Description: |
Molecular systems biology is a key area in post-genomics bioinformatics.
This has two main drivers.
First is the need to understand the behaviour
of cells at the systems-level, for example towards the goal of
drug design or industrial biotechnology.
Second is the increasing availability
of high-throughput technology such as microarrays generating
genome-wide data sets.
In this project, we are working according to a methodology adapted
from that of Leroy Hood at the Institute for Systems Biology:
1) assemble an integrated collection of data sets, prepared
in a format ready for computational inference, in collaboration
with biologists
2) apply techniques of computational inference, visualization, etc.
to generate executable ``models'' of biological activity
from the data
3) run the models on the data on a ``what-if'' basis to generate
testable predictions
4) run experiments designed to test these predictions and feed back the
results to stages 1 and 2, adding data sets and revising the
executable models
By computational inference we intend not only statistical algorithms
but logical, i.e., based on deduction and induction (machine learning). |
