Translating Time-course Gene Expression Profiles into Semi-Algebraic Hybrid Automata

Speaker:  Alberto Casagrande - Università di Udine
  Tuesday, June 26, 2007 at 4:15 PM Verde (Ciclo COVAR)

Biotechnological innovations that allow one to sample gene expression
(e.g. gene-expression arrays or real-time Polymerase Chain
Reaction (PCR)), have made it possible to measure the gene expression
levels of a biological system with varying degree of accuracy, cost and
speed. By repeating the measurement steps at different sampling rates
during the system evolution, one can both infer relations among the
genes (e.g., with clustering techniques), as well as define a dynamic
model of the underlying biological system. When a very large number of
genes and measurements are involved, they raise several difficult
algorithmic questions, as accurate model-building, checking and
inference tasks are simply beyond the skills of any human expert for all
but a few trivial examples. Hence, the automation of the required
analysis task is currently viewed as a critical milestone, necessary for
the development of systems biology. Semi-algebraic hybrid automata have
already been proposed as a modeling formalism for biological systems
and have demonstrated their abilities to handle complex biochemical
pathways. Unfortunately, a suitable algorithmic program aimed at the
construction of hybrid automata from biological measurement data still
remains remote. This talk addresses this challenge with an automatic
procedure to build semi-algebraic hybrid automata from gene-expression
profile sequences.

Programme Director
Marco Squassina

External reference
Publication date
June 17, 2007