Systems biology is an emerging multi-disciplinary field in which the behaviour of
complex biological systems is studied by considering the interaction of many cellular
and molecular constituents rather than using a “traditional” reductionist approach
where constituents are studied individually. Systems are often studied over time
with the ultimate goal of developing models which can be used to understand and
predict complex biological processes, such as human diseases. To support systems
biology, a large number of biological pathways are being derived for many different
organisms, and these are stored in various databases. This pathway collection presents
an opportunity to compare and contrast pathways, and to utilise the knowledge they
represent. This thesis presents some of the first algorithms that are designed to
explore this opportunity. It is argued that the methods will be useful to biologists
in order to assess the biological plausibility of derived pathways, compare different
biological pathways for semantic similarities, and to derive putative pathways that are
semantically similar to documented biological pathways. The methods will therefore
extend the systems biology toolbox that biologists can use to make new biological
discoveries.Knowledge Foundation. Grant No. 2003/0215Information Fusion Research Program (University of Skovde, Sweden) Grant No 2003/010
