Fuzzy Relational Biology

Preface What is referred to as a genomic revolution can be summarised by the following tasks: Analysis of the DNA sequence of a genome, identification of genes, study of gene products (usually proteins). While the identification of components, cataloguing of products and their classification is very exciting, the interrelationships and interactions of these components is what makes organisms like ourselves 'tick'. Once all parts of a clockwork are known, the challenge is to find out how the clock 'works'. New technology makes it possible to study gene activity and allows us to investigate the organisation and control of genetic regulatory pathways. These pathways describe dynamic processes. Their complexity as well as the difficulty in observing them will challenge all analytical tools developed to date. This shift of focus from molecular characterisation to an understanding of functional activity, implies a change from generating hypotheses to testing hypotheses. Problems in genomics will become conceptual as well as empirical. I will argue that the biggest challenge of bioinformatics is not the volume of data, as commonly stated, but the formal representation of knowledge. The area of bioinformatics has provided an important service to biologists; helping them to visualize molecular structures, analyze sequences, store and manipulate data and information. These activities will continue to be an essential part of bioinformatics, developing working methodologies and tools for biologists. However in order to directly contribute towards a deeper understanding of the biology, bioinformatics has to establish a conceptual framework for the v vi PREFACE formal representation of interrelationships and interactions between genes or proteins. To this date biological knowledge is encoded in scientific texts and diagrams with a noticeable lack of formal mathematical models. There are obvious reasons for this as most physical or engineering systems, for which mathematical modelling has been successful, appear trivial in comparison to molecular or genetic systems and it is not altogether clear whether a mathematical analysis of genomics systems is the long awaited solution. We may however be able to learn in some respect from the engineering sciences. Like biologists, engineers are not born with a love for mathematics and yet they have learned to embrace it as a problem solving strategy or way of thinking. Engineers have been in very much the same situation in which biologists find themselves now: The systems or processes they study, the data they generate are too complex to be dealt with using common …