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2012, with author Germani A., in the category IASI Research Reports
(or show them all):
IASI Research Report n. 12-13 (Previous )
Cacace F.,
Alfredo Germani,
Pasquale PalumboA study on observer-based algorithms to infer information from gene expression dataABSTRACT In this paper a mathematical tool is presented, to estimate unknown variables of
transcription networks, according to a set of measurements of the transcriptional activity of promot-
ers. The approach is based on the use of the mathematical model of the network under investigation
and on the state-reconstruction technique known as `state-observer', borrowed from the control the-
ory. To this aim, besides the general case, the network motif of the Multi-Output Feed-Forward
Loop (MO-FFL) will be investigated in details. Simulations show the eectiveness of the proposed
approach in a wide range of possible critical frameworks, such as only one target gene measurements,
non-smooth input perturbations, noisy measurements and model parameter uncertainties.