APCRC-Q Seminar featuring the research of Professor Lars Nielsen of Australian Institute for Bioengineering and Nanotechnology, UQ
| Event Type | Seminar |
|---|---|
| When |
Dec 04, 2014
from 02:00 AM to 03:00 AM |
| Where | TRI Auditorium |
| Contact Name | prostatecentre@qut.edu.au |
| Contact Phone | 07 3176 7957 |
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Prof Lars Nielsen
Cand Polyt Denmark, PhD Qld.
Professor & Chair of Biological Engineering
Australian Institute for Bioengineering and Nanotechnology (AIBN)
The University of Queensland, Brisbane
'Genome scale metabolic and regulatory network modelling'
Professor Nielsen heads the Systems and Synthetic Biology Group at the Australian Institute of Bioengineering and Nanotechnology. Using thermodynamic principles, novel approaches are developed for the rational design of complex pathways as well as handling complex, transient dynamics in developing tissue. A team of 50 people use these novel approaches in the design of bioprocesses as diverse as the production of blood cells for transfusion and the production of industrial biopolymers. Professor Nielsen also heads the Metabolomics Australia Queensland Node, which is focused on assisting Australian scientists developing flux modeling and analysis approaches for their biological systems of interest.
Abstract
Implicit in the collection of high-throughput data is an assumption that computational models ultimately will facilitate biological discovery, reconciliation of heterogeneous data types, identify inconsistencies and enable the systematic generation of hypotheses. Current naïve statistically models falls well short of this ambition, while the use of the conventional dynamic models of physics suffers from our inability to accurately determine in vivo parameters. Genome scale network modelling is rapidly evolving from prediction of gross phenotypic features of natural and engineered microbial systems to dynamic analysis of complex, multicellular systems. In this talk, I will introduce microbial genome scale modelling before discussing our recent advances into modelling metabolism and large scale signal transduction networks in higher organisms.
