Preliminaries for distributed natural computing inspired by the slime mold Physarum Polycephalum

This doctoral thesis aims towards distributed natural computing inspired by the slime mold Physarum polycephalum. The vein networks formed by this organism presumably support efficient transport of protoplasmic fluid. Devising models which capture the natural efficiency of the organism and form a suitable basis for the development of natural computing algorithms is an interesting and challenging goal. We start working towards this goal by designing and executing wet-lab experi- ments geared towards producing a large number of images of the vein networks of P. polycephalum. Next, we turn the depicted vein networks into graphs using our own custom software called Nefi. This enables a detailed numerical study, yielding a catalogue of characterizing observables spanning a wide array of different graph properties. To share our results and data, i.e. raw experimental data, graphs and analysis results, we introduce a dedicated repository revolving around slime mold data, the Smgr. The purpose of this repository is to promote data reuse and to foster a practice of increased data sharing. Finally we present a model based on interacting electronic circuits including current controlled voltage sources, which mimics the emergent flow patterns observed in live P. polycephalum. The model is simple, distributed and robust to changes in the underlying network topology. Thus it constitutes a promising basis for the development of distributed natural computing algorithms.Diese Dissertation dient als Vorarbeit für den Entwurf von verteilten Algorithmen, inspiriert durch den Schleimpilz Physarum polycephalum. Es wird vermutet, dass die Venen-Netze dieses Organismus den effizienten Transport von protoplasmischer Flüssigkeit ermöglichen. Die Herleitung von Modellen, welche sowohl die natürliche Effizienz des Organismus widerspiegeln, als auch eine geeignete Basis für den Entwurf von Algorithmen bieten, gilt weiterhin als schwierig. Wir nähern uns diesem Ziel mittels Laborversuchen zur Produktion von zahlreichen Abbildungen von Venen-Netzwerken. Weiters führen wir die abgebildeten Netze in Graphen über. Hierfür verwenden wir unsere eigene Software, genannt Nefi. Diese ermöglicht eine numerische Studie der Graphen, welche einen Katalog von charakteristischen Grapheigenschaften liefert. Um die gewonnenen Erkenntnisse und Daten zu teilen, führen wir ein spezialisiertes Daten-Repository ein, genannt Smgr. Hiermit begünstigen wir die Wiederverwendung von Daten und fördern das Teilen derselben. Abschließend präsentieren wir ein Modell, basierend auf elektrischen Elementen, insbesondere stromabhängigen Spannungsquellen, welches die Flüsse von P. poly- cephalum nachahmt. Das Modell ist simpel, verteilt und robust gegenüber topolo- gischen änderungen. Aus diesen Gründen stellt es eine vielversprechende Basis für den Entwurf von verteilten Algorithmen dar

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Brave New Worlds: How computer simulation changes model-based science

A large part of science involves building and investigating models. One key feature of model-based science is that one thing is studied as a means of learning about some rather different thing. How scientists make inferences from a model to the world, then, is a topic of great interest to philosophers of science. An increasing number of models are specified with very complex computer programs. In this thesis, I examine the epistemological issues that arise when scientists use these computer simulation models to learn about the world or to think through their ideas. I argue that the explosion of computational power over the last several decades has revolutionised model-based science, but that restraint and caution must be exercised in the face of this power. To make my arguments, I focus on two kinds of computer simulation modelling: climate modelling and, in particular, high-fidelity climate models; and agent-based models, which are used to represent populations of interacting agents often in an ecological or social context. Both kinds involve complex model structures and are representative of the beneficial capacities of computer simulation. However, both face epistemic costs that follow from using highly complex model structures. As models increase in size and complexity, it becomes far harder for modellers to understand their models and why they behave the way they do. The value of models is further obscured by their proliferation, and a proliferation of programming languages in which they can be described. If modellers struggle to grasp their models, they can struggle to make good inferences with them. While the climate modelling community has developed much of the infrastructure required to mitigate these epistemic costs, the less mature field of agent-based modelling is still struggling to implement such community standards and infrastructure. I conclude that modellers cannot take full advantage of the representational capacities of computer simulations unless resources are invested into their study that scale proportionately with the models' complexity

Consistency analysis and improvement of matabolic databases for the integration of metabolic models

Modern systems biology uses analysis and modeling of large metabolic networks. These models can be assembled by integrating data from sources SBML files and online databases. Sometimes this integration can be challenging, as information can be hidden in human-readable texts or annotational layers not directly accessible with the help of common methods. Here, it is shown how algebraic analysis can be used to unravel structural information hidden in the kinetic laws of SBML models. Additionally, this work will demonstrate the Organization Theory approach and its application for inconsistency detection on the Biomodels Database. The usefulness of combining algebraic analysis and OT is shown by comparing the gathered results with data originating from other methods, like FBA. It is shown how scientific methods can be prone to an incorrect interpretation of the data given as well as their representation format. Complementing the analysis of data given in SBML model files, we also present a tool designed to help identifying microbial communities suited to perform biodegradation tasks. The preliminaries needed to perform such a task are discussed together with problems that hinder automatic solution of metabolic research questions. Problems usually occuring in the work with databases are specified and investigated in dato; using the KEGG databse as the main source. Strategies to circumvent the found problems by rule-based network descriptions are sketched out. A detailed description of the idea of rule-based databases for metabolic and biological data will be given. Subsequently, possible applications are listed, giving examples for reasonably simple models. A new formalism will be presented which might suit the task better than more general formalisms like BGNL, which is indeed a very powerful, yet rather tedious methodology. Finally, we will give an account of the advantages and challenges of networks modeled with the rule-based description introduced in this work.Moderne Systembiologie nutzt Analyse und Modellierung von metabolischen Netzwerken im großen Maßstab. Solche Modelle sind durch das Verweben von Daten aus verschiedenen Quellen wie SBML-Dateien oder Internet-Datenbanken erreichbar. Die Zusammenführung dieser Daten stellt uns vor Schwierigkeiten, da oft Informationen in für das menschliche Auge gedachten Texten versteckt sind. Teilweise sind Daten auch in Notations-Ebenen versteckt, die sich herkömmlichen Verfahren nicht direkt erschließen. In dieser Schrift wird unter anderem aufgezeigt, wie algebraische Analysen genutzt werden können um strukturelle Informationen freizulegen, die in den Massenwirkungsgesetzen von SBML-Dateien annotiert sind. Des weiteren wird der Organisationstheorie-Ansatz und dessen Anwendung für die Detektion von Unschlüssigkeiten in der Biomodels-Datenbank demonstriert. Ein Vergleich der Ergebnisse dieser Kombination von algebraischer Analyse und Organisationstheorie mit anderen Methoden wie der Fluss-Balance-Analyse (FBA) soll dann die Nützlichkeit dieses Verfahrens belegen. Es wird gezeigt, wie fehleranfällig wissenschaftliche Methoden sind, wenn die zu Grunde liegenden Daten fehlerbehaftet sind. Die Analyse der Biomodels-Datenbank, wird ergänzt durch ein Programm, das entworfen wurde um bestimmte Bakteriengemeinschaften zu ergründen: Diese Bakteriengemeinschaften sollen genutzt werden um auf biologischem Wege Altlasten zu vermindern. Des weiteren wird auf die Schwierigkeiten eingegangen, die unweigerlich auftreten, wenn versucht wird eine automatisierte Lösung für dieses Problem zu finden. Um tiefer in die Welt dieser Probleme einzutauchen wird in einem weiteren Kapitel die KEGG-Datenbank nach Inkonsistenzen durchleuchtet. In der Diskussion der gefundenen Fehler spielt der Umstieg auf regelbasierte Beschreibungen chemischer Reaktionen eine wesentliche Rolle. Es wird ein Formalismus für regelbasierte Moleküle und Reaktionen vorgestellt und mögliche Anwendungen postuliert

Drawing Futures

Drawing Futures brings together international designers and artists for speculations in contemporary drawing for art and architecture. Despite numerous developments in technological manufacture and computational design that provide new grounds for designers, the act of drawing still plays a central role as a vehicle for speculation. There is a rich and long history of drawing tied to innovations in technology as well as to revolutions in our philosophical understanding of the world. In reflection of a society now underpinned by computational networks and interfaces allowing hitherto unprecedented views of the world, the changing status of the drawing and its representation as a political act demands a platform for reflection and innovation. Drawing Futures will present a compendium of projects, writings and interviews that critically reassess the act of drawing and where its future may lie. Drawing Futures focuses on the discussion of how the field of drawing may expand synchronously alongside technological and computational developments. The book coincides with an international conference of the same name, taking place at The Bartlett School of Architecture, UCL, in November 2016. Bringing together practitioners from many creative fields, the book discusses how drawing is changing in relation to new technologies for the production and dissemination of ideas

Whole-Body Regeneration

This Open Access volume provides a comprehensive overview of the latest tools available to scientists to study the many facets of whole-body regeneration (WBR). The chapters in this book are organized into six parts. Part One provides a historical overview on the study of the WBR phenomena focusing on the primary challenges of this research. Parts Two and Three explore a series of non-vertebrate zoological contexts that provide experimental models for WBR, showing how they can be approached with cellular tools. Parts Four, Five, and Six discuss the future advancements of WBR, reporting about the cutting-edge techniques in genetics and omics used to dissect the underlying mechanisms of WBR, and systems biology approaches to reach a synthetic view of WBR. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and thorough, Whole-Body Regeneration: Methods and Protocols is a valuable resource for scientists and researchers who want to learn more about this important and developing field