TY - GEN

T1 - Robust intervention in probabilistic boolean networks

AU - Pal, Ranadip

AU - Datta, Aniruddha

AU - Dougherty, Edward R.

PY - 2007

Y1 - 2007

N2 - Probabilistic Boolean Networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. To date, a number of intervention strategies have been proposed in the context of Probabilistic Boolean networks. However, all these techniques assume perfect knowledge of the transition probability matrix of the PBN. Such an assumption cannot be satisfied in practice since the presence of noise and the availability of limited number of samples will prevent the transition probabilities from being accurately determined. Moreover, even if the exact transition probabilities could be estimated from the data, mismatch between the PBN model and the actual genetic regulatory network will invariably be present. In this paper, we develop a robust intervention strategy that is obtained by minimizing the worst-case cost over the uncertainties in the entries of the transition probability matrix.

AB - Probabilistic Boolean Networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the network for the design and analysis of intervention strategies aimed at moving the network out of undesirable states, such as those associated with disease, and into desirable ones. To date, a number of intervention strategies have been proposed in the context of Probabilistic Boolean networks. However, all these techniques assume perfect knowledge of the transition probability matrix of the PBN. Such an assumption cannot be satisfied in practice since the presence of noise and the availability of limited number of samples will prevent the transition probabilities from being accurately determined. Moreover, even if the exact transition probabilities could be estimated from the data, mismatch between the PBN model and the actual genetic regulatory network will invariably be present. In this paper, we develop a robust intervention strategy that is obtained by minimizing the worst-case cost over the uncertainties in the entries of the transition probability matrix.

UR - http://www.scopus.com/inward/record.url?scp=46449129976&partnerID=8YFLogxK

U2 - 10.1109/ACC.2007.4282544

DO - 10.1109/ACC.2007.4282544

M3 - Conference contribution

AN - SCOPUS:46449129976

SN - 1424409888

SN - 9781424409884

T3 - Proceedings of the American Control Conference

SP - 2405

EP - 2410

BT - Proceedings of the 2007 American Control Conference, ACC

Y2 - 9 July 2007 through 13 July 2007

ER -