"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Simulating the evolution of recruitment behavior in foraging Ants

Spatial heterogeneity in the distribution of food is an important determinant of species\u27 optimal foraging strategies, and of the dynamics of populations and communities. In order to explore the interaction of food heterogeneity and colony size in their effects on the behavior of foraging ant colonies, we built agent-based models of the foraging and recruitment behavior of harvester ants of the genus Pogonomyrmex. We optimized the behavior of these models using genetic algorithms over a variety of food distributions and colony sizes, and validated their behavior by comparison with data collected on harvester ants foraging for seeds in the field. We compared two models: one in which ants lay a pheromone trail each time they return to the nest with food; and another in which ants lay pheromone trails selectively, depending on the density of other food available in the area where food was found. We found that the density-dependent trail-laying model fit the field data better. We found that in this density-dependent recruitment model, colonies of all sizes evolved intense recruitment behavior, even when optimized for environments in which the majority of foods are distributed homogeneously. We discuss the implications of these models to the understanding of optimal foraging strategy and community dynamics among ants, and potential for application to ACO and other distributed problem-solving systems

Applications of Natural Language Processing in Biodiversity Science

Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science

Artificial in its own right

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Chemical Reactions as Petite Rendezvous: The Use of Metaphor in Materials Science Education

Every time we communicate our science, we are involuntarily involved in an educational activity, affecting the listeners’ methodology and motivation. In a beautiful metaphor, late Nobel Laureate, Richard E. Smalley compared interacting atoms and molecules to boys and girls falling in love. Elaborated and exemplified with a couple of entertaining analogies in this discourse is the effectiveness of the use of metaphors in illustrating scientific concepts to both scientific novices and peers. Human brain has been considered to be a complex neural circuitry for the computation of metaphors, which explains the naturalness of their usage, especially when solid arguments could be given in support of the thesis that scientific imagery in general presents a collection of mathematically operable metaphors. On top of this, knowledge could be enriched through logic alone, but new concepts could be learned only through analogies. The greater pervasion of metaphors in scientific presentations could boost their inspirational potential, make the audience more attentive and receptive to their contents, and, finally, expand their educational prospect and enable their outreach to a far broader audience than it has been generally accomplished

Genome-wide transcriptome analysis of the transition from primary to secondary stem development in Populus trichocarpa

<p>Abstract</p> <p>Background</p> <p>With its genome sequence and other experimental attributes, <it>Populus trichocarpa </it>has become the model species for genomic studies of wood development. Wood is derived from secondary growth of tree stems, and begins with the development of a ring of vascular cambium in the young developing stem. The terminal region of the developing shoot provides a steep developmental gradient from primary to secondary growth that facilitates identification of genes that play specialized functions during each of these phases of growth.</p> <p>Results</p> <p>Using a genomic microarray representing the majority of the transcriptome, we profiled gene expression in stem segments that spanned primary to secondary growth. We found 3,016 genes that were differentially expressed during stem development (Q-value ≤ 0.05; >2-fold expression variation), and 15% of these genes encode proteins with no significant identities to known genes. We identified all gene family members putatively involved in secondary growth for carbohydrate active enzymes, tubulins, actins, actin depolymerizing factors, fasciclin-like AGPs, and vascular development-associated transcription factors. Almost 70% of expressed transcription factors were upregulated during the transition to secondary growth. The primary shoot elongation region of the stem contained specific carbohydrate active enzyme and expansin family members that are likely to function in primary cell wall synthesis and modification. Genes involved in plant defense and protective functions were also dominant in the primary growth region.</p> <p>Conclusion</p> <p>Our results describe the global patterns of gene expression that occur during the transition from primary to secondary stem growth. We were able to identify three major patterns of gene expression and over-represented gene ontology categories during stem development. The new regulatory factors and cell wall biogenesis genes that we identified provide candidate genes for further functional characterization, as well as new tools for molecular breeding and biotechnology aimed at improvement of tree growth rate, crown form, and wood quality.</p