||Zeti-Azura Mohamed-Hussein (Universiti Kebangsaan Malaysia)
||Reconstruction of biological pathway: computational and knowledge-based approach
||Biological networks are complex and consist of hundreds and thousands of reactions that directly and indirectly affect each other. In order to understand the relationship between these reactions in detail, it is necessary to view the network as a whole. Computational models of metabolic networks have been developed to provide an overview on the biosynthetic processes involved in multiple pathways. These models can be used to observe the changes in the concentration of each metabolite corresponding to those that occur during normal and perturbed conditions. It can also be simulated and used to determine how such changes may contribute to the entire biosynthetic process. The use of computational models of this type gives us an in-depth view of the problems that need to be solved and points to new strategies and alternatives. Various approaches can be used to build, reconstruct and simulate a biological pathway model. However, choosing the right approach to building a particular biological model relies on the type of biological pathway it is to represent. Here I will discuss the approaches that are used in my lab to understand the fundamentals of two biological pathway of interest; i.e. secondary metabolite pathway and pathogenesis of Polycystic Ovarian Syndrome.
||Zeti Hussein received her BSc in Zoology and MRes in Pharmaceutical Science from Universiti Kebangsaan Malaysia and a PhD in Bioinformatics from The University of Edinburgh, Scotland in 2005. She obtained her postdoctoral training at Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, New Zealand. Her research covers various areas in bioinformatics from biological database development and big data analysis. However recently, her research focuses on the reconstruction of biological pathways in plant secondary metabolites and Polycystic Ovarian Syndrome. She is also an active member in Malaysia Cancer Research Network working on analyzing cancer omics data from Malaysian patients.