Evolution has frequently been seen as a result of the continuous or dis continxad uous accumulation of small mutations. Over the many years since Darwin, it has been found that simple point mutations are not the only mechanism driving genomic change, for example, plasmids, transposons, bacteriophages, insertion sequences, deletion and duplication, and stress-sensitive mutation all have a part to play in directing the genetic composition and variation of organisms towards meeting the moving target that is the environmental ideal that exists at any one time. These generate the variation necessary to allow rapid evolutionary response to changing environmental conditions. Predictive models of E. coli cellular processes already exist, these tools are excellent models of behaviour. However, they suffer the same drawbacks; all rely on actual experimental data to be input and more importantly, once input that data are static. The aim of this study is to answer some of the questions regarding bacterial evolution and the role played by genetic events using an evolving multicellular and multispecies model that builds up from the scale of the genome to include the proteome and the environment in which these evolving cells compete. All these scales follow an individual based philosophy, where by genes, gene products and cells are all represented as individual entities with individual parameters rather than the more typieal aggregate population levels in a grid. This vast number of parameters and possibilities adds another meaning to the name of the simulation, COSMIC: COmputing Systems of Microbial Interaetions and Communications.

Simulated Bacterially-Inspired Problem Solving --- The Behavioural Domain

Bacterial populations meet the challenges of dynamic spatially heterogeneous environments with fluctuating biotic and abiotic factors in a number of ways. The motivation for the work presented here has been to transfer ideas from bacterial adaptability and evolvability to computational problem solving. Following a brief comment on some examples of the ways bacteria solve problems, a bacterially-inspired computational architecture for simulating aspects of problem solving is described. We then examine three case studies. The first, a study of the mutational impact of a remediation to toxic (fitness-reducing) material, highlights how a sufficiently pre-engineered adaptive system can solve a difficult problem quite easily. The second study looks at why it is difficult to evolve complex problem solving behaviours and how artificial selection mechanisms coupled with pre-engineering the system can help. Specifically, this refers to quorum sensing and tactic behaviours. A further study looked at ways in which a quorum sensing analogue could help computational agents find multiple peaks in a landscape. The paper concludes with a discussion of an investigation of bacteria that had both quorum sensing and tactic capabilities.

Diagrammatic representations for modelling biological knowledge

The contemporary research and development context in multidisciplinary biology has a serious requirement for integrating knowledge from disparate sources, and facilitating much-needed inter- and intra-disciplinary dialogue. A multiplicity of models arises when pluralistic approaches to modelling are followed, and also when there is not only a requirement to model systems and data, but also knowledge of systems and data. The challenges of addressing this multiplicity do not only include articulating the structure of complex systems, but also placing modelling within the framework of a process as well as a product. The graph representations presented here facilitate dialogue, modelling, clarification and specification of concepts, and the sharing of terms. This paper explores relationships between collections of graph representations. It is hoped that in future, when readers look at a node or a process in a graph, they will have a much deeper appreciation of relationships and context.

Systemic Metaphor Analysis for Knowledge Based System Development

One of the key phases in the development of a Knowledge Based System (KBS) is that of knowledge acquisition. All KBSs must go through this stage and it presents a major hurdle to be overcome when building such systems. The focus of attention at this stage of a KBS development is the domain which, in simple terms, is that knowledge needed to solve a set of real world problems. Research on the MEKAS project has indicated that knowledge about a domain not only includes the tasks and objects which it addresses, but also the wider cognitive and real world environment in which it is set (Paton & Nwana, 1990). As such, human knowledge about domains is so complex that without an analysis stage that probes the underlying nature of a real world domain and how experts may conceptualise it, the knowledge that is eventually incorporated into a KBS remains shallow and incomplete. Just as conventional systems development requires a systems analysis phase to analyse the problem situation, so KBS development requires a knowledge analysis phase which characterises the domain by organising the information acquired from a variety of knowledge sources into a coherent and unambiguous whole.

Developing Algebraic Models of Protein Signalling Agents

This chapter considers a number of ways in which individual molecules in protein signalling systems can be thought of as computational agents. We begin with a general discussion of some of the ways proteins can be viewed from an information processing point of view. The degree of computational prowess shown by many proteins, such as enzymes and transcription factors is discussed; specifically in terms of a number of ‘cognitive’ capacities. We review some of the proteins involved in signalling that make use of transfer of phosphate groups (kinases and phosphatases) and focus attention on the Yeast MAP Kinase cascades. An algebraic approach to modelling certain aspects of protein interactions is introduced. We begin with a simple algebraic model which we describe in some depth, using yeast signalling pathways as an example; we then describe techniques and tools which promise more sophisticated models.