Syntrophic relationships between algae and bacteria in a fresh-water stream and in culture /

Interactions between freshwater algae and bacteria were examined in a natural stream habitat and a laboratory model. Field observations provided circumstantial evidence, in statistical correlation for syntrophy between the microbial populations. This relation is probably subject to control by the temperature and pH of the aquatic environment. Several species of a pond community were isolated in axenic culture and tests were performed to determine the nature of mixed species interactions. Isolation procedures and field studies indicated that selected strains of Chlorella and Azotobacter were closely associated in their natural habitat. With the suspected controlling parameters, pH and temperature, held constant, mixed cultures of algae and bacteria were compared to axenic cultures of the same organisms, and a mutual stimulation of growth was observed. A mixed pure culture apparatus was designed in this laboratory to study the algal-bacterial interaction and to test the hypothesis that such an interaction may take place through a diffusable substance or through certain medium-borne conditions, Azotobacter was found to take up a Chlorella-produced exudate, to stimulate protein synthesis, to enhance chlorophyll production and to cause a numerical increase in the interacting Chlorella population. It is not clear whether control is at the environmental, cellular or genetic level in these mixed population interactions. Experimental observations in the model system, taken with field correlations allow one to state that there may be a direct relationship governing the population fluctuations of these two organisms in their natural stream surroundings.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.