@article {2013,
	title = {Stabilization of Stochastic Quantum Dynamics via Open and Closed Loop Control},
	journal = {IEEE Transactions on Automatic Control. Volume 58, Issue 1, 2013, Article number6228517, Pages 74-85},
	year = {2013},
	abstract = {In this paper, we investigate parametrization-free solutions of the problem of quantum pure state preparation and subspace stabilization by means of Hamiltonian control, continuous measurement, and quantum feedback, in the presence of a Markovian environment. In particular, we show that whenever suitable dissipative effects are induced either by the unmonitored environment, or by non-Hermitian measurements, there is no need for feedback, as open-loop time-invariant control is sufficient to achieve stabilization of the target set in probability. Constructive necessary and sufficient conditions on the form of the control Hamiltonian can be provided in this case. When time-invariant control is not sufficient, state stabilization can be attained by the addition of filtering-based feedback control},
	doi = {10.1109/TAC.2012.2206713},
	url = {http://hdl.handle.net/1963/6503},
	author = {Claudio Altafini and Francesco Ticozzi and K. Nishio}
}
@article {2011,
	title = {A system-level approach for deciphering the transcriptional response to prion infection},
	journal = {Bioinformatics (Oxford, England). 2011 Dec; 27(24):3407-14},
	number = {PMID:22016408;},
	year = {2011},
	publisher = {Oxford University Press},
	abstract = {MOTIVATION: Deciphering the response of a complex biological system to an insulting event, at the gene expression level, requires adopting theoretical models that are more sophisticated than a one-to-one comparison (i.e. t-test). Here, we investigate the ability of a novel reverse engineering approach (System Response Inference) to unveil non-obvious transcriptional signatures of the system response induced by prion infection.\\r\\nRESULTS: To this end, we analyze previously published gene expression data, from which we extrapolate a putative full-scale model of transcriptional gene-gene dependencies in the mouse central nervous system. Then, we use this nominal model to interpret the gene expression changes caused by prion replication, aiming at selecting the genes primarily influenced by this perturbation. Our method sheds light on the mode of action of prions by identifying key transcripts that are the most likely to be responsible for the overall transcriptional rearrangement from a nominal regulatory network. As a first result of our inference, we have been able to predict known targets of prions (i.e. PrP(C)) and to unveil the potential role of previously unsuspected genes.\\r\\nCONTACT: altafini@sissa.it\\r\\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	doi = {10.1093/bioinformatics/btr580},
	url = {http://hdl.handle.net/1963/5745},
	author = {Mattia Zampieri and Giuseppe Legname and Daniel Segr{\`e} and Claudio Altafini}
}
