Understanding the relationship between gene expression and phenotype is important in many areas of biology and medicine. Current methods for measuring gene expression such as microarrays however are invasive, require biopsy, and expensive. These factors limit experiments to low rate temporal sampling of gene expression and prevent longitudinal studies within a single subject, reducing their statistical power. Thus methods for non-invasive measurements of gene expression are an important and current topic of research. An interesting approach (Segal et al, Nature Biotechnology 25 (6) 2007) to indirect measurements of gene expression has recently been reported that uses existing imaging techniques and machine learning to estimate a function mapping image features to gene expression patterns, providing an image-derived surrogate for gene expression. However, the design of machine learning methods for this purpose is hampered by the cost of training and validation. My thesis shows that populations of artificial organisms simulating genetic variation can be used for designing machine learning approaches to decoding gene expression patterns from images. If analysis of these images proves successful, then this can be applied to real biomedical images reducing the limitations of invasive imaging. The results showed that the box counting dimension was a suitable feature extraction method yielding a classification rate of at least 90% for mutation rates up to 40%. Also, the box-counting dimension was robust in dealing with distorted images. The performance of the classifiers using the fractal dimension as features, actually, seemed more vulnerable to the mutation rate as opposed to the applied distortion level. / Master of Science

Translating the conceptual term of sustainability into materials and exploring bio plastics in order to generate visually and tacitly intriguing objects are the aims for this project. Other designers and previous projects in the field of textile design showed how the material works in a small scale. This project used the material’s design properties to generate groups of object elements. The three main design properties of the material, transparency, biomorph expression and flexibility, and their opposites, opaqueness, geometric expression and stiffness, were combined in different sets which resulted in the used artistic methodology. Therefore, the material properties and earlier established techniques formed the base for finding the overall forms of the installations. The collection and a book about the surface design possibilities for plant based plastic show how the material can be manufactured. The installations focus on the interplay between colour, light and shadow, material texture and pattern in relation to the overall shape of the object. Moulding, laser cutting and the addition of other ingredients like recycled paper, fibres and mica powder influenced the material’s durability, flexibility, transparency and texture. Experiments revealed that the colours change over time and all of them are highly influenced in their intensity and shade by the light source behind the material. This project visualises how all these factors interact and which techniques and tools are required to process the new material. Moreover, it generates new options for a new formal language and terminology for sustainable interior textiles.

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