Discrete particle swarm optimization for combinatorial problems with innovative applications.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file

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

Biolaw: Cracking the Code

This is the published version

Quantitative analyses in basic, translational and clinical biomedical research: metabolism, vaccine design and preterm delivery prediction

2 t.There is nothing more important than preserving life, and the thesis here presented is framed in the field of quantitative biomedicine (or systems biomedicine), which has as objective the application of physico-mathematical techniques in biomedical research in order to enhance the understanding of life's basis and its pathologies, and, ultimately, to defend human health. In this thesis, we have applied physico-mathematical methods in the three fundamental levels of Biomedical Research: basic, translational and clinical. At a basic level, since all pathologies have their basis in the cell, we have performed two studies to deepen in the understanding of the cellular metabolic functionality. In the first work, we have quantitatively analyzed for the first time calcium-dependent chloride currents inside the cell, which has revealed the existence of a dynamical structure characterized by highly organized data sequences, non-trivial long-term correlation that last in average 7.66 seconds, and "crossover" effect with transitions between persistent and anti-persistent behaviors. In the second investigation, by the use of delay differential equations, we have modeled the adenylate energy system, which is the principal source of cellular energy. This study has shown that the cellular energy charge is determined by an oscillatory non-stationary invariant function, bounded from 0.7 to 0.95. At a translational level, we have developed a new method for vaccine design that, besides obtaining high coverages, is capable of giving protection against viruses with high mutability rates such as HIV, HCV or Influenza. Finally, at a clinical level, first we have proven that the classic quantitative measure of uterine contractions (Montevideo Units) is incapable of predicting preterm labor immediacy. Then, by applying autoregressive techniques, we have designed a novel tool for premature delivery forecasting, based only in 30 minutes of uterine dynamics. Altogether, these investigations have originated four scientific publications, and as far as we know, our work is the first European thesis which integrates in the same framework the application of mathematical knowledge to biomedical fields in the three main stages of Biomedical Research: basic, translational and clinical

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