technical report Elucidation of Functional Consequences of Interaction Networks


Abstract

To further understand the complexity of biological processes and decipher the mechanisms that lead to healthy or diseased organisms, it is important to consider protein functions in the context of complex molecular networks. This is because most diseases cannot be explained by one genetic mutation or the action of a single gene product or pathway. The vast amount of data on molecular structure, interaction and activity information being acquired provides a picture of intricate molecular networks that underlie biological function. This ever growing amount of data demands we adopt powerful computational techniques for analysing and interpreting content that is not immediately intelligible to the scientist. Whereas traditional statistics-based methods test a priori hypothesis against data, data mining strives to discover new, previously unknown and hidden patterns in large data sets; and then tries to represent and interpret these patterns in an intelligible way. Ultimately, effective data-driven dynamic modelling of molecular networks will play a pivotal role in the conversion of data to knowledge and permit detailed understanding into how protein interacts to form healthy or diseased pathways. Here we describe a methodology for guiding computational modelling of molecular networks - in particular, the p53 and NF-kappaB systems - useful for capturing different aspects of network dynamics by uncovering the behaviour of molecular species and their combinatorial interactions.



Paper Details

Authors

A. Ihekwaba,  C. Priami,  P. Nguyen

Download

/var/papers/TR/TR-07-2009.pdf

Language

English
.